Edge IoT Industrial Immersive and Spatial Computing Applications

Alliance o IoT, AI and Edge
Con inuum Inno a ion 2025
AIOTI WG Resea ch
Edge IoT Indus ial Imme si e
and Spa ial Compu ing
Applica ions
AIOTI. All igh s ese ed.
Edge IoT Indus ial Imme si e and
Spa ial Compu ing Applica ions
Release 1
AIOTI WG Resea ch
18 Sep embe 2025
© AIOTI. All igh s ese ed.
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1 Execu i e Summa y
Imme si e echnologies, including i ual eali y (VR), augmen ed eali y (AR), mixed eali y (MR),
and ex ended eali y (XR), alongside ad anced concep s such as digi al wins (DT), imme si e
iple s (IMT), he me a e se, omni e se, and spa ial compu ing, ep esen a signi ican shi in
indus ial applica ions ac oss sec o s like cul u e and he i age, manu ac u ing, au omo i e,
ene gy, buildings/cons uc ion, mobili y. anspo a ion, logis ics, heal hca e,
ag icul u e/ a ming, ou ism, educa ion and aining.
The con e gence o hese imme si e echnologies wi h edge IoT, a i icial in elligence (AI), and
ad anced in elligen connec i i y in as uc u e is shaping an indus ial eal-digi al- i ual
con inuum, e med he "Phygi al" wo ld. By combining eal-wo ld in e ac ions and i ual
simula ions, indus ies achie e imp o ed ope a ional e iciency, educed down ime, enhanced
sa e y p o ocols, and supe io decision-making capabili ies [24].
Edge IoT indus ial imme si e echnologies equi e ex ensi e in e disciplina y collabo a ion and
obus in as uc u e, om ad anced compu ing pla o ms o ad anced senso s and hap ic
de ices.
Real- ime, high-pe o mance p ocessing capabili ies, alongside eliable and secu e
connec i i y wi h e y low la ency, a e undamen al. As hese echnologies ma u e,
s anda disa ion, in e ope abili y, us wo hiness, e hics, and sus ainabili y become c i ical
conside a ions, shaping global egula o y amewo ks and indus y s anda ds.
De eloping imme si e applica ions equi es managing isks associa ed wi h da a p o ec ion,
p i acy, AI e hics, and echnological con e gence while os e ing inno a ion and g ow h.
This posi ion pape on “Edge IoT Indus ial Imme si e and Spa ial Compu ing Applica ions” aims
o p o ide a comp ehensi e o e iew o he con e gence be ween IoT, AI, edge, and spa ial
compu ing, and how hey a e applied o a ious indus ial imme si e applica ions ac oss
di e en indus ial sec o s. I de ails he ans o ma i e impac o hese applica ions ac oss a
wide ange o indus ial sec o s, including cul u e and he i age, manu ac u ing, au omo i e,
ene gy, cons uc ion, mobili y, logis ics, heal hca e, ag icul u e, ou ism, educa ion and aining.
Fo each sec o , he pape p esen s a ho ough analysis o speci ic applica ion scena ios. I
iden i ies he key use s and s akeholde s in ol ed, desc ibes how hese applica ions a e
implemen ed wi hin a i ual wo ld, and ou lines he signi ican added alue hey b ing o he
indus y. Fu he mo e, i speci ies he equi ed imme si e echnology unc ionali ies, de ails he
unde lying echnology laye equi emen s, and examines how c oss-cu ing ho izon al issues
mani es in each speci ic con ex .
Beyond he sec o -speci ic applica ions, he pape dedica es chap e s o c i ical ho izon al
opics such as us wo hiness, e hics, sus ainabili y, s anda disa ion, and in e ope abili y. Fo
each o hese a eas, i iden i ies he p ima y challenges o success ul implemen a ion and
discusses u u e esea ch ends and di ec ions, o e ing a o wa d-looking pe spec i e on he
e olu ion o hese echnologies.
The goal o his app oach is o c ea e a holis ic documen ha showcases he cu en s a e and
po en ial o indus ial imme si e applica ions and p o ides a s a egic oadmap. I highligh s he
echnological equi emen s and add esses he c ucial non- echnical challenges, se ing as a
ounda ional esou ce o esea che s, inno a o s, and indus y leade s na iga ing his apidly
ad ancing ield.
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Table o Con en
4.1 Imme si e Cul u al E en s Pla o m wi h Holog aphic P esence ..................................... 25
4.1.1 Scena io .................................................................................................................................. 25
4.1.2 Use s and s akeholde s ......................................................................................................... 26
4.1.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 26
4.1.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 26
4.1.5 Technology laye s equi emen s .......................................................................................... 27
4.1.6 Ho izon al issues and cha ac e is ics ................................................................................... 28
4.2 IoT-based Managemen o Tangible Cul u al He i age Asse s ....................................... 28
4.2.1 Scena io .................................................................................................................................. 28
4.2.2 Use and s akeholde s ........................................................................................................... 30
4.2.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 30
4.2.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 30
4.2.5 Technology laye s equi emen s .......................................................................................... 30
4.2.6 Ho izon al issues and cha ac e is ics ................................................................................... 30
5.1 Indus ial Me a e se ........................................................................................................... 31
5.1.1 Scena io .................................................................................................................................. 31
5.1.2 Use s and s akeholde s ......................................................................................................... 32
5.1.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 32
5.1.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 33
5.1.5 Technology laye s equi emen s .......................................................................................... 34
5.1.6 Ho izon al issues and cha ac e is ics ................................................................................... 34
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5.2 Robo ic Welding ................................................................................................................. 35
5.2.1 Scena io .................................................................................................................................. 35
5.2.2 Use s and s akeholde s ......................................................................................................... 35
5.2.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 36
5.2.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 36
5.2.5 Technology laye s equi emen s .......................................................................................... 37
5.2.6 Ho izon al issues and cha ac e is ics ................................................................................... 37
5.3 Assis ance o Equipmen Se icing and Main enance ................................................... 39
5.3.1 Scena io .................................................................................................................................. 39
5.3.2 Use s and s akeholde s ......................................................................................................... 40
5.3.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 40
5.3.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 40
5.3.5 Ho izon al issues and cha ac e is ics ................................................................................... 41
5.4 Tele- epai and Remo e Main enance ............................................................................. 41
5.4.1 Scena io .................................................................................................................................. 41
5.4.2 Use s and s akeholde s ......................................................................................................... 42
5.4.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 42
5.4.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 43
5.4.5 Technology laye s equi emen s .......................................................................................... 43
5.4.6 Ho izon al issues and cha ac e is ics ................................................................................... 44
5.5 Ci cula i y in Manu ac u ing wi h XR and AI ..................................................................... 44
5.5.1 Scena io .................................................................................................................................. 44
5.5.2 Use s and s akeholde s ......................................................................................................... 45
5.5.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 45
5.5.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 46
5.5.5 Technology laye s equi emen s .......................................................................................... 46
5.5.6 Ho izon al issues and cha ac e is ics ................................................................................... 46
5.6 XR-Based Human-Robo Collabo a ion Along a Con eyo Picking Line: The case o
Cons uc ion and Demoli ion Was e So ing ................................................................................ 47
5.6.1 Scena io .................................................................................................................................. 47

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5.6.2 Use and s akeholde s ........................................................................................................... 47
5.6.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 47
5.6.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 48
5.6.4.1 Use Feedback Mechanisms ............................................................................................ 48
5.6.5 Technology laye s equi emen s .......................................................................................... 48
5.6.6 Ho izon al issues and cha ac e is ics ................................................................................... 49
6.1 Me a-Fac o y Model o Elec i ied B aking Sys em ......................................................... 50
6.1.1 Scena io .................................................................................................................................. 50
6.1.2 Use s and s akeholde s ......................................................................................................... 51
6.1.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 51
6.1.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 51
6.1.5 Technology laye s equi emen s .......................................................................................... 52
6.1.6 Ho izon al issues and cha ac e is ics ................................................................................... 52
7.1 Imme si e VR o Ope a ion o Wind Tu bines .................................................................. 53
7.1.1 Scena io .................................................................................................................................. 53
7.1.2 Use and s akeholde s ........................................................................................................... 54
7.1.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 54
7.1.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 54
7.1.5 Technology laye s equi emen s .......................................................................................... 54
7.1.6 Ho izon al issues and cha ac e is ics ................................................................................... 54
8.1 Vi ual A chi ec u al Design ............................................................................................... 55
8.1.1 Scena io .................................................................................................................................. 55
8.1.2 Use and s akeholde s ........................................................................................................... 55
8.1.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 56
8.1.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 56
8.1.5 Technology laye s equi emen s .......................................................................................... 56
8.1.6 Ho izon al issues and cha ac e is ics ................................................................................... 56
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9.1 Collabo a i e Ai c a Cockpi Design .............................................................................. 57
9.1.1 Scena io .................................................................................................................................. 57
9.1.2 Use s and s akeholde s ......................................................................................................... 58
9.1.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 58
9.1.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 59
9.1.5 Technology laye s equi emen s .......................................................................................... 59
9.1.6 Ho izon al issues and cha ac e is ics ................................................................................... 60
10.1 XR-Based Heal hca e Cybe secu i y Digi al Twin ............................................................ 61
10.1.1 Scena io .................................................................................................................................. 61
10.1.2 Use and s akeholde s ........................................................................................................... 62
10.1.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 62
10.1.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 62
10.1.5 Technology laye s equi emen s .......................................................................................... 62
10.1.6 Ho izon al issues and cha ac e is ics ................................................................................... 63
11.1 XR-based Ag icul u al Digi al Twin .................................................................................... 64
11.1.1 Scena io .................................................................................................................................. 64
11.1.2 Use s and s akeholde s ......................................................................................................... 65
11.1.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 65
11.1.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 66
11.1.5 Technology laye s equi emen s .......................................................................................... 67
11.1.6 Ho izon al issues and cha ac e is ics ................................................................................... 67
11.2 Imme si e Ag icul u al Digi al Twin ................................................................................... 68
11.2.1 Scena io .................................................................................................................................. 68
11.2.2 Use and s akeholde s ........................................................................................................... 69
11.2.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 69
11.2.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 69
11.2.5 Technology laye s equi emen s .......................................................................................... 69
11.2.6 Ho izon al issues and cha ac e is ics ................................................................................... 69
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11.3 Sma Ag icul u e: P ecision Fa ming ................................................................................ 70
11.3.1 Scena io .................................................................................................................................. 70
11.3.2 Use and s akeholde s ........................................................................................................... 70
11.3.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 71
11.3.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 71
11.3.5 Technology laye s equi emen s .......................................................................................... 71
11.3.6 Ho izon al issues and cha ac e is ics ................................................................................... 71
12.1 In eg a ion o Digi al Twins in he Na u al Rese e Lauk ikøyene .................................. 72
12.1.1 Scena io .................................................................................................................................. 72
12.1.2 Use and s akeholde s ........................................................................................................... 73
12.1.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 73
12.1.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 74
12.1.5 Technology laye s equi emen s .......................................................................................... 74
12.1.6 Ho izon al issues and cha ac e is ics ................................................................................... 74
12.1.7 Requi ed imme si e echnologies unc ionali ies .............................................................. 74
12.1.8 Ho izon al issues and cha ac e is ics ................................................................................... 74
13.1 T aining o Comp esso Assembly .................................................................................... 75
13.1.1 Scena io .................................................................................................................................. 75
13.1.2 Use s and s akeholde s ......................................................................................................... 75
13.1.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 75
13.1.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 76
13.1.5 Technology laye s equi emen s .......................................................................................... 76
13.1.6 Ho izon al issues and cha ac e is ics ................................................................................... 77
13.2 G und os Machine Ope a ion and Sa e y T aining .......................................................... 77
13.2.1 Scena io .................................................................................................................................. 77
13.2.2 Use s and s akeholde s ......................................................................................................... 78
13.2.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 78
13.2.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 78
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13.2.5 Technology laye s equi emen s .......................................................................................... 79
13.2.6 Ho izon al issues and cha ac e is ics ................................................................................... 79
13.3 DSB T ain Ope a o T aining ............................................................................................... 80
13.3.1 Scena io .................................................................................................................................. 80
13.3.2 Use s and s akeholde s ......................................................................................................... 80
13.3.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 80
13.3.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 81
13.3.5 Technology laye s equi emen s .......................................................................................... 81
13.3.6 Ho izon al Issues and Cha ac e is ics .................................................................................. 81
13.4 MR Inciden Simula o o imme si e Command and Con ol Room T aining ................ 82
13.4.1 Scena io .................................................................................................................................. 82
13.4.2 Use and s akeholde s ........................................................................................................... 82
13.4.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 82
13.4.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 82
13.4.5 Technology laye s equi emen s .......................................................................................... 83
13.4.6 Ho izon al issues and cha ac e is ics ................................................................................... 83
13.5 XR-based Remo e Collabo a ion wi h IoT Con ex ual In eg a ion .................................. 83
13.5.1 Scena io .................................................................................................................................. 83
13.5.2 Use and s akeholde s ........................................................................................................... 84
13.5.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 84
13.5.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 84
13.5.5 Technology laye s equi emen s .......................................................................................... 84
13.5.6 Ho izon al issues and cha ac e is ics ................................................................................... 84
13.6 Ad anced VR simula o o T aining Law En o cemen O ice s ...................................... 84
13.6.1 Scena io .................................................................................................................................. 84
13.6.2 Use and s akeholde s ........................................................................................................... 84
13.6.3 Implemen a ion in a i ual wo ld and added alues ...................................................... 85
13.6.4 Requi ed imme si e echnologies unc ionali ies .............................................................. 85
13.6.5 Technology laye s equi emen s .......................................................................................... 85
13.6.6 Ho izon al issues and cha ac e is ics ................................................................................... 86
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Lis o Keywo ds
3D
5G
6G
Ae onau ics
Ag icul u e
AI assis an
AI objec de ec ion
Ai c a design
A i icial in elligence
Asse loca ion
Augmen ed eali y
Au omo i e
B aking sys em
Ci cula economy
Cockpi design
Collabo a i e design
Collabo a i e inno a ion
Collabo a i e aining
Compliance
Comp esso assembly
Compu e ision
Cons uc ion demoli ion was e
Con eyo sys ems
Cul u al e en s
Compu e ision
Cybe secu i y
Da a isualisa ion
Digi al asse s
Digi al win
Doo ope a ion
Edge compu ing
Employee aining
Equipmen main enance
E gonomics
E hical
Explainable AI
Ex ended eali y
Fa m managemen
Fa ming
Hap ic eedback
Head acking
Heal hca e
Hololens
Holog aphic p esence
HTC Vi e
Human-machine in e ac ion
Human-machine in e ace
Human- obo collabo a ion
Human- obo in e ac ion
Imme si e i ual eali y
Indoo posi ioning
Indus ial me a e se
Indus ial aining
Indus y 4.0
Indus y 5.0
In ellec ual p ope y
In e ne o hings
In e ope abili y
La ge language model
Li e pe o mance
Low la ency
Machine ope a ion
Manu ac u ing
Me a ques
Me a- ac o y
Me a e se
Mixed eali y
Ne wo k slicing
Oculus ques
Pose acking
P edic i e main enance
P ocedu al lea ning
P oduc ion down ime
Railway simula ion
Remo e collabo a ion
Remo e main enance
Remo e aining
Robo ics
Robo ic welding
Robo Ope a ing Sys em
Sa e pa h inding
Sa e y p ocedu es
Senso ne wo k
Sma ac o y
Sma a ming
S anda disa ion
Sus ainabili y
Sus ainabili y in manu ac u ing
Tele- epai
Teleme y
Tele obo ics
Tou ism
T ain ope a o aining
Ul a-wideband
Uni y 3D engine
Vi ual commissioning
Vi ual p o o yping
Vi ual eali y
Vi ual eali y aining
Was e so ing
WEAVR pla o m
Wo ke assis ance

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2 Goal
Fusion and con e gence o echnologies spa ks inno a ion, allowing o c oss-pollina ion o
ideas, he c ea ion o no el app oaches, and ans o ms indus ial landscape by enabling new
edge IoT de ices, sys ems, se ices, and business models.
De eloping cu ing-edge IoT indus ial imme si e echnologies and spa ial compu ing equi es
a holis ic, in e disciplina y app oach as a d i e o knowledge c ea ion, esea ch, and
inno a ion. Collabo a ion be ween he disciplines is hus a i al complemen o he g ow o he
disciplines hemsel es [24].
IoT and edge compu ing esea ch and inno a ion add ess IoT/edge con inuum dis ibu ed
a chi ec u es, in elligen connec i i y. End- o-end (E2E) secu i y, he e ogenous IoT edge mesh,
IoT DTs, AI, IoT swa m sys ems, In e ne o Things Senses (IoTS), us wo hiness, e i ica ion,
alida ion, and es ing (VV&T), s anda disa ion, and he con e gence o all he abo e in o he
In e ne o In elligen Things.
Imme si e echnology e e s o any echnology ha blu s he line be ween he physical and
digi al wo lds, c ea ing a sense o p esence and engagemen o he use .
Imme si e echnologies aim o anspo use s o i ual en i onmen s o enhance hei eal-wo ld
expe iences by o e laying digi al in o ma ion on o hei physical su oundings h ough eal- ime
in e ac ions in physical, digi al, i ual, cybe , and spa ial en i onmen s. Imme si e echnologies
ha e eme ged as a e olu iona y app oach o c ea ing digi al expe iences ha eel eal o
use s. By inco po a ing a ious ools and sys ems, imme si e echnology encompasses eal- ime
in e ac ions in physical, digi al, i ual, cybe , and spa ial en i onmen s using a b oad spec um
o expe iences ha blu he bounda ies be ween he physical and digi al wo lds, p o iding
inno a i e ways o in e ac , explo e, and lea n.
IoT and edge compu ing enable inno a ion and b oad adop ion in imme si e echnologies and
applica ions by b inging he no el elemen s o con e ging echnologies o he edge and eal-
ime in e ac ion be ween he physical and i ual wo lds. Web 4.0 embodies a new e a o he
In e ne , concei ed as a decen alised online ecosys em ounded on blockchain echnology.
Unlike he p esen In e ne e sion Web 2.0, domina ed by cen alised pla o ms and se ices
owned by a hand ul o la ge co po a ions, Web 4.0 aims o e u n con ol and owne ship o he
use s.
The echnological ad ances i b ings, ha e he po en ial o p o oundly change he way one
in e ac s wi h he digi al ealm, c ea ing a mo e open, anspa en , and use -empowe ed
in e ne .
The u u e o echnology, pa icula ly wi h he con e gence o IoT, edge compu ing, AI, and
indus ial imme si e echnologies, is poised o g oundb eaking de elopmen s, especially when
in eg a ed wi h eme ging concep s like he me a e se, he omni e se, he mul i e se, and
In e ne Web 4.0.
The u u e e sion o he In e ne is expec ed o be mo e au onomous, in elligen , and seamlessly
in eg a ed in o e e yday objec s. I could le e age blockchain o secu i y and decen alisa ion,
acili a e mic o ansac ions wi hin he a ious e ses, and suppo sophis ica ed AI-d i en
in e ac ions.
The con e gence o hese echnologies signi ies a echnological shi , as well as a cul u al and
economic one, po en ially al e ing how we pe cei e and in e ac wi h he digi al and physical
wo lds. This con e gence p omises a mo e in eg a ed, imme si e, and in e ac i e u u e.
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The deploymen o imme si e echnologies in eal-wo ld applica ions necessi a es add essing
he challenge o ensu ing end- o-end secu i y ac oss di e se and in e connec ed sys ems,
conside ing he he e ogeneous na u e o he IoT edge sys ems, and he goal o in e ope able
in eg a ion. Ensu ing he us wo hiness and eliabili y o indus ial imme si e applica ions
h ough adi ional e i ica ion, alida ion, and es ing poses a signi ican challenge.
F om an economic s andpoin , he ini ial in es men equi ed o deploy imme si e and edge IoT
in as uc u e can be a signi ican ba ie o many o ganisa ions. The cos s associa ed wi h
scaling, main aining, and upg ading he indus ial imme si e sys ems also p esen ongoing
economic challenges. This leads o he need o ca e ully planning and choosing he IoT, AI,
communica ion and pla o m echnologies used by a ious indus ial applica ions. In addi ion,
indus ial imme si e applica ions equi e p o essionals wi h in e disciplina y expe ise o de elop
and manage hese con e ged echnologies.
Today's indus ial imme si e applica ions ma ke is agmen ed, wi h compe i ion be ween
a ious p op ie a y sys ems, which can p e en widesp ead, s anda dised adop ion and c ea e
esis ance om indus ies hesi an o dis up hei exis ing wo k lows.
The challenges p esen ed equi e a clea ocus on s anda disa ion, in e ope abili y, and he
design, de elopmen , implemen a ion and deploymen o a ious indus ial imme si e
applica ions ac oss di e en indus ial sec o s in ol ing in e disciplina y ecosys ems o acili a e
collabo a ion and coope a ion.
Technically, he con e gence o AI, IoT and edge compu ing allows o dis ibu ed a chi ec u es
ha suppo in elligen connec i i y and eal- ime in e ac ions ha can be applied o indus ial
imme si e applica ions.
5G and u u e 6G p i a e ne wo ks (NPNs) a e pi o al o ad ancing imme si e indus ial
applica ions by p o iding la ge indus ies he au onomy o deploy and manage hei own
dedica ed ne wo k in as uc u e. This localised con ol ensu es eliable, low-la ency, and high-
bandwid h connec i i y, which is essen ial o da a-in ensi e applica ions like AR, VR, and he
eal- ime con ol o au onomous s eams. By ope a ing a p i a e ne wo k, indus ies can
gua an ee secu i y, cus omise ne wo k pe o mance o speci ic ope a ional needs, and ensu e
ha mission-c i ical imme si e se ices a e no comp omised by he conges ion o public
ne wo ks, he eby accele a ing he adop ion o inno a i e solu ions ac oss indus ial domains.
The de elopmen o IoT digi al wins, imme si e iple s and swa m sys ems o e s e ec i e new
ways o simula e, moni o , and op imise indus ial ope a ions and in eg a e imme si e
echnologies in eal- ime p ocesses, while AI-d i en analy ics enhance decision-making and
c ea e mo e esponsi e use expe iences.
Economically, he ise o Web 4.0 and blockchain echnology opens he doo o decen alised
business models and secu e mic o ansac ions wi hin imme si e indus ial en i onmen s like he
me a e se. Signi ican cos sa ings can be ealised h ough p edic i e main enance, emo e
collabo a ion, and he au oma ion o complex ope a ions, while new e enue s eams can be
c ea ed om inno a i e, alue-added imme si e se ices.
Ac oss a ious ma ke s, he indus ial imme si e applica ions unlock ans o ma i e app oaches.
In manu ac u ing, imme si e aining and emo e assis ance can imp o e sa e y and e iciency.
Heal hca e can le e age imme si e ech o ad anced diagnos ics, he apeu ic in e en ions,
and su gical aining.
The eme gence o he me a e se, omni e se and mul i e se is c ea ing new ecosys ems o
comme ce, social in e ac ion, and en e ainmen , undamen ally al e ing how people and
machines engage wi h he physical, digi al and cybe wo lds.
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Indus ial imme si e applica ions, powe ed by he con e gence o AI, IoT, edge, and spa ial
compu ing, a e undamen al o he digi isa ion and au oma ion o indus y. The adop ion o
hese in eg a ed indus ial imme si e applica ions is a di ec d i e o inc eased compe i i eness.
By using imme si e en i onmen s o emo e assis ance and aining, companies can d as ically
educe down ime and a el cos s, b inging expe knowledge o any loca ion ins an ly. The
in eg a ion o imme si e applica ions in o manu ac u ing p ocesses os e s agile and esponsi e
ope a ions, enabling manu ac u e s o econ igu e p oduc ion lines i ually and es new
p oduc s' easibili y wi hou dis up ing he ac ual ac o y loo .
Indus ial imme si e echnologies a e se ing as a c i ical expe imen al labo a o y o he nex -
gene a ion ision In e ne , called Web 4.0, and he b oade de elopmen o i ual wo lds.
The demands o indus ial applica ions o high- ideli y digi al wins, imme si e iple s, eal- ime
collabo a ion, and high eliabili y a e pushing he bounda ies o wha is possible, se ing a high
ba o pe o mance ha will e en ually ansi ion o consume expe iences. The solu ions
enginee ed o hese imme si e en i onmen s a e laying he g oundwo k o he nex -gene a ion
in e ac i e In e ne .
Indus y is pionee ing he concep o digi al wins and imme si e iple s ha a e con inuously
synch onised wi h hei eal-wo ld coun e pa s.
The echnical expe ise acqui ed in building and main aining hese complex sys ems o sys ems,
always-on i ual en i onmen s, p o ides a e e ence o c ea ing scalable and dynamic sha ed
i ual wo lds. These indus ial me a e ses, omni e ses, and mul i e ses buil o mission-c i ical
pu poses a e becoming he ounda ional es beds o he echnologies and p ac ices ha will
de ine mo e expansi e and socially- ocused i ual ealms.
The high us and iden i y, including secu i y, eliabili y, esilience, obus ness, p i acy and
se e al o he quali y equi emen s o indus ial se ings, a e accele a ing solu ions ha will be
i al o a us wo hy Web 4.0. When con olling c i ical in as uc u e o handling sensi i e
con en in an imme si e en i onmen , obus me hods o e i ying iden i y and secu ing da a
a e pa amoun . These high-s akes secu i y models, o ged o p e en indus ial in e e ence o
ope a ional ailu e, can p o ide he secu e amewo k needed o manage digi al iden i y, asse
owne ship, and economic ansac ions in he b oade i ual economies o he u u e.
The collabo a i e po en ial o u u e concep s like he me a e se, omni e se and mul i e se
p o oundly accele a es inno a ion. These sha ed i ual spaces se e as pe sis en , in e ac i e
pla o ms whe e globally dispe sed eams o enginee s, designe s, and s akeholde s can co-
c ea e and inno a e in eal- ime. The i ual collabo a ion and in e ac ion using indus ial
imme si e applica ions acili a es apid p o o yping, ins an eedback loops, and ex ensi e
es ing in simula ed en i onmen s, d ama ically educing he ime and cos equi ed o b ing
no el p oduc s om concep o ma ke .
Looking b oade , he de elopmen o Web 4.0, a decen alised and in elligen in e ne , p o ides
he ounda ional us and economic laye o his new indus ial pa adigm. I s decen alised
na u e, o en buil on blockchain, can ensu e secu e and anspa en da a exchange be ween
coun less IoT de ices, au onomous sys ems, and di e en company pla o ms, os e ing
in e ope abili y and us in mul i-s akeholde ecosys ems. This can c ea e a secu e amewo k
o a machine- o-machine economy, enhance supply chain anspa ency, and suppo a mo e
sus ainable u u e by enabling p ecise esou ce managemen .
The u u e o wo k is also eshaped, empowe ing a dis ibu ed wo k o ce wi h imme si e ools
ha make emo e collabo a ion as e ec i e as being physically p esen , ensu ing con inuous
inno a ion and economic g ow h.
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3 The Indus ial Imme si e Con inuum
Human beha iou occu s in he ime and space o he physical wo ld. Spa ial- empo ali y
ensu es ha human beha iou p oceeds no mally and limi s human beha iou om de ia ing
om i s no ms. The concep o hype -spa ial- empo ali y has been men ioned and in oduced
in Chinese my hology, Taoism, and some Wes e n philosophical heo ies. Human imagina ion
and explo a ion o i ual wo lds ha e led o he g adual concep ualisa ion o hype -spa ial-
empo ali y [6].
The con e gence o dis up i e echnologies such as he In e ne o Things (IoT), a i icial
in elligence (AI), digi al wins (DT), and spa ial compu ing is o ging a complex and powe ul
indus ial eal-digi al- i ual con inuum. This con inuum ep esen s a new pa adigm o digi al
ans o ma ion, whe e he bounda ies be ween he physical and i ual wo lds a e inc easingly
blu ed. I is ealised h ough imme si e en i onmen s, powe ed by augmen ed eali y (AR),
i ual eali y (VR), mixed eali y (MR), and ex ended eali y (XR), which allow use s o eel
physically p esen and in e ac wi h digi al con en na u ally. These echnologies a e he
building blocks o u u e i ual wo lds, including concep s like he me a e se and he nex -
gene a ion spa ial web (Web 4.0), and hei applica ion in indus ial se ings is poised o unlock
unp eceden ed e iciencies and capabili ies [24].
Indus ial imme si e solu ions a e mo ing beyond isola ed applica ions o become in eg a ed
pla o ms ha combine da a om he eal wo ld wi h powe ul simula ions and in e ac i e i ual
en i onmen s. This enables a con inuous low o in o ma ion be ween physical asse s and hei
digi al coun e pa s, allowing o mo e inno a i e design, mo e e icien ope a ions, and mo e
e ec i e aining. F om he ac o y loo o he ope a ing oom, hese echnologies a e
undamen ally changing how we wo k, lea n, and collabo a e. This documen p o ides a
comp ehensi e o e iew o he ad ancemen s, challenges, and u u e esea ch ends o
imme si e applica ions ac oss key indus ial sec o s, highligh ing he ans o ma i e po en ial o
his new echnological wa e.
Nex gene a ion i ual wo lds will le e age ecen and u u e de elopmen s in AI, XR, and IoT
bu also connec i i y and in as uc u e ad ances. While o e ing many open oppo uni ies,
u u e i ual wo lds also come wi h many challenges including echnical, socie al, economic
and legal ones. The oppo uni y s isk ade-o mus he e o e be ca e ully add essed om he
ea lies s ages o de elopmen and deploymen . The oppo uni ies o e ed by imme si e
applica ions o socie y and he economy a e signi ican in se e al sec o s [68].
Se e al key ad ancemen s ha e enabled he widesp ead adop ion o imme si e echnologies
ac oss indus ies. The de elopmen o high- ideli y, ligh weigh , and un e he ed XR headse s has
signi ican ly imp o ed use com o and mobili y, making hem p ac ical o use in dynamic
indus ial en i onmen s [1]. Ad ances in compu e ision (CV) and AI-powe ed spa ial mapping
enable hese de ices o unde s and and in e ac wi h he physical wo ld in eal- ime, allowing
o con ex -awa e AR o e lays and seamless MR in e ac ions. The in eg a ion o digi al win
echnology is a pa icula ly impac ul ad ancemen , allowing companies o c ea e highly
de ailed, da a- ich i ual eplicas o physical asse s, p ocesses, and sys ems. These digi al wins
can be used in imme si e en i onmen s o simula ion, moni o ing, and p edic i e main enance,
p o iding immense alue [64].
The ma u a ion o cloud and edge compu ing in as uc u e has p o ided he necessa y
compu a ional powe o ende complex scenes and p ocess la ge da ase s wi h low la ency,
which is c i ical o collabo a i e and esponsi e imme si e expe iences. An example o
applica ions o edge in elligence o he Me a e se is p esen ed in [61]. The in as uc u e laye
le e ages edge in elligence o suppo AI o he in elligen Me a e se (e.g., edge o AI) and
u ilise AI o ealize he esou ce e icien collabo a i e edge pa adigm (e.g., AI o edge) [61].
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Figu e 3-1 Applica ions o Edge In elligence o he Me a e se [61].
Despi e his p og ess, se e al c oss-cu ing challenges impede he ull ealisa ion o indus ial
imme si e applica ions.
A p ima y hu dle is he need o obus s anda disa ion and in e ope abili y. Many cu en
solu ions exis in p op ie a y, siloed ecosys ems, which limi he abili y o sha e da a and asse s
be ween di e en pla o ms and applica ions [29]. Ano he signi ican challenge is da a
in eg a ion. Indus ial en i onmen s a e complex and in ol e nume ous legacy sys ems, senso s,
and da a o ma s. In eg a ing his dispa a e da a in o a cohesi e and usable o ma o
imme si e applica ions equi es signi ican e o and expe ise. Ne wo k eliabili y, low la ency
and bandwid h a e also c i ical conce ns, especially o applica ions ha equi e he eal- ime
s eaming o high- ideli y 3D da a in emo e o mobile se ings [17]. Finally, use accep ance and
wo k o ce aining emain c ucial ac o s.
Ensu ing ha imme si e in e aces a e in ui i e and com o able o long- e m use, and ha
wo ke s a e adequa ely ained o use hem e ec i ely, a e key o success ul implemen a ion.
Imme si e echnologies a e e ol ing and a e be e adap ed o mee he needs o imme si e
expe iences, which os e s a pa adigm shi illus a ed by h ee ends [51]:
• The con e gence o echnologies like b oade senso y spaces o in eg a e senses such
as isual, audi o y, kines he ics, ac ile, smell and as e, o ensu e be e senso y co e age
and mul isenso y in e ac ion, ac ua ion, hap ic e ol ing in o a se o MR solu ions,
enabling bo h AR and VR.
• The di e si ica ion o uses spans ields such as main enance, heal h, sa e y, indus ial
p oduc ion, assembly, and b oade domains, including imme si e leisu e, imme si e
aining, and imme si e AI.
• The eme gence o en i onmen s ha a e some imes ealis ic and conc e e, o
me apho ical and abs ac , can acili a e access o and in e ac ion wi h AI ools. The
p og ess o AI is e iden wi h he use o ex ual/ ocal AI (cha bo , ocal assis an ) and
u he gene a i e AI, AI agen s and agen ic AI o suppo analysis o decision
unc ionali ies.
Among he di e en imme si e echnologies, he Me a e se has e ol ed in he las yea s wi h
applica ions in di e en indus ial sec o s [67].

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The Me a e se is composed o he wo ds "me a" and " e se" (me a comes om G eek, meaning
" anscending," and " e se" means "uni e se"). I is a pa allel wo ld closely connec ed o he eal
wo ld, he p oduc o he de elopmen and in eg a ion o a ious echnologies, he nex s age
o In e ne de elopmen , and a i ual li ing space wi h social a ibu es. The Me a e se's
de elopmen ep esen s a ce ain ex en o he i ual wo ld's de elopmen . Fi e s ages in he
e olu ion o i ual wo lds a e p oposed in [30], om hose ha ini ially exis ed only in li e a u e
and games o oday's imme si e 3D i ual wo lds, whe e use s can c ea e con en
independen ly [6].
An example o he Me a e se a chi ec u e [52] is illus a ed in Figu e 3-2, whe e human socie y
is cen ed a ound use s who in e ac wi h digi al a a a s h ough sma wea able de ices and
echnologies like human-compu e in e ac ion and XR.
Figu e 3-2 Me a e se A chi ec u e – Combina ion Human, Physical, and Digi al Realms (Sou ce: Adap ed om [52]).
The physical in as uc u e acili a es da a pe cep ion, ansmission, p ocessing, and physical
con ol h ough sma objec s, senso s, and di e se ne wo ks. These in as uc u es assis he
in e ac ion be ween he digi al and human wo lds. The digi al wo ld comp ises in e connec ed
sub-me a e ses, o e ing use s a ange o i ual goods/se ices and en i onmen s. The
me a e se engine le e ages his in e ac i i y o gene a e, main ain, and upda e he i ual
wo ld using da a om he eal wo ld, AI, digi al wins, and blockchain (BC) echnologies o
ensu e he ichness and sus ainabili y o he me a e se ecosys em. In he me a e se, in o ma ion
lows eely ac oss each wo ld, whe he human, physical, digi al, i ual, o cybe , d i en by
social ne wo ks, IoT in as uc u e, and he me a e se engine. The IoT de ices b idge hese
wo lds, acili a ing he in e ac ion be ween he physical and digi al ealms and allowing
seamless in o ma ion low [52].
The Indus ial Me a e se e e s o he es ablishmen o a sha ed i ual space empowe ed by
Me a e se echnologies, o suppo mul i-use , mul i-de ice indus ial scena ios o 3D modelling
and imme si e in e ac ion, and used o p oduc design, p oduc ion ope a ions, indus ial quali y
inspec ion, and p oduc es ing o indus ial p oduc ion [53] d i en by key enabling
echnologies, including blockchain, digi al wins, imme si e iple s, IoT, 5G/6G, imme si e
echnologies (AR, VR, XR, MR, Web 4.0) and AI.
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The e e ence a chi ec u e o he indus ial Me a e se in indus ial scena ios desc ibed in [53] is
p esen ed in Figu e 3-3.
The a chi ec u e includes h ee laye s: he da a inpu laye , he enabling laye , and he indus ial
applica ion laye . The enabling laye comp ises six componen s: AI, DT, BC, XR, he Me a e se
managemen cen e, and he da a p ocessing sys em.
Figu e 3-3 Indus ial Me a e se A chi ec u e [53].
Building in o ma ion modelling (BIM) is used in he a chi ec u e and building indus y o enhance
he quali y o documen a ion p oduced, as well as imp o e cons uc abili y.
New echnological de elopmen s combine BIM wi h imme si e echnologies like AR o enable
he physical con ex o each cons uc ion ac i i y o ask o be isualised in eal- ime, using AR
ubiqui ously (including con ex awa eness) and hus ope a e in conjunc ion wi h acking and
sensing echnologies [37].
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The Me a e se p esen s a compelling and ans o ma i e ision o he u u e o communica ion
sys ems, ma ked by g ea e imme sion, in e ac i i y, and inclusi i y. I is c ucial o de elop a
obus unde s anding o he Me a e se’s echnical equi emen s, socie al implica ions, and
economic po en ial. This unde s anding is key o ensu ing i s success ul in eg a ion in o daily li es
and le e aging i o he b oade bene i s o socie y [54].
Ini ially, he Me a e se has eme ged om he con e gence o h ee majo digi al echnologies:
gaming, AR/VR, and Web3. The echnological de elopmen s among hese majo digi al
echnologies ha e esul ed in pla o m ecosys ems consis ing o connec ed bu un ela ed
s akeholde s om di e en backg ounds, and complex sys ems comp ising componen s om
a ious sec o s ha a e con e ging.
The new wa e o imme si e echnologies add esses he in eg a ion o 6G-enabled edge AI and
Me a e se, di e en ypes o edge-Me a e se a chi ec u es ha use 6G-enabled edge AI o
sol e esou ce and compu ing cons ain s in Me a e se [63]. 6G edge in elligence has he
ad an ages o low la ency, compu ing o load, and high pe o mance while b inging high-
accu acy posi ioning suppo ing imme si e solu ions. The applica ion o 6G-o ien ed edge
in elligence o e s se e al bene i s, including balanced da a s o age, e icien da a ansmission,
and high eliabili y wi h e y low la ency. Figu e 3-4 shows an example o an a chi ec u e o he
Me a e se, which includes physical laye , i ual laye , and echnical laye ha suppo s he
eal- ime in e ac ion o use s in he physical- i ual wo ld [63].
Figu e 3-4 Me a e se A chi ec u e - Real-Time Physical-Vi ual Wo ld In e ac ion Suppo ed by Low Bandwid h, Low La ency,
Ubiqui ous Access, and T us wo hiness [63].
The u u e imme si e applica ions a e expec ed o use he 6G mobile communica ion
echnology has se e al ad an ages such as high pe o mance, global co e age, eal- ime
p ocessing, high eliabili y, and ene gy e iciency. Table 3-1shows a compa ison be ween 5G
and 6G echnologies [63] wi h speci ic pe o mance indica o s ha a e key o imme si e
applica ions.
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Table 3-1 Pe o mance indica o s compa ison be ween 6G and 5G [63].
Pe o mance Indica o s
5G
6G
Peak ansmission a e
10 ∼ 20 Gb/s
100 Gb/s ∼ 1 Tb/s
Use expe ience a e
0.1 ∼ 1 Gb/s
30 ∼ 50 Gb/s
Time delay
10 ∼ 50 ms
0.1 ∼ 1 ms
Reliabili y
10−5
10−9
Flow densi y
10 Tbps/km2
100 ∼ 1000 Tbps/km2
Posi ioning p ecision
1 ∼ 10 m
0.1 ∼ 1 m
Connec ion densi y
1 million/km3
10 ∼ 100 million/km3
Ne wo k e iciency
100 bi /s
200 bi /s
Mobili y
500 km/h
1000 km/h
Spec um bandwid h
30 ∼ 100 bps/Hz
200 ∼ 500 bps/Hz
Base s a ion compu ing powe
100 ∼ 200 Tops
1000 Tops
Co e age
Pa ial
Global
Secu i y
Pa chy secu i y
Endogenous secu i y
In o ma ion imeliness
High
Ex emely high
As he echnologies ha e de eloped, i has become e iden ha he in e play o hese
echnologies, along wi h he popula i y and signi icance o pla o m ecosys ems, necessi a es
he de elopmen o echnology s anda ds a mul iple le els, om da a o unc ional in e aces
and p o ocols, by building on a ounda ion o open s anda ds. The echnical s anda ds
applicable in he Me a e se a e es ablished no ms o ub ics o he common and epea ed use
o ules, condi ions, guidelines, o cha ac e is ics o p oduc s o ela ed p ocesses and
p oduc ion me hods, as well as ela ed managemen sys em p ac ices. Responsible s anda ds
de elopmen is a equi ed componen o he comme cial esea ch and inno a ion p ocess. The
concep o esponsible inno a ion has been desc ibed as an inclusi e and isk-mi iga ing
app oach o esea ch and inno a ion. I aims o ensu e ha unin ended nega i e impac s a e
a oided, ha ba ie s o dissemina ion, adop ion and di usion o esea ch and inno a ion a e
educed, and ha he posi i e socie al and economic bene i s o esea ch and inno a ion a e
ealised [55][56].
De ailed discussions o s anda disa ion ac i i ies ela ed o imme si e echnologies, including
he Me a e se, a e p esen ed in [57][59][29][24] and in he chap e co e ing s anda disa ion
and in e ope abili y.
The u u e o indus ial imme si e applica ions will be de ined by g ea e in elligence, deepe
in eg a ion, and seamless in e ope abili y. The nex gene a ion o hese sys ems will be powe ed
by inc easingly sophis ica ed AI, enabling en i onmen s ha a e no jus in e ac i e bu a e uly
esponsi e and adap i e o use beha iou and con ex .
The concep o he imme si e iple , which links he physical asse , i s eal- ime digi al win, and
an AI-d i en p edic i e model, will become mo e p e alen , allowing o p oac i e
main enance and ope a ional op imisa ion. The ise o he IoT o Senses (IoTS) will enable iche
mul i-senso y expe iences, inco po a ing hap ics, and e en smell and as e, o c ea e a mo e
p o ound sense o p esence.
A p ima y ocus o u u e esea ch is on de eloping open, in e ope able s anda ds necessa y o
a ue indus ial me a e se, enabling seamless ans e o da a, asse s, and a a a s be ween
pla o ms.
The de elopmen o he nex -gene a ion spa ial web (Web 4.0) will p o ide he decen alised
in as uc u e needed o his ision. As hese echnologies become inc easingly in eg a ed in o
c i ical indus ial p ocesses, ensu ing hei us wo hiness, secu i y, and e hical use will be o
pa amoun impo ance. The indus ial eal-digi al- i ual con inuum is s ill in i s ea ly s ages, bu
i s po en ial o enhance human capabili y, op imise complex sys ems, and d i e he nex wa e
o digi al ans o ma ion is undeniable.
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5 Manu ac u ing
The manu ac u ing sec o is u ilising imme si e echnologies o c ea e he " ac o y o he u u e."
Digi al wins o en i e p oduc ion lines a e isualised and manipula ed in VR o op imise layou s,
simula e p oduc ion lows, and iden i y po en ial bo lenecks be o e any physical equipmen is
ins alled o al e ed [37].
The " i ual commissioning" d as ically educes se up imes and p oduc ion isks. Fo wo ke
aining, VR o e s a sa e and epea able en i onmen in which o lea n how o ope a e complex
machine y o pe o m haza dous asks wi hou isk o he indi idual o he equipmen .
Remo e assis ance ia AR is ano he key applica ion, whe e an expe loca ed anywhe e in he
wo ld can see wha a ield echnician sees and p o ide eal- ime guidance by anno a ing he
echnician's iew, signi ican ly educing machine down ime and a el cos s [38].
The inclusion o VR/AR echnologies in manu ac u ing p ocesses has he po en ial o bene i
signi ican ly adop e s, especially in e ms o inc eased e iciency and p oduc i i y, hanks o
adi ional p ocesses being ca ied ou in a cheape and as e way.
Employees can also bene i om VR/AR adop ion, including highe -quali y aining, inc eased
wo kplace secu i y, and oppo uni ies o upskill hei digi al knowledge. VR/AR can e olu ionise
many manu ac u ing p ocesses.
Assembly and main enance enhanced by AR in o ma ion can minimise mis akes while educing
he need o on-si e suppo o expe s, especially in combina ion wi h IoT and AI echnologies.
Pe sonnel ge ing a -a-glance de ails on a speci ic p oduc o ma e ial could also be mo e
e icien in aw ma e ial p epa a ion, and in se ing up p oduc ion p ocesses [65].
P oduc de elopmen al eady highly bene i s om VR, since indus ial p oduc design allows o
a mo e in ui i e design phase and an easie cus ome −manu ac u e in e ac ion. The u ilisa ion
o 3D models o DT du ing he p o o yping phase b ings nume ous bene i s.
The c ea ion o e ined i ual p oduc models is ins umen al in b idging he gap be ween
design and manu ac u ing and educing ma e ial was e, which is no iceable in cos and ime
sa ings.
Collabo a i e wo king in manu ac u ing is an eme ging applica ion whe e se e al ac o s a e
usually engaged du ing he i s s ages o p o o yping and p oduc design.
Mee ings conduc ed in a 3D and in e ac i e en i onmen allow design eams om di e en
physical loca ions o come oge he in pho o ealis ic i ual spaces, minimising he eliance on
physical models and p o o ypes. Remo e collabo a ion is e ol ing in applica ions such as
emo e guidance and ac i i y supe ision ac oss a ious manu ac u ing sec o s [65].
5.1 Indus ial Me a e se
5.1.1 Scena io
The Indus ial Me a e se ep esen s a pa adigm shi in he domain o indus ial ope a ions and
main enance. I is a concep ha in ol es he deep in eg a ion o he IoT, AI, and a spec um
o imme si e echnologies including VR/AR/MR. The p ima y objec i e is o e olu ionize how
indus ial en i onmen s a e managed and ope a ed.

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Figu e 5-1 –Indus ial Me a e se
The applica ion enables he c ea ion o comp ehensi e i ual simula ions o p oduc s,
p ocesses, and en i e ac o ies. I allows o eal- ime moni o ing o physical asse s h ough hei
digi al wins, acili a es ad anced p edic i e main enance o an icipa e ailu es, and suppo s
seamless emo e collabo a ion be ween eams. By p o iding a ich, da a-d i en i ual
en i onmen , he Indus ial Me a e se suppo s enhanced decision-making, helps c ea e mo e
e icien p oduc ion wo k lows, and allows o eal- ime oubleshoo ing o complex issues.
5.1.2 Use s and s akeholde s
The use s o he Indus ial Me a e se a e di e se, spanning he en i e li ecycle o indus ial
ope a ions. This includes design enginee s who can p o o ype and es in a i ual space,
p oduc ion manage s who can simula e and op imise wo k lows, and main enance echnicians
who can ecei e emo e expe guidance o p ac ice complex epai s in a sa e, simula ed
en i onmen .
The key s akeholde s a e he indus ial en e p ises hemsel es. By adop ing his echnology, hey
can achie e signi ican imp o emen s in ope a ional e iciency, educe cos ly down ime, and
enhance o e all p oduc quali y. The inno a ions os e ed by he Indus ial Me a e se a e
in ended o c ea e mo e sus ainable p ac ices and cul i a e a highly adap i e and compe i i e
indus ial landscape o hese o ganisa ions.
5.1.3 Implemen a ion in a i ual wo ld and added alues
The implemen a ion o his concep is a Me a e se, a pe sis en , sha ed, and in e ac i e i ual
space ha mi o s he physical indus ial wo ld. This is no a single applica ion bu an ecosys em
o in e connec ed digi al wins and simula ions. The use in e ace wi hin his wo ld is buil on
de ailed 3D elemen s, holog aphic displays, and in e ac i e na iga ion sys ems ha p o ide
in ui i e pa hways o explo e i ual ac o ies and indus ial layou s.
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The added alue is ans o ma i e. The sys em di ec ly con ibu es o sus ainabili y by enabling
i ual p o o yping ha educes physical was e, op imizing esou ce usage h ough AI-d i en
analy ics, and suppo ing emo e collabo a ion o minimize a el- ela ed emissions. I enhances
sa e y by allowing aining and p oblem-sol ing in a isk- ee en i onmen . Ul ima ely, hese
inno a ions lead o educed down ime, imp o ed p oduc quali y, and a mo e esilien and
agile indus ial ope a ion.
5.1.4 Requi ed imme si e echnologies unc ionali ies
The unc ional and echnological ounda ion o he Indus ial Me a e se is ex ensi e and mul i-
laye ed. Use in e ac ion is designed o be highly in ui i e, u ilising a ange o modes including
hand ges u es o manipula ing i ual ools, oice commands o hands- ee ope a ion, and eye
acking o gaze-based con ol. VR con olle s o e p ecision o speci ic asks, while eme ging
in e aces like B ain-Compu e In e aces (BCIs) a e on he ho izon. Use eedback is deli e ed
h ough ealis ic hap ic eedback and AR displays ha p o ide eal- ime da a o e lays.
The sys em equi es seamless HMI and IoT in eg a ion, le e aging da a om mo ion, dep h, and
p oximi y senso s, along wi h en i onmen al ac ua o s o p ecise da a collec ion. To p ocess his
as amoun o da a and ende complex simula ions in eal ime, he a chi ec u e elies on low-
la ency edge compu ing wi h high-pe o mance CPUs and GPUs. This is suppo ed by high-
speed 5G o 6G ne wo ks o ensu e seamless da a ansmission and eal- ime upda es.
Da a managemen mus suppo complex 3D and spa io- empo al da a, wi h scalable local
and cloud s o age solu ions. A co e componen is he in eg a ion o AI algo i hms o p edic i e
analy ics, compu e ision, and na u al language p ocessing. The en i e sys em is buil o be
pla o m-agnos ic, wi h suppo o ha dwa e like Oculus Ri and Mic oso HoloLens, using SDKs
ha ensu e lexibili y and b oad usabili y.
The use case is using Me a e se as imme si e echnology.
Hand ges u es ha acili a e in ui i e manipula ion o i ual ools and equipmen in indus ial
p ocesses. Voice commands enabling hands- ee ope a ion o complex machine y and
sys ems, imp o ing wo k low e iciency. Eye acking, which enhances sa e y and e iciency by
enabling gaze-based con ol and moni o ing in c i ical indus ial asks.
Mo ion acking, which accu a ely eplica es wo ke mo emen s o i ual aining and
e gonomic analysis in indus ial se ings. B ain-Compu e In e aces (BCIs) as an eme ging
in e ace o enabling con ol o machine y and sys ems in high-s akes en i onmen s wi h
minimal physical inpu . VR con olle s used o o e p ecise con ol o aining simula ions and
in e ac ion wi h i ual eplicas o indus ial se ups.
3D elemen s used o enable de ailed isualiza ion o indus ial asse s, p ocesses, and
en i onmen s o enhanced design and decision-making. In e ac i e na iga ion sys ems ha
p o ide in ui i e pa hways o explo e i ual ac o ies and indus ial layou s, imp o ing use
expe ience and e iciency. Real- ime o e lays, which deli e c i ical ope a ional da a and
ins uc ions o e laid on physical o i ual equipmen o p ecise and imely ac ions. Holog aphic
displays used o c ea e imme si e 3D p ojec ions o indus ial sys ems, allowing collabo a i e
planning and p oblem-sol ing in i ual spaces.
Hap ic eedback ha p o ides ealis ic ac ile eedback o emo e ope a ion and p ecision
asks in simula ed en i onmen s. AR displays, which deli e s eal- ime ope a ional da a and s ep-
by-s ep ins uc ions di ec ly wi hin he use 's ield o iew o main enance and assembly.
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5.1.5 Technology laye s equi emen s
Seamless in e aces in eg a ing AR/VR ools and IoT-enabled de ices o eal- ime in e ac ion.
Mo ion, dep h, and p oximi y senso s, along wi h en i onmen al ac ua o s, o p ecise da a
collec ion and in e ac ion. Low-la ency p ocessing a he edge wi h high-pe o mance
CPUs/GPUs o suppo eal- ime imme si e expe iences. High-speed 5G/6G ne wo ks and
in elligen connec i i y o ensu e seamless da a ansmission and eal- ime upda es.
Suppo o 3D and spa ial- empo al da a wi h scalable local/cloud s o age o li ecycle
managemen and quick e ie al. In eg a ion o AI algo i hms o p edic i e analy ics, compu e
ision, and na u al language p ocessing o enhance wo k lows. Suppo o pla o ms like Oculus
Ri and Mic oso HoloLens wi h SDKs ensu ing usabili y and lexibili y.
5.1.6 Ho izon al issues and cha ac e is ics
T us wo hiness is a ounda ional equi emen , demanding obus secu i y, s ic use da a
p i acy, and ull compliance wi h indus y egula ions o main ain ope a ional in eg i y.
The sys em's e hical amewo k is designed o p io i ize use p i acy and da a secu i y, sa egua d
agains bias, and ensu e i does no disc imina e, gua an eeing equi able ope a ion. The sys em
is designed o high in e ac i i y, wi h eal- ime esponsi eness o use ac ions c ea ing a
dynamic and imme si e expe ience ha seamlessly blends physical and digi al eali ies.
The sys em pe cei es a wide ange o human inpu s, including ges u es, oice commands,
biome ic da a, and spa ial posi ioning, o enable i s ich in e ac i i y.
A commi men o s anda disa ion and in e ope abili y is essen ial o ensu e ha di e en sys ems
and pla o ms wi hin he me a e se can communica e and wo k oge he , allowing o a uly
in eg a ed and collabo a i e en i onmen . This os e s a compe i i e landscape whe e di e en
echnologies can connec and enhance he o e all ecosys em.
Robus secu i y, use da a p i acy, and compliance wi h indus y egula ions o main ain
ope a ional in eg i y.
Ensu es e hical use by p io i izing use p i acy, da a secu i y, and compliance wi h egula o y
s anda ds. I sa egua ds agains bias and does no disc imina e agains indi iduals o g oups,
ensu ing equi able ope a ion.
The inpu s he sys em pe cei es om humans a e ges u es, oice commands, biome ic da a,
and spa ial posi ioning.
The in e ac i i y o he sys em is ma e ialised h ough eal- ime esponsi eness o use ac ions,
p o iding a dynamic and imme si e expe ience.
The imme si eness o he sys em is ep esen ed by seamless blending o physical and digi al
eali ies, os e ing a sense o p esence in he indus ial me a e se.
The use case p omo es sus ainabili y by enabling i ual p o o yping, educing physical was e,
op imizing esou ce usage h ough AI-d i en analy ics, and suppo ing emo e collabo a ion o
minimize a el- ela ed emissions.
The use case complies wi h indus y s anda ds o ensu e sys em compa ibili y and
in e ope abili y ac oss a ious pla o ms.
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5.2 Robo ic Welding
5.2.1 Scena io
WELD-E a ge s he manu ac u ing sec o and de elops a highly in e ac i e, esponsi e and
imme si e, bidi ec ional, XR-based, Human-Machine-In e ac ion applica ion acili a ing eal-
ime coope a ion be ween a welding expe and a specialised obo ha pe o ms welding
ope a ions.
Welding expe s shall be able o p o ide bo h oice and isual suppo o he obo ic welde
enabling i o execu e p ecise welding asks ollowing expe s’ guidelines and eedback. Mo e
speci ically, he applica ion uses a combina ion o AI models such as Au oma ic Speech
Recogni ion, Neu al Machine T ansla ion,
Visual Language models and a Con e sa ion Agen (componen s de eloped and enhanced
om he VOXREALITY p ojec ). A e conduc ing each s ep o he welding p ocess, he obo ic
welde sends eal- ime eedback o he welding expe , enabling comp ehensi e, end- o-end
suppo .
Figu e 5-2 – Imme si e Welding
5.2.2 Use s and s akeholde s
The p ima y use o he WELD-E sys em is he welding expe , who le e ages hei specialized
knowledge o guide he obo ic a m emo ely. This allows a single expe o o e see mul iple
ope a ions o o p o ide guidance in en i onmen s ha a e haza dous o di icul o access.
O he key s akeholde s include he manu ac u ing companies ha own and ope a e he
obo ic welde s, as hei ope a ional e iciency and quali y con ol a e di ec ly imp o ed.
The de elopmen eam, building upon he ounda ion o he VOXREALITY p ojec , is also a
s akeholde , as a e he p o ide s o he ha dwa e componen s such as he obo ic a ms and
he mixed eali y headse s.
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5.2.3 Implemen a ion in a i ual wo ld and added alues
The implemen a ion o WELD-E is g ounded in Mixed Reali y (MR) echnology, expe ienced
h ough a HoloLens headse . As Indus y 5.0 echnologies ans o m manu ac u ing, XR
applica ions a e becoming essen ial o eleope a ion o in ica e machine y. WELD-E capi alizes
on hese ad ancemen s o c ea e a sys em whe e complex human-machine in e ac ions eel
in ui i e and seamless.
The sys em uses i ual and physical da a o gene a e 3D holog ams, c ea ing an accu a e
ep esen a ion o he welding en i onmen ha is supe imposed on o he use 's eal-wo ld iew.
The p ima y added alue o WELD-E is i s abili y o e icien ly moni o and guide obo ic welding
asks in eal- ime by combining human expe ise wi h ad anced AI in an imme si e, mul i-modal
XR en i onmen . This enhances sa e y, imp o es p ecision, and allows o he democ a iza ion
o expe knowledge ac oss he ac o y loo .
5.2.4 Requi ed imme si e echnologies unc ionali ies
The sys em is buil on a ounda ion o Mixed Reali y, wi h use in e ac ion acili a ed h ough a
combina ion o oice commands, hand acking, and con olle inpu s.
The use in e ace inco po a es bo h 2D elemen s o displaying da a and menus, and 3D
elemen s o na iga ing he spa ial en i onmen and in e ac ing wi h he digi al win o he
obo . Feedback is p o ided o he use h ough bo h audio cues and isual o e lays wi hin he
MR en i onmen .
A signi ican po ion o he sys em's unc ionali y is d i en by a sophis ica ed AI pipeline. WELD-E
o e s an end- o-end, model-based, and oice-d i en sys em ha u ilises p e-exis ing models
om he VOXReali y p ojec , including an Au oma ic Speech Recogni ion model based on
OpenAI's Whispe and a con ex -awa e Neu al Machine T ansla ion model.
WELD-E enhances his ounda ion wi h wo new componen s: an open-sou ce Coqui-TTS model
o na u al, mul ilingual oice syn hesis, and he Welding La ge Language Model (WeLLM).
WeLLM is a ine uned LLM ha imp o es he accu acy o ansla ed commands and co ec s
po en ial e o s, ensu ing ins uc ions a e a ional and comp ehensible.
Fu he mo e, WELD-E cus omizes he VOXReali y Vision-Language model by in oducing an
au o-labelling and knowledge dis illa ion mechanism. This p ocess uses ounda ion models o
e ine bounding boxes in o p ecise segmen a ion masks, which a e hen used o ine une a
YOLO-NAS model. This allows he sys em o ecognize a b oade ange o objec s wi h g ea e
accu acy. The o e all goal is o ex end he capabili ies o he VOXReali y ecosys em o eal-
wo ld indus ial applica ions by enhancing echnical easoning.
The pla o m a chi ec u e in eg a es hese AI models wi hin he XR en i onmen . I consis s o
ROS2 (Robo Ope a ing Sys em) o obo ic communica ion, which is connec ed wi h a Uni y3D
applica ion. The XR en i onmen is deployed on a Mic oso HoloLens.
A cen al o ches a o , Welde-Connec , manages he da a low be ween he AI models, ROS2
nodes, and he XR applica ion ia REST API in e aces, ensu ing synch onized and e icien
ope a ions.
The ype o imme si e echnology used is MR (Hololens). The use in e ac ion modes a e based
on VR Con olle s, Hand T acking, Voice Commands. The use in e ace design employs 2D
Elemen s, 3D Elemen s (Na iga ion). The use eedback mechanisms comp ise o audio and
Visual eedback.

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5.2.5 Technology laye s equi emen s
The AI echnologies used a e NLP, ML models, AI in eg a ion poin s, oice in e ac ion, isual
ecogni ion, aining da a equi emen s, and in e ence capabili ies.
WELD-E o e s an end- o-end model-based and oice-d i en pipeline o suppo welding
ope a ions pe o med by obo ic a ms. To accomplish his, WELD-E u ilises p e-exis ing
VOXReali y models in a black-box manne —including Au oma ic Speech Recogni ion (ASR),
Neu al Machine T ansla ion (NMT) — bu also p o ides wo new a chi ec u al componen s: an
open-sou ce Tex - o-Speech (TTS) model and he Welding La ge Language Model (WeLLM). The
TTS componen adop s Coqui-TTS, an open-sou ce solu ion known o i s lexibili y and high-
quali y ou pu , enabling na u al and mul ilingual oice syn hesis.
The Welding La ge Language Model (WeLLM) consis s o a ine uned LLM model capable o
imp o ing he accu acy o ansla ed welding commands bu also co ec ing po en ial e o s
(misspellings, i a ional ph ases, hallucina ions p oduced om inco ec o noisy ansla ions,
e c.). This ensu es ha all echnical ins uc ions a e a ional and comp ehensible. By
inco po a ing TTS and WeLLM, WELD-E enhances he o e all use expe ience and he
ope a ional e iciency o he welding asks.
The Au oma ic Speech Recogni ion (ASR) model, eused om he VOXReali y p ojec , le e ages
OpenAI's Whispe a chi ec u e, ine uned wi h Adap e modules o imp o e pe o mance in low-
esou ce languages such as G eek. Meanwhile, he VOXReali y Neu al Machine T ansla ion
(NMT) model ocuses on con ex -awa e, obus , and simul aneous ansla ion, u ilising
echniques such as mul i-encode a chi ec u es and da a augmen a ion.
WELD-E cus omises he VOXReali y Vision-Language (VL) model by in oducing an au o-labelling
and knowledge dis illa ion mechanism o imp o e objec de ec ion capabili ies. This p ocess
uses ounda ion models o e ining bounding boxes in o p ecise segmen a ion masks. These
high-quali y masks a e hen used o ine une a YOLO-NAS model h ough knowledge dis illa ion
echniques, hus allowing i o ecognise a b oade ange o objec s wi h enhanced accu acy
and e iciency. WELD-E aims o ex end he scope and capabili ies o he VOXReali y model
ecosys em in eal-wo ld indus ial applica ions by enhancing hei echnical easoning and
capabili ies.
WELD-E is designed o in eg a e ad anced AI models wi hin an XR en i onmen o imp o e
welding ope a ions. The pla o m consis s o ROS2 (Robo Ope a ing Sys em) connec ed wi h
Uni y3D, while he XR en i onmen employs Mixed Reali y (MR) using Mic oso HoloLens. The
WELD-E model ecosys em includes a cen al o ches a o (Welde-Connec ), while in e ac ions
wi h he XR en i onmen a e conduc ed h ough REST API (OpenAPI) in e aces. Mic oso
HoloLens p o ides he necessa y MR capabili ies o ope a o s o in e ac wi h i ual elemen s
o e laid in he physical wo ld.
Uni y3D se es as he de elopmen engine ha c ea es he in e ac i e XR applica ion, o e ing
powe ul ools o 3D con en c ea ion. ROS2 ac s as he middlewa e o obo ic communica ion,
handling eal- ime da a exchange and ensu ing seamless coo dina ion be ween ha dwa e and
so wa e componen s. The cen al o ches a o manages he low o da a be ween he AI
models, ROS2 nodes, and he XR applica ion, ensu ing synch onised ope a ions and e icien
p ocessing. This a chi ec u e ensu es in e ope abili y wi h o he pla o ms and de ices, p o iding
lexibili y and obus ness in eal-wo ld indus ial applica ions.
5.2.6 Ho izon al issues and cha ac e is ics
The sys em is designed o pe cei e a a ie y o human inpu s o enable e ec i e and in ui i e
con ol. These include spoken commands in mul iple languages, hand ges u es o de ine ac ions
like a welding ajec o y, and ouch inpu s on holog aphic in e aces o basic commands. The
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sys em's in e ac i i y is a co e ea u e, p o iding ich eedback o he ope a o . This includes a
isual MR en i onmen ac ing as a digi al win o he welding a ea, o e ing eal- ime insigh s
and isual ale s. I also deli e s audio eedback wi h na u al speech o p o ide s a us upda es
and sa e y wa nings.
The imme si eness o he sys em is cen al o i s design. By using MR o supe impose 3D holog ams
and da a on o he physical wo ld, i allows o a seamless usion o i ual in o ma ion and eal-
wo ld con ex . This high le el o imme sion acili a es he in ui i e in e ac ion, isualiza ion, and
manipula ion o he digi al win, which is c i ical o he p ecise guidance o he welding obo .
The sys em ensu es ha all in e ac ions a e na u al, enhancing si ua ional awa eness and
decision-making o he expe ope a o . T us wo hiness is embedded h ough he AI's abili y o
a ionalize commands and he sys em's obus eedback loops, ensu ing sa e y and eliabili y in
an indus ial se ing.
The XR Solu ion pe cei es a ious human inpu s o enable e ec i e in e ac ion and con ol.
These include spoken commands in mul iple languages, which allow use s o issue ins uc ions
na u ally, and isual cues, such as ges u es o con ex ual signals wi hin he wo kspace.
The sys em pe cei es se e al ypes o inpu s om humans o enable in ui i e in e ac ion and
con ol:
• Voice Inpu s: Spoken commands o e bal ins uc ions in a ious languages, ha he
sys em p ocesses o execu e asks o p o ide eedback.
• Ges u es: Hand mo emen s o o he physical ges u es ecognised by he sys em o
igge ac ions, such as he welding ajec o y.
• Touch Inpu s: In e ac ion wi h i ual bu ons o slide s h ough ouch ges u es on
holog aphic in e aces o ini ia e basic ac ions such “S a Welding”, “Cancel Welding”
and so on.
The sys em also o e s se e al eedback channels o he ope a o s:
• Visual In e ac ion: A MR (Mixed Reali y) en i onmen ac ing as he Digi al Twin
ep esen a ion o he Welding A ea. This isualisa ion p o ides ope a o s wi h eal- ime
insigh s in o he welding p ocess, including spa ial layou s, ool posi ioning, and p og ess
upda es, enabling enhanced si ua ional awa eness and p ecise decision-making.
Addi ionally, i inco po a es isual ala ms and ale s, such as colou -coded wa nings o
lashing indica o s, o no i y ope a o s o po en ial sa e y haza ds, sys em mal unc ions,
o de ia ions in he welding p ocess.
• Audio eedback: Deli e s na u al, human-like speech o p o ide ope a o s wi h upda es
on he welding cycle and issues ale s when sa e y egula ions a e no ollowed.
Vi ual Reali y (VR), Mixed Reali y (MR), and Augmen ed Reali y (AR) a e example XR
echnologies ha a e essen ial in he de elopmen o eleope a ion mechanisms o in ica e
machine y and p ocesses.
As manu ac u ing in Indus y 5.0 has been ans o med by XR echnologies, i p og essi ely
depends mo e on complex and in elligen human-machine in e ac ions, especially in
specialised indus ial ields such as welding.
Accu a e ep esen a ions o in ended su oundings a e now possible hanks o ecen
de elopmen s in XR, which in u n simpli ies he in e ac ion, isualisa ion and manipula ion o 3D
objec s supe imposed o he eal wo ld. In e ac ions wi h hese imme si e ep esen a ions a e
acili a ed by XR sys ems, which o e 3D holog ams by using i ual and physical da a. The
p ima y objec i e o WELD-E is o e icien ly moni o and guide obo ic welding asks in eal ime
by combining human expe ise wi h ad anced AI in imme si e mul i-modal XR en i onmen s.
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5.3 Assis ance o Equipmen Se icing and Main enance
5.3.1 Scena io
This applica ion is a p oo o concep de eloped by he Aal o Fac o y o he Fu u e a Aal o
Uni e si y. I add esses a common challenge in complex indus ial en i onmen s: he e icien
se icing and main enance o equipmen .
The use case is ocussing on p o iding eal- ime assis ance o echnicians and wo ke s on he
ac o y loo .
Figu e 5-3 – Assis ance o Se icing
Figu e 5-4 – Imme si e Se icing
The sys em is designed o help a use loca e a speci ic piece o equipmen wi hin a la ge and
po en ially clu e ed acili y. I hen gene a es and displays a sa e pa h om he use 's cu en
loca ion o he a ge equipmen , p ojec ing his pa h di ec ly in o he use 's ield o iew ia an
Augmen ed Reali y headse . This guidance sys em is dynamic, upda ing he pa h in eal- ime as
he use mo es h ough he en i onmen .
Once he use eaches he equipmen , he applica ion p o ides a display o e lay showing
ele an in o ma ion abou ha asse .
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5.3.2 Use s and s akeholde s
The p ima y use s o his applica ion a e se ice echnicians and main enance wo ke s who a e
esponsible o he upkeep and epai o indus ial machine y. Thei wo k low is di ec ly impac ed
by he e iciency and sa e y o loca ing and iden i ying equipmen .
Key s akeholde s include ac o y loo manage s, who a e conce ned wi h o e all ope a ional
e iciency, wo k o ce p oduc i i y, and sa e y compliance. The Aal o Fac o y o he Fu u e
esea ch g oup a Aal o Uni e si y, a e also cen al s akeholde s as he de elope s and
inno a o s behind his p oo o concep .
5.3.3 Implemen a ion in a i ual wo ld and added alues
The applica ion is implemen ed using Augmen ed Reali y (AR) echnology, which o e lays
digi al in o ma ion on o he use 's iew o he physical wo ld. This c ea es an ex ended eali y
expe ience whe e i ual guidance is seamlessly in eg a ed wi h he eal en i onmen . The
implemen a ion does no c ea e a ully i ual wo ld bu a he enhances he exis ing physical
one.
The added alue o his sys em is subs an ial. I signi ican ly educes he ime echnicians spend
sea ching o equipmen , di ec ly imp o ing p oduc i i y. By calcula ing and displaying he
sa es possible ou e, he applica ion enhances wo ke sa e y, helping hem na iga e a ound
po en ial haza ds on he ac o y loo . Fu he mo e, by p o iding immedia e access o
equipmen -speci ic in o ma ion on he AR display, i can educe e o s and imp o e he quali y
o main enance asks.
5.3.4 Requi ed imme si e echnologies unc ionali ies
The unc ionali y o he sys em is buil upon a speci ic se o echnologies designed o obus
pe o mance in an indus ial se ing. The imme si e echnology employed is Augmen ed Reali y,
expe ienced h ough a Mic oso HoloLens 2 En e p ise headse . Use in e ac ion is managed
h ough ges u e acking and an AR con olle , allowing o in ui i e con ol o he applica ion.
The use in e ace is ocused on na iga ion and he display o 3D elemen s, wi h use eedback
p o ided isually.
The echnological ounda ion elies on Ul a-Wide Band (UWB) posi ioning modules o p ecise
loca ion acking. This se up includes ixed ancho s h oughou he en i onmen , a ag ca ied
by he use , and ags a ached o he equipmen o in e es . This allows o highly accu a e, eal-
ime spa ial awa eness.
All compu a ional p ocessing is handled a he edge, u ilising he onboa d p ocessing powe o
he HoloLens 2 and an N idia Je son de ice. This on-edge app oach ensu es low la ency and
eal- ime esponsi eness.
Fo connec i i y, he sys em ope a es on a 5,4GHz Wi-Fi ne wo k, p o iding he necessa y
bandwid h o s able communica ion. Da a s o age is minimal, wi h he applica ion ile being
s o ed locally on he de ice.
I is impo an o no e ha his p oo o concep does no cu en ly inco po a e any AI algo i hms;
i s logic is based on he eal- ime p ocessing o posi ioning da a. The chosen pla o m, Mic oso
HoloLens 2, p o ides he necessa y en e p ise-g ade ea u es and SDKs o his ype o indus ial
applica ion.
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5.6 XR-Based Human-Robo Collabo a ion Along a Con eyo Picking Line: The
case o Cons uc ion and Demoli ion Was e So ing
5.6.1 Scena io
The applica ion aims o le e age on human- obo collabo a ion (HRC) imp o e he e iciency
o cu en was e so ing p ac ices. By in oducing human dex e i y in he loop, HRC can imp o e
so ing e iciency, measu ed by speed and accu acy, app op ia ely combining human and
obo capabili ies. The XR echnology p o ides he necessa y eedback mechanism be ween
he con eyo -cobo so ing sys em and he humans aiding and supe ising he p ocess.
Figu e 5-8 – XR-Based Human-Robo Collabo a ion
5.6.2 Use and s akeholde s
P ima y use s include was e so ing ope a o s who in e ac di ec ly wi h he XR sys em and
cobo s on he con eyo line o pe o m so ing asks. S akeholde s encompass was e so ing
plan manage s ocused on e iciency gains, en i onmen al egula o s ensu ing compliance
wi h was e managemen s anda ds, and echnology p o ide s supplying XR ha dwa e and
so wa e. Addi ionally, wo ke s' unions may be in ol ed o add ess job impac s and sa e y
conce ns a ising om HRC in eg a ion.
5.6.3 Implemen a ion in a i ual wo ld and added alues
In a i ual wo ld, he sys em simula es he con eyo line en i onmen using digi al wins o
humans, cobo s and was e ma e ials, allowing ope a o s o ain in isk- ee scena ios and
op imise wo k lows. Added alues include educed physical s ain on humans h ough i ual
ehea sals, imp o ed accu acy in was e ca ego iza ion ia imme si e p e iews, and scalable
es ing o new so ing algo i hms wi hou dis up ing eal ope a ions.

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5.6.4 Requi ed imme si e echnologies unc ionali ies
The ype o imme si e echnologies used a e XR HMD and p ojec ion-based AR. These sys ems
o e lay eal- ime so ing da a and cobo in en on o HMD enhancing si ua ional awa eness.
Imme si e p ojec ions on he physical con eyo line suppo collabo a i e spaces o mul iple
use s, imp o ing eam-based so ing decisions.
The use in e ac ion modes a e hand acking o sys em con ol and o di ec manipula ion o
i ual objec s on he con eyo , enables he upda e o he cu en s a e o he i ual objec s o
assu e ask comple ion.
Voice commands allow hands- ee con ol, such as di ec ing cobo s o pick speci ic i ems o
pausing he line o inspec ion. These modes ensu e seamless in eg a ion in as -paced
en i onmen s, educing cogni i e load and enhancing collabo a i e e iciency be ween
humans and obo s.
The UI design combines 2D o e lays o eal- ime da a, like so ing s a is ics and ale s, wi h 3D
holog aphic ep esen a ions o was e i ems ha a e in e ac i e and allow he use o upda e
he s a e o an i em, e.g. change i s so ing classi ica ion. In ui i e icons and colo -coded
elemen s guide use ac ions, ensu ing accessibili y o di e se skill le els. Adap i e layou s adjus
based on use p e e ences, p omo ing e gonomic in e ac ion in p olonged sessions.
5.6.4.1 Use Feedback Mechanisms
The use eedback mechanisms include isual eedback includes augmen ed o e lays
highligh ing de ec ed objec s, e o ale s, p og ess indica o s on he con eyo line o con i m
ac ions, and cobo pa hs o spa ial awa eness.
5.6.5 Technology laye s equi emen s
The equi emen s o he di e en laye s a e as ollowing:
Sensing and pe cep ion laye s
• Mo ion senso s: T ack ope a o body pos u e and hand mo emen s o na u al
in e ac ion.
• Dep h senso s: Enable accu a e 3D mapping o con eyo en i onmen and was e i ems.
• P oximi y senso s: Ensu e sa e dis ances be ween humans and cobo s.
• RGB came as: P o ide isual eeds o compu e ision pipelines.
• X- ay came as: Suppo ma e ial composi ion analysis (e.g., de ec ing nails o me als in
wood pieces).
• Dep h came as: Allow eal- ime 3D de ec ion and localiza ion o i egula was e objec s.
• Wea ables: XR HMDs, hap ic glo es, and biome ic moni o s o ope a o eedback.
Ac ua ion laye
• Cobo s: Collabo a i e obo ic a ms wi h complian o ce senso s o sa e objec
manipula ion.
• Al e na i es: Comp essed ai nozzles o del a obo s
P ocessing and compu ing laye s
• Edge compu ing nodes: Localized p ocessing o minimize la ency in isual eeds and AI
in e ence.
• High-speed compu e ision pipelines: Requi e <50ms la ency o objec de ec ion and
acking (YOLO 8 o simila models).
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• AI model hos ing: Suppo o eal- ime in e ence, e aining, and con inuous da ase
upda es.
Communica ion and ne wo king laye
• Low-la ency connec i i y: 5G o indus ial E he ne ensu ing <10ms communica ion delay
be ween XR de ices, cobo s, and edge nodes.
• P o ocols: OPC UA, ROS2 middlewa e o in e ope abili y and secu e da a exchange.
• Cloud in eg a ion: Fo long- e m da a s o age, digi al win synch oniza ion, and
pe o mance analy ics.
XR and eedback Laye
• XR de ices: P o ide ac ile cues o objec classi ica ion o e o ale s.
• Augmen ed o e lays: Real- ime isualiza ion o so ing pa hs, cobo ajec o ies, and
p og ess indica o s.
AI o nea - eal- ime in e ence models like YOLO o objec de ec ion o was e i ems o
dimensionali y educ ion o hype spec al da a o ma e ial ecogni ion; in eg a. Machine
lea ning algo i hms adap o a ying was e composi ions, imp o ing accu acy o e ime
h ough supe ised aining on labelled da ase s.
The sys em le e ages ROS2 o cobo con ol and coo dina ion, o e ing enhanced eal- ime
capabili ies, imp o ed secu i y, and be e suppo o dis ibu ed sys ems in human- obo
collabo a ion. Fo XR isualiza ion, Uni y o Un ealEngine p o ide obus ende ing o imme si e
in e ac ions in i ual en i onmen s.
NVIDIA Omni e se enables ad anced 3D simula ion and collabo a i e wo k lows using USD o
digi al wins and physics-based obo ics es ing.
5.6.6 Ho izon al issues and cha ac e is ics
The use case inco po a es anspa en AI decision logs and ail-sa e mechanisms ha allow
humans o o e ide o cobo ac ions. Regula audi s and ce i ica ion agains indus ial
s anda ds like ISO 10218 o collabo a i e obo s ensu e eliabili y and sa e y. Use aining
p og ams emphasize sys em p edic abili y, educing app ehension and os e ing accep ance
in HRC wo k lows.
E hical conside a ions include p o ec ing wo ke p i acy h ough GDPR-complian da a
p ocessing and ensu ing equi able job dis ibu ion wi hou displacing human oles. Human igh s
a e upheld by designing inclusi e in e aces accessible o di e se abili ies and p e en ing o e -
eliance on au oma ion ha could lead o skill e osion. Bias mi iga ion in AI models a oids
disp opo iona e ask alloca ion o human so e s ha a e pe o ming well.
The applica ion p omo es sus ainabili y by op imizing was e so ing o inc ease ecycling a es
and educing land ill con ibu ions om cons uc ion deb is. Ene gy-e icien edge compu ing
and low-powe XR de ices minimize en i onmen al oo p in . Long- e m bene i s include
esou ce conse a ion h ough be e ma e ial eco e y, aligning wi h ci cula economy
p inciples in manu ac u ing.
Adop ion o open s anda ds in he con ex o Digi al Twins enhances scalabili y and
collabo a ion in mul i- endo ecosys ems. S anda disa ion ollowing he ISO23247 e e ence
a chi ec u es o digi al wins in manu ac u ing and ele an componen implemen a ion om
he Eclipse o FIWARE ecosys em and p o ocols like MQTT o OPC-UA o seamless in eg a ion
be ween cobo s, senso s, and XR sys ems ac oss endo s ensu es in e ope abili y and
compa ibili y wi h exis ing con eyo in as uc u es.
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6 Au omo i e
The au omo i e indus y has been an ea ly adop e o imme si e echnologies, le e aging hem
ac oss he en i e ehicle li ecycle. In he design phase, VR allows enginee s and designe s om
a ound he wo ld o collabo a e in sha ed i ual spaces, in e ac ing wi h ull-scale digi al
p o o ypes o ehicles long be o e physical models a e buil . This accele a es he design p ocess,
educes e o s, and sa es cos s associa ed wi h physical p o o yping. Fo manu ac u ing, AR
p o ides echnicians on he assembly line wi h in e ac i e, s ep-by-s ep ins uc ions o e laid
di ec ly on o hei ield o iew, imp o ing accu acy and educing assembly ime. These sys ems
can also be used o quali y assu ance, au oma ically highligh ing de ec s o de ia ions om
he design speci ica ion. In sales and ma ke ing, imme si e show ooms allow cus ome s o
explo e and cus omise ehicles in a highly ealis ic i ual en i onmen .
6.1 Me a-Fac o y Model o Elec i ied B aking Sys em
6.1.1 Scena io
The METABRAKE applica ion is si ua ed wi hin he au omo i e sec o , speci ically ocusing on he
collabo a i e inno a ion o ad anced au omo i e p oduc s and hei associa ed
manu ac u ing p ocesses. Led by B embo S.p.A., he p ojec aims o de elop a ully elec i ied
b aking sys em ha is syne gis ically in eg a ed in o he mode n ecosys em o elec ic, digi al,
and connec ed ca s.
Figu e 6-1 – METABRAKE use case.
Figu e 6-2 – Imme si e “Me a-Fac o y”
To achie e his, METABRAKE u ilises a "me a- ac o y" model o he p e-indus ial e alua ion o
he inno a i e new p oduc and i s assembly p ocess. This me a- ac o y is a comp ehensi e and
in e ac i e i ual en i onmen ha ep esen s he en i e manu ac u ing sys em.
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Wi hin his i ual space, p oduc modi ica ions can be in oduced and analysed, allowing he
eam o assess he impac on ac o y layou , p oduc ion cos s, and inal p oduc quali y be o e
any physical esou ces a e commi ed.
6.1.2 Use s and s akeholde s
The p ima y use s o he METABRAKE me a- ac o y a e he enginee s, p oduc designe s, and
p ocess planne s wi hin B embo and i s pa ne o ganisa ions. They will in e ac wi h he i ual
en i onmen o es hypo heses, simula e changes, and collabo a i ely e ine bo h he p oduc
and he p oduc ion line.
The s akeholde s o his p ojec ex end beyond he immedia e de elopmen eam. The
collabo a i e na u e o he pla o m is designed o in eg a e downs eam supply chain ac o s
and e en cus ome s di ec ly in o he inno a ion p ocess. This allows o a mo e holis ic app oach
o p oduc de elopmen , whe e eedback om supplie s and end-use s can be inco po a ed
and e alua ed ea ly in he design cycle.
6.1.3 Implemen a ion in a i ual wo ld and added alues
The co e o his applica ion is implemen ed as a Me a e se, a pe sis en , sha ed i ual space
whe e use s can in e ac wi h a digi al win o he ac o y. This me a- ac o y is no jus a s a ic
model bu a dynamic simula ion en i onmen whe e p ocesses can be un, da a can be
gene a ed, and changes can be es ed in eal ime.
The added alue o his app oach is mul i ace ed. I signi ican ly enhances bo h economic and
en i onmen al sus ainabili y by allowing o ex ensi e es ing and op imiza ion in he p e-
indus ial phase, educing was e and cos ly physical p o o ypes. I os e s a new pa adigm o
collabo a i e inno a ion by b eaking down silos be ween he manu ac u e , i s supply chain,
and i s cus ome s. Fu he mo e, by simula ing p ocesses in de ail, he sys em will be used o
e alua e and op imise he e gonomics o wo ks a ions, imp o ing human well-being on he
u u e p oduc ion line.
6.1.4 Requi ed imme si e echnologies unc ionali ies
The sys em's unc ionali y is buil upon a sophis ica ed s ack o indus ial simula ion and i ual
eali y echnologies. Use in e ac ion wi hin he Me a e se is p ima ily h ough VR con olle s and
hand acking, allowing o in ui i e manipula ion o he 3D elemen s ha cons i u e he i ual
ac o y. Feedback is p o ided o he use h ough bo h audio and isual cues o c ea e a
esponsi e expe ience.
The unde lying echnology laye s a e designed o indus ial-g ade simula ion. The ield-le el IoT
de ices and ac ua o s a e no physically p esen bu a e modelled using i ual commissioning
solu ions. A suppo ing IT in as uc u e has been designed o simula e he en i e edge laye ,
including de ices, applica ions, and managemen . This equi es signi ican CPU and GPU powe
o ensu e eal- ime ende ing and low-la ency in e ac ions du ing complex simula ions. A low-
la ency ne wo k is also c ucial o p o iding eal- ime eedback om hese demanding
simula ions.
The pla o m manages complex da a ypes, including 3D da a, li e-cycle objec da a, and he
digi al win o he assembly line, comple e wi h i ual senso s. All da a s o age and backup a e
handled on-p emises. The echnology pla o m is a composi e o se e al indus ial and gaming
solu ions, including he Siemens simula ion sui e (NX, Plan Simula ion, PLCSimAd anced), he
Uni y Real-Time De elopmen Pla o m o he i ual en i onmen , SIMATIC WinCC o he use
dashboa d, and Uni y Relay o collabo a ion. A i icial In elligence is no a p ima y ocus o he
cu en p ojec phase.
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The ype o imme si e echnology used is Me a e se. The use in e ac ion modes a e VR
con olle s, hand acking wi h use in e ace based on 3D Elemen s and use eedback
mechanisms elying on audio and isual eedback.
6.1.5 Technology laye s equi emen s
The ield-le el laye is modelled wi h i ual commissioning solu ions, and a suppo IT
in as uc u e has been designed and will be implemen ed o simula e edge de ices, edge
apps, and he edge managemen laye .
The edge compu ing p ocessing uses CPU/GPU speci ica ions o ensu e eal- ime ende ing and
low-la ency in e ac ions.
The high-speed ne wo ks and in elligen connec i i y in as uc u e equi es low-la ency o
ensu e eal- ime eedback. This is pa icula ly challenging due o he complex simula ions
in ol ed. The da a ypes and s o age mus conside 3D da a, li e-cycle objec da a, DT o he
assembly line, i ual senso s and s o age and backup on-p emises.
The AI is no in he ocus o he use case. Indus ial pla o ms o simula ion and digi al
ep esen a ion a e used (e.g., Siemens pla o m: NX mecha onic concep designe ,
Plan Simula ion S anda d, PLCSimAd anced) and i ual en i onmen pla o m (e.g., Uni y Real-
Time De elopmen Pla o m | 3D, 2D, VR & AR Engine), use dashboa d in he me a e se (e.g.,
SIMATIC WinCC Uni ied) and collabo a ion pla o ms (e.g., Uni y Relay).
6.1.6 Ho izon al issues and cha ac e is ics
T us wo hiness is a key conside a ion, and he sys em is designed o gua an ee use p i acy and
compliance wi h all ele an sec o egula ions. The eliabili y and obus ness o he pla o m a e
c i ical, as is he secu i y o he sensi i e p oduc and p ocess da a i con ains. F om an e hical
s andpoin , while he e a e no majo implica ions, he p ojec ac i ely con ibu es o human
well-being by using he simula ions o imp o e he e gonomics o u u e wo ks a ions.
The sys em is designed o be highly in e ac i e, enabling use s o dynamically manipula e he
i ual en i onmen and simula e he impac o hei changes in eal ime. Human inpu s a e
cap u ed ia VR con olle s and hand acking, and use s can also upload new design da a o
e alua e i s e ec s. The imme si eness o he sys em is ensu ed by he de ailed IT in as uc u e
and i ual senso s ha c ea e a comple e and esponsi e digi al eplica. This app oach
inhe en ly suppo s sus ainabili y and equi es a s ong commi men o s anda disa ion and
in e ope abili y o acili a e he seamless in eg a ion o ex e nal pa ne s.
Use p i acy and compliance wi h sec o egula ions should be gua an eed. The sys em should
be eliable and obus , and he secu i y o sensi i e da a mus be ensu ed.
This applica ion does no ea u e signi ican e hical implica ions. Howe e , conce ning human
well-being, i should be no ed ha he simula ions in he i ual en i onmen will also be used o
e alua e and op imise he e gonomics o he wo king s a ions.
Inpu s h ough he VR con olle and acking o hand mo ions o in e ac wi h he i ual
en i onmen . Mo eo e , he use will be able o upload design modi ica ions (e.g., 3D da a) o
e alua e he impac in he me a- ac o y.
The sys em is highly in e ac i e o enable dynamic manipula ion and impac simula ion. IT
in as uc u e and i ual senso s ha e been implemen ed o ensu e he sys em's imme si eness
and comple eness.
Me a- ac o ies can enhance he economic and en i onmen al sus ainabili y o he p e-
indus ial phase. S anda d and in e ope abili y should be gua an eed o allow in eg a ion
wi h downs eam supply chain ac o s and cus ome s.

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7 Ene gy
The ene gy sec o co e s all he s akeholde s in i s en i e alue chain such as he Ene gy
o ganisa ions (Oil& Gas p oduc ion uni s, Re ine ies, The mal Powe Plan s, Nuclea Powe Plan s,
Wind Powe Fa ms, Sola Powe Plan s, Powe T ansmission & Dis ibu ion Sys ems e c), Ene gy
Technology / P ocess Licenso s, Enginee ing o ganisa ions, EPC o ganisa ions, Manu ac u e s
and Fab ica o s, Cons uc ion companies, Mining Companies ( o Coal) as well as se e al
se ice p o ide s co e ing HSE, logis ics, inspec ion and o he se ices [39].
The ene gy sec o , om adi ional oil and gas o enewable sou ces, is le e aging imme si e
applica ions o imp o e sa e y and ope a ional e iciency. VR aining modules a e used o
simula e high-s akes eme gency p ocedu es, such as esponding o a blowou on an o sho e
oil ig o pe o ming main enance on a wind u bine, in a comple ely sa e en i onmen . AR and
MR a e u ilised o asse managemen and main enance on complex si es, such as powe plan s
o subs a ions. A echnician wea ing an MR headse can see eal- ime ope a ional da a,
his o ical main enance eco ds, and schema ics o e laid di ec ly on o he physical equipmen
hey a e se icing. This imp o es accu acy, educes he chance o human e o , and enhances
wo ke sa e y by p o iding c i ical in o ma ion a he poin o need.
VR can be applied in he ene gy sec o o op imise enewable ene gy sys ems, enhancing
ene gy e iciency, and acili a ing wo k o ce aining. One o he p ima y a eas in which VR is
u ilised in sus ainable ene gy is in he design and es ing o enewable ene gy sou ces. Vi ual
en i onmen s enable he simula ion o sola panels, wind a ms, and ene gy s o age sys ems
unde a ious en i onmen al condi ions, allowing enginee s o analyse pe o mance, iden i y
po en ial ine iciencies, and op imise layou s [49].
7.1 Imme si e VR o Ope a ion o Wind Tu bines
7.1.1 Scena io
The aining applica ion has he scope o p o iding an easy o anspo , sa e, and epea able
VR-based aining solu ion o ope a o s in he ields. I helps s a membe s o lea n o imp o e
p ocedu al skills and amilia ise hem wi h he wo k en i onmen and ools. Fo ins ance, p ocess
aining s eps on how o assemble pa s o he wind u bine, how o iden i y a eas whe e objec s
like sc ews a e placed and how o ope a e a elescopic c ane.
Figu e 7-1 – IVR aining solu ion in Siemens-Gamesa
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Figu e 7-2 – IVR aining solu ion
7.1.2 Use and s akeholde s
Main use s a e unexpe ienced s a membe s o companies ha ha e o deploy in - he- iled
some equipmen (e.g., a wind u bine).
7.1.3 Implemen a ion in a i ual wo ld and added alues
The added alue is o educe he cos o aining and p o ide an in-si u capabili y o be e
add essing cons uc ion p oblems hanks o he use o VR.
7.1.4 Requi ed imme si e echnologies unc ionali ies
The ype o imme si e echnology used is VR wi h VR con olle s as use in e ac ion modes and
a use in e ace design buil a ound 3D elemen s, gami ica ion and mul i-use in e ac i i y.
The use eedback mechanisms a e hap ic eedback is used in he simula o o gi e ib a ions
when ainees do some hing w ong, e.g., when hey walk in o i ual walls. No hap ics when
in e ac ing wi h objec s.
7.1.5 Technology laye s equi emen s
The HTC Vi e is used as pla o m o he cu en ly deployed VR aining. Fo mos pa s o he
aining, he in e ac ions in ol e wo hands (using he VR con olle s). The wo kspace is oom-
scale, as he ainees mus kneel, walk a ound and u n.
7.1.6 Ho izon al issues and cha ac e is ics
No speci ic ho izon al issues de ined.
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8 Buildings
The A chi ec u e, Enginee ing, and Cons uc ion (AEC) sec o uses imme si e echnology o
e olu ionise how buildings a e designed, cons uc ed, and managed. A chi ec s u ilise VR o
c ea e imme si e walk h oughs o hei designs, enabling clien s o expe ience a space and
p o ide eedback well be o e cons uc ion begins. This helps o align expec a ions and educe
cos ly la e-s age design changes. On he cons uc ion si e, AR can be used o o e lay Building
In o ma ion Models (BIM) on o he physical en i onmen , allowing wo ke s o e i y ha
cons uc ion is p oceeding acco ding o plan and o iden i y clashes be ween di e en sys ems
(e.g., plumbing and elec ical) be o e hey become p oblems [46]. Pos -cons uc ion, digi al
wins o buildings, accessed ia imme si e in e aces, a e used o acili ies managemen , space
planning, and op imising ene gy consump ion.
Cons uc ion design began wi h he d a ing boa d, mo ed h ough he age o Compu e -
Aided Design (CAD), and now u ilises BIM echnology, which employs uni ied da a models and
imme si e echnologies o suppo designing 3D s uc u es in 2D space. As pa o he ongoing
e olu ion o he cons uc ion indus y, imme si e echnologies such as AR ha e a signi ican
impac on he AEC sec o , isualisa ions, in o ma ion e ie al, and in e ac ion [47].
BIM and VR can be in eg a ed o enhance indoo ligh ing design in cons uc ion by le e aging
gaming engine echnologies, he app oach allows o he c ea ion o 3D en i onmen s o
expe imen wi h a ious ligh ing designs, add essing he c i ical ole o ligh ing in bo h aes he ics
and unc ionali y [48]. The au ho s in [48] p o ide an o e iew and se e al examples o he
adop ion o i ual and augmen ed eali y in he a chi ec u e, enginee ing, cons uc ion, and
acili ies managemen .
8.1 Vi ual A chi ec u al Design
8.1.1 Scena io
This imme si e applica ion allows ci y planne s and a chi ec s o isualize and in e ac wi h
digi al wins o u ban en i onmen s. Th ough his pla o m, use s can simula e a a ie y o
scena ios, om in as uc u e changes o he implemen a ion o g een spaces, and assess hei
impac on u ban dynamics. By in eg a ing eal- ime da a, use s can explo e how a ic pa e ns
shi o how new de elopmen s a ec pedes ian low. The applica ion also includes p edic i e
analy ics o e alua e en i onmen al impac s, such as ai quali y imp o emen s o po en ial noise
pollu ion changes.
Figu e 8-1 – Vi ual A chi ec u al Design
8.1.2 Use and s akeholde s
Using imme si e echnology, s akeholde s can collabo a e in a i ual space, b inging oge he
a chi ec s, enginee s, and ci y o icials o make in o med decisions.
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8.1.3 Implemen a ion in a i ual wo ld and added alues
N/A.
8.1.4 Requi ed imme si e echnologies unc ionali ies
The ype o imme si e echnologies used a e Me a e se and XR (AR/VR) en i onmen s wi h use
in e ac ion modes based on hand acking, oice commands, VR con olle s, eye acking.
The use in e ace design combines 3D elemen s o ealis ic ci y models and in e ac i e design
laye s wi h 2D elemen s o HUDs and da a display, p o iding in ui i e na iga ion h ough i ual
u ban spaces.
The use eedback mechanisms Inco po a e hap ic eedback o ac ile in e ac ions, Audio
eedback o guided explo a ion and ale s, and isual eedback o eal- ime changes and
no i ica ions.
8.1.5 Technology laye s equi emen s
In eg a es wi h IoT o eal- ime da a collec ion and en i onmen al moni o ing, enhancing HMI
h ough seamless in e ac ions wi h i ual ep esen a ions o physical spaces.
U ilises mo ion senso s, dep h senso s, came as o cap u ing en i onmen al da a, and hap ic
de ices o ealis ic eedback. En i onmen al ac ua o s simula e elemen s like empe a u e o
wind, enhancing ealism.
Requi es high-pe o mance CPU/GPU capabili ies o eal- ime ende ing and low-la ency
in e ac ions. Combines dis ibu ed compu ing pa adigms o manage compu a ional loads
be ween edge and cloud-based esou ces e ec i ely.
Employs obus communica ion p o ocols o ensu e low-la ency, eal- ime da a ans e and
p ocessing, essen ial o synch onising i ual and eal-wo ld da a s eams.
Manages la ge olumes o audio/ ideo, 3D da a ypes, and spa ial- empo al da a. U ilises
hyb id local/cloud s o age solu ions o capaci y scalabili y, wi h backup/ eco e y and da a li e
cycle managemen p o ocols ensu ing da a in eg i y.
AI le e ages compu e ision o spa ial analysis, na u al language p ocessing o oice
commands, and machine lea ning o p edic i e analy ics. AI algo i hms enhance use
in e ac ions and isualise complex da a, in o med by obus aining da ase s.
The pla o ms used a e compa ible wi h Oculus Ri , HTC Vi e, Mic oso HoloLens, suppo ing
c oss-pla o m in e ope abili y h ough comp ehensi e SDKs and a ge ed OS op imisa ions.
8.1.6 Ho izon al issues and cha ac e is ics
T us wo hiness ensu es use da a anonymisa ion and compliance wi h u ban planning
s anda ds and egula ions. The sys em is designed o eliabili y, aul ole ance, and secu i y,
sa egua ding sensi i e de elopmen al da a and use in e ac ions.
The use case p io i ises p i acy, ensu ing ha simula ed scena ios do no in inge on indi idual
igh s. Emphasizes e hical AI deploymen by elimina ing biases in planning simula ions and
p omo ing inclusi e u ban designs.
The use case acili a es sus ainable u ban planning by allowing use s o isualize and e alua e
he en i onmen al impac o a ious design choices, p omo ing eco- iendly u ban
de elopmen p ac ices.
The use case adhe es o indus y s anda ds o da a o ma s and communica ion p o ocols o
ensu e in e ope abili y wi h o he u ban planning ools, acili a ing easie in eg a ion in o
b oade ci y de elopmen ini ia i es.
This applica ion le e ages imme si e echnologies o ans o m adi ional u ban planning in o
an in e ac i e, da a-d i en p ocess, enabling mo e in o med, inclusi e, and sus ainable ci y
de elopmen .
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The pla o ms used o XR isualiza ion a e Uni y o Un eal Engine p o ide obus ende ing o
imme si e in e ac ions in i ual en i onmen s. VR HMD de ices will be used by he VR clien o
con ol he VR cybe secu i y assessmen .
An AR able (iPad) could be used o he clien o con ol and ack any AR lea ning and
assessmen elemen s o he applica ion.
Any suppo ing e-lea ning con en could be linked o a WebView edi o and a Moodle API
in e ace o any aining cou se con en .
Suppo ing ools such as shapes XR, Re i , win Mo ion, blende , Uni y would be le e aged o
comple e he digi al win c ea ion o he hospi al en i onmen and simula ed expe iences.
10.1.6 Ho izon al issues and cha ac e is ics
Such aining p og ams p o ide a heal hca e sys em ha p omo es pa ien sa e y as co e o i s
o ganisa ion, is anspa en and is p oac i e in i s cybe secu i y measu es.
Cybe secu i y h ea s in a hospi al en i onmen aise majo e hical conce ns and challenges as
hey can impac he pa ien ’s sa e y and p i acy.
The e is an obliga ion o heal hca e wo ke s and p o essionals o p o ec pa ien s’ con iden iali y
and no expose sensi i e da a. Such imme si e aining scena ios wi h such de ined
cybe secu i y aining scena ios help ain such p o essionals and build public us in heal hca e
sys ems and hei du y o p epa edness.
The applica ion p omo es sus ainabili y by educa ing he indi idual o be mo e knowledgeable
abou po en ial cybe secu i y h ea s and how o ac i ely a oid. This in u n ensu es he hospi al
ope a es sus ainably, main ain high quali y ca e wi hou being hinde ed by cybe h ea s.
The use case uses consis en p o ocols and bes p ac ice. S eamlines isk assessmen and aligns
wi h egula o y equi emen s and s anda ds.
Wi h ega d in e ope abili y i s builds us be ween sys ems and p o ide s and allows o mo e
secu e da a exchange.

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11 Ag icul u e
Imme si e echnologies a e b inging p ecision ag icul u e o a new le el. Fa me s in he ield can
use AR applica ions o isualise da a om d ones and senso s di ec ly o e hei c ops. This can
include in o ma ion on soil mois u e, nu ien le els, o he p esence o pes s and diseases,
allowing o highly a ge ed in e en ions. This da a-d i en app oach, o en connec ed o a
digi al win o he a m, helps o op imise esou ce use, such as wa e and e ilise , and inc ease
c op yields sus ainably [50]. VR is also being used o aining ag icul u al wo ke s on he
ope a ion o sophis ica ed a ming equipmen and o simula ing he po en ial impac o
di e en a ming s a egies o clima e change scena ios.
Using DT as a cen al means o a ming and con ol can o e signi ican ad an ages as DTs
emo e limi a ions conce ning place, ime, and human obse a ion, and ag icul u e would no
longe equi e physical p oximi y, which enables emo e and au oma ed execu ion, moni o ing,
con ol, and coo dina ion o a m ope a ions, allowing o he decoupling o physical lows om
in o ma ion aspec s o a m p ocesses. DT can also be en iched wi h in o ma ion ha canno
be obse ed by he human senses (e.g. senso and sa elli e da a) o da a ha o he in o ma ion
owne s p o ide. DT can add in elligence using ad anced analy ics ha no jus ep esen ac ual
s a es, bu can also analyse his o ical s a es and simula e u u e beha iou , which enables
a me s and o he s akeholde s o ac immedia ely in case o (expec ed) de ia ions and b ing
sma a ming o new le els o a ming p oduc i i y and sus ainabili y [50].
11.1 XR-based Ag icul u al Digi al Twin
11.1.1 Scena io
The use case p oposes a pla o m hos ing digi al wins o ag icul u al a ms, which u ilises
ex ended eali y o bo h da a collec ion and isualisa ion. The applica ion domain is an XR-
based Digi al Twin o a a m, con aining mul iple da a laye s ed by an Augmen ed Reali y
applica ion and made isible h ough a Vi ual Reali y in e ace.
Figu e 11-1 - Imme si e XR-based Ag icul u al Digi al Twin Concep
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Figu e 11-2 - Imme si e XR-based Ag icul u al Digi al Twin
By combining he eal-wo ld a m's eal- ime IoT da a wi h he abili y o e iew, analyse, and
audi a m p ocesses, we enable he a me o he u u e o op imise hei ope a ions. This allows
use s o moni o c op g ow h, ensu e animal wel a e, check soil heal h, and main ain
compliance wi h heal h and sa e y equi emen s on he a m. Augmen ed Reali y is used o
cap u e li e da a and map a m loca ions, while a Vi ual Reali y headse is ha nessed o
isualise he a ious da a laye s o he a m.
11.1.2 Use s and s akeholde s
The p ima y use s o his applica ion a e a me s and a m manage s who seek o mode nise and
op imise hei ag icul u al p ocesses. They will use he sys em o day- o-day moni o ing, long-
e m planning, and ensu ing ope a ional sa e y and e iciency.
Seconda y s akeholde s include ag icul u al consul an s, compliance audi o s, and echnology
p o ide s. Consul an s can use he win o p o ide da a-d i en ad ice, while audi o s can
emo ely e i y adhe ence o sa e y and en i onmen al egula ions. Technology p o ide s, such
as digi al win pla o m hos s and senso manu ac u e s, a e also key s akeholde s in he
de elopmen and main enance o he ecosys em.
11.1.3 Implemen a ion in a i ual wo ld and added alues
The implemen a ion o his use case e ol es a ound ex ended eali y (XR) echnology, which
encompasses bo h Augmen ed Reali y o da a collec ion and Vi ual Reali y o da a
isualisa ion and in e ac ion. The AR componen , likely on a mobile de ice, cap u es eal-wo ld
da a and scans en i onmen s, while he VR componen p o ides an imme si e, scaled
ep esen a ion o he en i e a m.
The added alue is signi ican , p o iding a comp ehensi e ool o mode n a m managemen .
I mo es beyond simple da a collec ion o o e ac ionable insigh s h ough in ui i e isualisa ion.
This enhances decision-making o c op o a ion, esou ce alloca ion, and animal he d
managemen . I also p o ides a obus amewo k o ensu ing and documen ing compliance
wi h heal h, sa e y, and en i onmen al s anda ds.
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11.1.4 Requi ed imme si e echnologies unc ionali ies
The sys em's unc ionali y is buil upon se e al laye s o ad anced echnology. The co e
imme si e echnology is XR, le e aging he unique s eng hs o bo h AR and VR. Use in e ac ion
is designed o be na u al and in ui i e, p ima ily using hand acking o in e ac wi h he use
in e ace. This is augmen ed by eye acking and oice commands o highligh and engage
wi h he a ious laye s o he digi al win, allowing o p ecise in e ac ion wi h speci ic p ocesses
and i ual a m machine y.
The use in e ace design hough ully in eg a es bo h 2D and 3D elemen s. The AR applica ion
u ilises a 2D UI o e icien da a display and cap u e in he ield. Wi hin he VR headse , a ully
3D UI allows use s o di ec ly manipula e he a m model wi h hei hands. Na iga ion h ough
he AR expe ience is simply he use 's mo emen ac oss he physical a m, whe eas he cap u ed
win in VR can be na iga ed and scaled in ui i ely. To enhance he sense o p esence and
con ol, he sys em elies on p ecise audio and isual eedback as a esponse o use
in e ac ions, compensa ing o he lack o physical hap ic de ices.
The echnology laye s equi ed o suppo his a e ex ensi e. Fo Human-Machine In e ac ion
and IoT, he win in eg a es da a om a ious senso s. This includes low-ene gy p oximi y senso s
and ad anced mmWa e ada o analysing oo all, occupancy, leak de ec ion, and e en
human o animal gai ecogni ion o wel a e and sa e y. Hype spec al came as moun ed
h oughou he a m will moni o soil heal h and c op g ow h.
To p ocess his as amoun o da a, he sys em will u ilise a dis ibu ed compu ing pa adigm,
elying on a digi al win solu ion p o ide like Ben ley Sys ems. This combines high-pe o mance
cloud compu ing wi h edge compu ing o eal- ime p ocessing. To ensu e seamless da a
ans e ac oss he la ge landmass o a a m, a high-speed 5G ne wo k is p oposed. This
in elligen connec i i y is c ucial o p o iding he low-la ency, eal- ime da a eeds necessa y
o sa e y and ope a ional moni o ing.
Da a managemen in ol es handling complex ypes, including 3D spa ial da a, which will be
s o ed in he highly in e ope able GLTF o ma . A i icial In elligence is essen ial, wi h algo i hms
like Gaussian spla ing used o c ea e pho o ealis ic eplica ions o in e io a m en i onmen s
om scans. The a ge pla o ms a e chosen o hei obus ea u es and ease o de elopmen ;
he Me a Ques 3 o he VR expe ience and an iPhone wi h LiDAR capabili ies using ARKi o
he AR da a cap u e componen .
The use case u ilises as imme si e echnologies XR which encompasses AR o da a collec ion
and cap u e and VR o da a isualisa ion.
The use in e ac ion modes use o hand acking o in e ac wi h he UI whils u ilising eye acking
and oice commands o highligh and in e ac wi h he a ying laye s o he digi al win and
p ecisely pinpoin speci ic p ocesses and a m machine y.
The use in e ace design uses o bo h 2DUI in AR applica ions and 3DUI in VR headse s o con ey
he in o ma ion equi ed e icien ly. Na iga ion h oughou AR expe ience is he landmass o he
winned a m whils he cap u ed win in VR can be modi ied manipula ing he model wi h he
use ’s hands using he 3DUI.
The use eedback mechanisms conside ed a e hand acking in he VR isualisa ion o c ea e
a mo e na u alis ic inpu mechanism. As such we ely on p ecisely designed in e ac ion me hods
o p o ide audio & isual eedback o he use as a means o hap ic eedback and enhance
sensa ion wi hin he applica ion.
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11.1.5 Technology laye s equi emen s
As a digi al win o an ag icul u al a m, he use case sepa a es he win in o ep esen a i e
laye s o show case di e en aspec s o he winned a m.
To analyse oo all and occupancy he use case in eg a es bo h low ene gy ex e nal solu ions
o p oximi y sensing as well as newe echnologies such as mmWa e ada o leak de ec ion,
s o age ank inspec ion and human/animal gai ecogni ion.
The use case plans o include soil moni o ing and c op g ow h senso s using hype spec al
image analysis h ough moun ed came as h ough he a m a ea.
To deploy such a sys em, a digi al win solu ion p o ide such as Ben ley, who p o ide he
esou ces equi ed o deploy wins o his scale will be used.
To acili a e da a ans e h oughou he ag icul u al en i onmen is p oposed o use o 5G
ne wo king. This allows connec i i y ac oss he la ge landmass o he winned a ea. Since he
isualisa ion o he win o heal h and sa e y would equi e eal ime da a low la ency is a high
p io i y.
The model ga he ed om he scanning using AR would be s o ed as GLTF as i a highly
in e ope able exchange o ma .
The use o Ai algo i hms ac oss he digi al win a e essen ial o i s da a cap u e and isualisa ion
needs. Gaussian spla ing would be used o c ea e eplica ion o in e io en i onmen s.
Fo ease o use and in e ope abili y o componen s he Me a Ques 3 p o ides a s able so wa e
en i onmen and easy o in eg a ion SDKs o de elop he VR pla o m. Fo he AR componen
o his applica ion he use o an iPhone wi h LiDAR capabili ies and ARKi SDK would se e as a
powe ul AR isualisa ion de ice. In addi ion is plan he de elopmen o he win using Ben ley’s
digi al win pla o ms.
11.1.6 Ho izon al issues and cha ac e is ics
T us wo hiness is pa amoun , especially as he pla o m ga he s in o ma ion on human ac i i y.
S ic adhe ence o GDPR will be equi ed, wi h use da a anonymised and associa ed only wi h
a unique ID. The sys em will also accoun o ISO s anda ds ega ding AR/VR usage sa e y and
da a secu i y.
F om an e hical pe spec i e, da a anonymisa ion is c i ical o p ese ing he p i acy o any
human pa icipan , upholding hei igh o da a emo al. While senso da a is used o ensu e
a m sa e y, i is managed in a way ha p o ec s he p i acy o indi iduals. The sys em is
designed o be highly imme si e and in e ac i e, allowing use s o explo e he a m's da a laye s
a a eal-wo ld scale.
Fo sus ainabili y, he sys em is designed o be ex ensible, allowing o he easy inclusion o
addi ional senso s o he upda ing o scanned en i onmen s h ough he AR applica ion. Finally,
o s anda disa ion and in e ope abili y, he pla o m will adhe e o he ISO/IEC 30173:2023
s anda d o digi al wins, ensu ing i can in eg a e wi h o he sys ems and echnologies in he
u u e.
As he pla o m would ga he in o ma ion on human ac i i y a ound he a m s ic adhe ence
GDPR would be equi ed. Use s would be associa ed only ia a UUID con olled by an IDM.
Adhe ence o ISO s anda ds on bo h use o AR/VR usage sa e y& da a secu i y would need o
be accoun ed o .
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The inpu s he sys em pe cei es om humans equi es da a anonymisa ion o p ese e he
p i acy o any human pa icipan as well as upholding hei igh o da a emo al. The gai
analysis/p esence would ensu e a m ga he ed om he senso s would enable a m sa e y whils
ensu ing he p i acy o he humans p esen on he a m.
The use o he sys em in e ac s h ough XR. Bo h in AR by cap u ing da a and eco ding
loca ions and h ough VR as win isualisa ion mechanism.
The pla o m is highly imme si e allowing he use o na iga e and explo e he da a laye s o he
digi al win in eal wo ld scale.
The sys em is ex ensible ia he inclusion o addi ional senso s o upda ing o addi ional scanned
componen s h ough he AR applica ion.
The pla o m would seek o adhe e o ISO/IEC 30173:2023 s anda d o digi al wins.
11.2 Imme si e Ag icul u al Digi al Twin
11.2.1 Scena io
The applica ion p oposes a XR-based DT o an ag icul u al a m con aining mul iple da a laye s
ed by AR applica ions and isible h ough a VR in e ace. XR is used o bo h da a collec ion
and isualisa ion and combines he eal wo ld o a m eal- ime IoT de ices wi h he abili y o
e iew, analyse and audi a m p ocesses, hus op imizing he managemen o all p ocesses and
aspec s o a a m. Fo ins ance, o moni o c op g ow h, ensu e animal wel a e, check soil heal h
and allow o compliance wi h heal h & sa e y equi emen s on he a m. AR cap u es li e da a
and map a m loca ions, whils he VR headse is ha nessed o isualise a m da a laye s.
Figu e 11-3 – XR based Ag icul u al Digi al Twin

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11.2.2 Use and s akeholde s
The main use s o he applica ions a e a me s ha wan o ha e a digi ized and mo e e icien
managemen o hei p ope y.
11.2.3 Implemen a ion in a i ual wo ld and added alues
No speci ic implemen a ion in a i ual wo ld.
11.2.4 Requi ed imme si e echnologies unc ionali ies
The applica ion u ilises imme si e echnologies like XR, which encompasses AR o da a
collec ion and cap u e and VR o da a isualisa ion.
The use in e ac ion modes comp ise o hand acking is used o in e ac wi h he UI whils u ilising
eye acking and oice commands o highligh and in e ac wi h he a ying laye s o he DT
and p ecisely pinpoin speci ic p ocesses and a m machine y.
The applica ion uses 2DUI in AR and 3DUI in VR headse s o e icien ly con ey he equi ed
in o ma ion. Na iga ion h oughou AR expe ience is he landmass o he winned a m whils
he cap u ed win in VR can be modi ied manipula ing he model wi h he use ’s hands using
he 3DUI.
The use eedback mechanisms used a e hand acking in he VR isualisa ion p ocess is
implemen ed, so o c ea e a mo e na u alis ic inpu mechanism.
P ecisely designed in e ac ion me hods can p o ide audio and isual eedback o he use as
a means o hap ic eedback and enhance sensa ion wi hin he applica ion.
11.2.5 Technology laye s equi emen s
The applica ion is highly imme si e allowing use s o na iga e and explo e he da a laye s o he
DT. As he applica ion ga he s in o ma ion on human ac i i y a ound he a m, s ic adhe ence
o GDPR is equi ed.
Rega ding he da a s o age, he model ga he ed by using AR is s o ed in he highly
in e ope able GL T ansmission Fo ma (GLTF), designed o he e icien ansmission and loading
o 3D models.
To acili a e da a ans e h oughou he ag icul u al en i onmen 5G is used, hanks o i s
capabili y o ensu ing eal ime da a exchanged a low la ency.
Fo ease o use and in e ope abili y o componen s he Me a Ques 3 p o ides a s able SW
en i onmen and easy o in eg a ion SDKs o de elop he VR pla o m.
Fo he AR componen o his applica ion he use o an iPhone wi h LiDAR capabili ies and ARKi
SDK se es as a powe ul AR isualisa ion de ice. De elopmen o he win is done using Ben ley’s
DT pla o ms.
11.2.6 Ho izon al issues and cha ac e is ics
Da a anonymisa ion would be equi ed o p ese e he p i acy o any human pa icipan as
well as upholding hei igh o da a emo al. The gai analysis/p esence would ensu e a m
ga he ed om he senso s would enable a m sa e y whils ensu ing he p i acy o he humans
p esen on he a m.
Adhe ence o he ISO/IEC 30173:2023 s anda d o DT is conside o his use case.
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11.3 Sma Ag icul u e: P ecision Fa ming
11.3.1 Scena io
The applica ion employs eX ended Reali y (XR) and Digi al Twin (DT) echnologies o enhance
sus ainable ag icul u al p ac ices. By c ea ing a digi al eplica o a a m ecosys em, i allows
eal- ime moni o ing, analysis, and decision-making.
Fa me s and ag icul u al expe s can imme se hemsel es in a 3D ep esen a ion o hei ields,
isualize c op heal h, simula e i iga ion s a egies, and p edic yields. The i ual wo ld
componen enables collabo a ion ac oss s akeholde s ( a me s, scien is s, and policymake s) in
a sha ed, i ual en i onmen o co-c ea e solu ions, es in e en ions, and ain in ad anced
a ming echniques.
Imagine you a e a a me and would like o unde s and when i would be bes o i iga e you
ields and using how much wa e . Using a DT o you ield you would be able o un scena io
compa ison and see he impac o low-, medium-, and high-i iga ion on you c op mo ing
o wa d in ime.
Figu e 11-4 – XR–DT In eg a ion o Imme si e Moni o ing and I iga ion Scena io Analysis in P ecision Ag icul u e.
11.3.2 Use and s akeholde s
The p ima y use s o his applica ion a e a me s, ag icul u al consul an s, goods and appliances
deli e y i ms in he ag icul u al wo ld.
S akeholde s a e he cus ome o a me s, a me s hemsel es, and mo e in gene al all he
ecosys em a ound a ming and p oduc ion o goods.
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11.3.3 Implemen a ion in a i ual wo ld and added alues
The applica ion is implemen ed wi hin an XR wo ld whe e di e en aspec s o a ming a e aken
ca e o and seamlessly a anged in o a uni ied 3D layou . This i ual en i onmen allows a me s
and o he s akeholde s o in e ac wi h i ual eplica o he a ming domain ( ields, ca le,
ins umen o i iga ion, machine o sp ead e ilize s, e c), os e ing eal- ime assessmen o he
s a us o c ops and ields as well as allowing o scena io playing (change wa e , e ilize le els,
e c).
Among he added alues one can lis educ ion in cos o un a ac o y, ene gy-e icien use o
esou ces, os e small a me s, scena io c ea ion o di e en echnologies/choices.
11.3.4 Requi ed imme si e echnologies unc ionali ies
The imme si e echnologies used a e AR o on- ield guidance and da a o e lay, VR o
imme si e aining and s a egy simula ion and i ual wo lds o s akeholde collabo a ion and
educa ion. The use in e ac ion modes a e using AR o sma phone/ able in e ace, AR glasses,
oice commands, and hand ges u es, VR o VR headse s, hand acking, and oice commands
and i ual wo lds o PC and VR headse s o accessing he sha ed i ual pla o m.
The use in e ace design is based on 2D dashboa ds o pe o mance me ics ha can be
in eg a ed wi h in e ac i e 3D models o ields and c op s ages. In addi ion, na iga ion ools o
explo ing he DT will be made a ailable.
The use eedback mechanisms a e isual eedback ( eal- ime analy ics and ale s), oge he
wi h audio eedback o ac ionable insigh s and hap ic eedback in aining simula ions o
equipmen handling.
11.3.5 Technology laye s equi emen s
HMI a e used o IoT de ices ha o e an in ui i e and mul ilingual in e aces o a me s. D ones,
wea he s a ions, sma i iga ion sys ems and senso s/ac ua o s ha can p o ide a se o key
measu emen s (e.g., soil mois u e). Real- ime da a p ocessing is applied o quick insigh s
h ough he implemen a ion o edge compu ing. Low-la ency connec i i y is used o emo e
ield a eas using sa elli e o 5G connec ion o gua an ee high-speed ne wo ks and an in elligen
connec i i y in as uc u e. Da a ypes and s o age conside spa ial and empo al da a, c op
heal h images, and IoT senso da a s o ed locally and on he cloud.
The AI de elopmen s include he de elopmen o p edic i e models o c op heal h and yield.
Compu e ision algo i hms o pes and disease iden i ica ion. Na u al Language P og amming
(NLP) o con e sa ional AI in na i e languages. The pla o ms used a e mobile AR/VR pla o ms
(Oculus, HTC Vi e, e c.), me a e se-compa ible ecosys ems (Decen aland, Me a Ho izon).
11.3.6 Ho izon al issues and cha ac e is ics
Da a anonymiza ion and compliance wi h ag icul u al egula ions a e used and added o a
secu e da a s o age and ans e p o ocols.
The use case ensu es accessibili y o smallholde a me s and p o ides anspa ency in AI
decision-making p ocesses.
The sus ainabili y is conside ed by he use case and e lec ed in educ ion in esou ce was age
(e.g., wa e , e ilize s) and deploymen o ene gy-e icien compu ing solu ions.
The s anda disa ion issues add ess he in eg a ion wi h global ag icul u al da abases and IoT
s anda ds.
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12 Tou ism
The ou ism indus y is le e aging imme si e ech as bo h a ma ke ing ool and a way o
enhance he isi o expe ience. VR " y-be o e-you-buy" expe iences enable po en ial ou is s o
expe ience a des ina ion, om explo ing a ho el oom o isi ing a amous landma k, be o e
booking a ip. Once a a des ina ion, AR applica ions can ac as in e ac i e ou guides,
p o iding in o ma ion abou poin s o in e es , ansla ing signs in eal- ime, and o e ing gami ied
sca enge hun s o make explo a ion mo e engaging. In he con ex o he me a e se, he e is
po en ial o a new o m o i ual ou ism, whe e use s can isi and socialise in ealis ic digi al
eplicas o des ina ions, c ea ing new e enue s eams o he indus y [44].
The u u e o indus ial imme si e applica ions will be de ined by g ea e in elligence, deepe
in eg a ion, and seamless in e ope abili y. The nex gene a ion o hese sys ems will be powe ed
by inc easingly sophis ica ed AI, enabling en i onmen s ha a e no jus in e ac i e bu a e uly
esponsi e and adap i e o use beha iou and con ex .
The concep o he "Imme si e T iple ," which links he physical asse , i s eal- ime digi al win,
and an AI-d i en p edic i e model, will become mo e p e alen , allowing o p oac i e
main enance and ope a ional op imisa ion. The ise o he IoT o Senses (IoTS) will enable iche
mul i-senso y expe iences, inco po a ing hap ics, and e en smell and as e, o c ea e a mo e
p o ound sense o p esence.
A p ima y ocus o u u e esea ch is on de eloping open, in e ope able s anda ds necessa y o
a ue indus ial me a e se, enabling seamless ans e o da a, asse s, and a a a s be ween
pla o ms. The de elopmen o he nex -gene a ion spa ial web (Web 4.0) will p o ide he
decen alised in as uc u e needed o his ision. As hese echnologies become inc easingly
in eg a ed in o c i ical indus ial p ocesses, ensu ing hei us wo hiness, secu i y, and e hical
use will be o pa amoun impo ance. The indus ial eal-digi al- i ual con inuum is s ill in i s ea ly
s ages, bu i s po en ial o enhance human capabili y, op imise complex sys ems, and d i e he
nex wa e o digi al ans o ma ion is undeniable.
12.1 In eg a ion o Digi al Twins in he Na u al Rese e Lauk ikøyene
12.1.1 Scena io
The Lauk ikøyene Na u e Rese e is in Vågan municipali y, No dland Coun y, No way. The
ese e co e s an a ea o 10,888 deca es, o which 7,372 deca es a e ma ine a eas. I was
es ablished o p ese e his aluable coas al a ea and i s associa ed plan and animal li e. The
ese e is cha ac e ized by shallow ma ine a eas, islands, ma shlands, and se e al eshwa e
ponds. Nume ous bays and co es cu in o he landscape, c ea ing a di e se and dynamic
en i onmen . Lauk ikøyene is enowned o i s ich biodi e si y, pa icula ly as a b eeding g ound
o we land bi ds and a win e ing a ea o seabi ds. In addi ion o bi dli e, he ese e is home o
he Eu opean O e (Lu a lu a), a species o conse a ion conce n. The bo anical alue o he
a ea is also signi ican , especially in he coas al zone, which hos s se e al a e and endange ed
plan species. The a ie y o habi a s, om ma ine o eshwa e ecosys ems, con ibu es o he
ecological impo ance o Lauk ikøyene. The na u e ese e se es as a c i ical e e ence poin
o moni o ing en i onmen al changes, p o iding a obus ounda ion o sus ainable
managemen and esea ch. I s ands ou as a i al loca ion o bo h conse a ion and
biodi e si y s udies, highligh ing he in e play be ween land and aqua ic ecosys ems.
The applica ion c ea es a DT o Lauk ikøyene ha in eg a es da a om a di e se senso
ne wo k. This digi al ep esen a ion p o ides eal- ime isualiza ion o he cu en s a e o he
ecosys em and suppo s decision-making, esea ch, and public engagemen .
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The use in e ace design conside s ha he IVR scena ios included a ealis ic ep esen a ion o
he machines and en i onmen , and isual cues o guide he use o pe o m he aining asks.
• Hap ics – No hap ic eedback was epo ed.
• Use Adap a ion – No adap a ion o speci ic use s was iden i ied
The e is no sys ema ic alida ion o he IVR aining. An in o mal con es - ype assessmen
be ween IVR ained and non-IVR ained employees p o ided equal pe o mance me ics,
depic ing he alidi y o IVR as a aining ool.
No hap ic eedback was epo ed and no adap a ion o speci ic use s was iden i ied.
The e is no sys ema ic alida ion o he IVR aining. An in o mal con es - ype assessmen
be ween IVR ained and non-IVR ained employees p o ided equal pe o mance me ics,
depic ing he alidi y o IVR as a aining ool.
13.2.5 Technology laye s equi emen s
In e ac ions a e h ough he Vi e con olle s using bo h hei acked posi ions and he con olle
bu ons. The ainees ge audio, isual, and ex cues guiding hem o comple e he aining
s eps.
13.2.6 Ho izon al issues and cha ac e is ics
The sys em is designed o be highly in e ac i e and imme si e. The ealis ic g aphical
ep esen a ion o he machine y and he ac o y en i onmen , combined wi h clea isual cues,
c ea es a compelling and engaging aining expe ience o he use . The IVR aining is in ac i e
use wi hin he company.
F om a human-cen ic pe spec i e, he sys em pe cei es use inpu h ough hei physical
mo emen s and in e ac ions wi h he VR con olle s. While la gely success ul, a no able side
e ec o cybe sickness was epo ed among a g oup o wo ke s who had no p e ious
expe ience wi h ideo games, highligh ing an impo an conside a ion o deploying VR o
di e se use g oups. The e a e no o he signi ican e hical implica ions no ed o his applica ion.
The sys em's p ima y con ibu ion o sus ainabili y is economic, h ough he educ ion o cos ly
machine down ime. Fo s anda disa ion, he pla o m excels by enabling he deploymen o a
uni o m aining s anda d ac oss G und os's global ope a ions.
The IVR aining is in use and s akeholde s es i y ha wi h he use o IVR aining, hey obse ed
an inc ease in he mo i a ion and dec ease o he ime equi ed o ain employees.
In addi ion, he employmen o IVR p o ided he possibili y o a) inc eased collabo a ion, as
se e al VR use s could ain oge he on he same con en ega dless o ime and space and b)
deploymen o a uni o m aining me hod and scena ios on a global scale [3]. Ne e heless,
some side e ec s like cybe sickness, in a g oup o wo ke s wi h no p e ious game expe ience,
was also epo ed.
The IVR scena ios included a ealis ic ep esen a ion o he machines and en i onmen , and
isual cues o guide he use o pe o m he aining asks.

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13.3 DSB T ain Ope a o T aining
13.3.1 Scena io
Danish S a e Railways (DSB) aces an ope a ional challenge in aining hei ain ope a o s on
c i ical, ime-sensi i e p ocedu es such as doo and whis le ope a ion. This is a c ucial pa o he
ope a o 's job, bo h o ou ine s a ion s ops and o handling eme gency si ua ions.
Figu e 13-3 – Imme si e T ain Ope a o T aining
The adi ional aining me hod equi es decommissioning a unc ional ain, making i
una ailable o passenge se ice. This p ocess causes unwan ed down ime and associa ed
cos s. Fu he mo e, his me hod o en ails o p o ide a ealis ic aining scena io, as i canno
easily eplica e he noisy, bus ling, and unp edic able en i onmen o a li e ain s a ion. To
add ess hese issues, an imme si e i ual eali y expe ience was c ea ed o simula e hese
scena ios e ec i ely.
13.3.2 Use s and s akeholde s
The p ima y use s o his applica ion a e he DSB ain ope a o ainees. These indi iduals need
o lea n and mas e speci ic ope a ional p ocedu es in a sa e, epea able, and ealis ic
en i onmen be o e hey pe o m hem on ac i e ain lines.
The key s akeholde s a e he DSB o ganisa ion, which bene i s om educed ope a ional
down ime and lowe aining cos s, and he expe aine s who manage he lea ning
expe ience. The aine s use he sys em no jus o di ec ins uc ion bu also o acili a e
e lec ion and eedback among he ainees, enhancing he o e all lea ning ou come.
13.3.3 Implemen a ion in a i ual wo ld and added alues
The solu ion is implemen ed using Imme si e Vi ual Reali y (IVR), c ea ing a de ailed and ealis ic
i ual ep esen a ion o a ain s a ion pla o m, he ain doo s, and he immedia e in e io o
he ain coach. T ainees can na iga e his 3D en i onmen ia elepo a ion while emaining
s a iona y in he physical wo ld.
The added alue o his i ualized app oach is signi ican . I elimina es he down ime o
unc ional ains, p o iding subs an ial cos sa ings. I also o e s a mo e ealis ic simula ion han
adi ional me hods can achie e, imme sing he ainee in a dynamic s a ion en i onmen .
The sys em allows o e icien aining in ba ches and in oduces a collabo a i e elemen , whe e
one ainee can lea n in VR while ano he obse es and p o ides eedback, os e ing pee - o-
pee lea ning.
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13.3.4 Requi ed imme si e echnologies unc ionali ies
The echnology pla o m o his aining u ilises Vi ual Reali y ha dwa e, speci ically men ioning
he HTC Vi e and Oculus Ques headse s. The aining se up is collabo a i e, in ol ing one
ainee in he IVR headse while a second ainee obse es he use 's p og ess on an iPad, a
se up managed by expe aine s.
In e ac ions wi hin he i ual wo ld a e p ima ily conduc ed using wo-handed VR con olle s.
Use eedback is mul i-modal, inco po a ing isual cues ha highligh poin s o in e es and
display in o ma ion like ime s. The sys em also p o ides hap ic eedback, such as a con olle
ib a ion, which ac s as a simple nudge o con i m ha an ac ion, like opening he doo s, has
been success ully comple ed. The sys em does no cu en ly ea u e any o m o adap a ion o
speci ic use skill le els.
The ypes o imme si e echnologies used a e VR and IVR wi h use in e ace modes based on
VR Con olle s, Visual cues.
The i ual en i onmen is a de ailed ep esen a ion o he ain s a ion pla o m, he ain doo s,
and he immedia e in e io o he ain coach behind he doo s. The ainees na iga e he i ual
en i onmen h ough elepo a ion while s anding in one physical loca ion. Visual cues a e
p esen o aid he ainee by displaying ime and highligh poin s-o -in e es .
The ainee uses an Oculus Ques and an iPad, whe e one ainee expe iences IVR aining in
he Ques while he o he obse es he p og ession, and p o ides eedback. Hap ic eedback is
p esen as an indica o o he ainee ha ce ain ac ions we e pe o med. Fo example, when
a ainee opens he ain doo s ge s ib a ion eedback o indica e success ul comple ion. This
is mean mo e like a nudge han o ep esen a physically ealis ic in e ac ion. No adap a ion
o speci ic use s was iden i ied.
13.3.5 Technology laye s equi emen s
The HTC Vi e is used o he cu en ly deployed VR aining. Fo mos pa s o he aining, he
in e ac ions in ol e wo hands (using he VR con olle s).
13.3.6 Ho izon al Issues and Cha ac e is ics
The sys em is designed o be imme si e and in e ac i e. The aining is deli e ed o g oups o
ainees, who a e spli in o pai s wi h one IVR headse and one able pe pai , c ea ing a unique
in e ac i e lea ning dynamic. The expe aine s play a c ucial ole in managing he expe ience
and helping he ainees e lec on wha hey ha e lea ned.
F om a human-cen ic pe spec i e, he sys em pe cei es he use 's ac ions h ough he acked
VR con olle s. In e iews wi h pa icipan s indica e ha hey pe cei e he IVR aining as a
bene icial me hod and a s ep in he igh di ec ion o mode nizing aining, e en hough hey
acknowledge i s ill equi es u he de elopmen . The p ima y con ibu ion o sus ainabili y is
economic, h ough he educ ion o cos s and he elimina ion o p oduc ion ain down ime. No
signi ican e hical issues a e no ed o his applica ion.
The in e iewees pe cei ed he IVR as a bene icial aining me hod, s ill equi ing de elopmen
bu due o he e e -inc easing demand o as aining a a lowe cos a s ep owa ds he igh
di ec ion.
T aining is deli e ed a he headqua e s in ba ches o 12 ainees, who a e spli in o g oups o
wo. Each g oup is assigned an IVR headse and a able . The e a e wo expe aine s who
manage he aining expe ience and aid he ainees e lec on wha hey lea ned.
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13.4 MR Inciden Simula o o imme si e Command and Con ol Room T aining
13.4.1 Scena io
The mixed- eali y applica ion called Inciden Simula o o Command-and-Con ol Rooms allows
o aining pu poses o ope a e a con ol oom ha has been po ed in o a i ual me a e se,
enabling he simula ion o c i ical e en s le e aging on cus omized DTs. The main aim is o allow
dispa che s and con ol cen e s a o p ac ice eal-wo ld scena ios in a amilia en i onmen
wi hou in e up ing he ope a ions o he ac ual con ol oom. The simula ed scena ios o
aining a e d awn om a me a-da abase, ha is popula ed wi h domain-speci ic p ocedu es
and guidelines. T ainees in e ac wi hin a XR en i onmen , ea u ing i ual mul i-moni o panels
ha eplica e he con ol oom se up, and espond o simula ed inciden s using eal-wo ld
de ices, such as physical phone sys ems, sma phones, and compu e s, enhancing he ealism
and e ec i eness o he aining expe ience.
Figu e 13-4 – MR Inciden Simula o .
13.4.2 Use and s akeholde s
Among he use s o he applica ion one can men ion he s a ope a ing a con ol oom, e.g.,
dispa che s, con ol cen e s a membe s, and ainees.
13.4.3 Implemen a ion in a i ual wo ld and added alues
Among he added alues one can lis o e ing he capabili y o ope a ing a con ol oom in a
MR en i onmen , wi hou in e up ing he ope a ion o he eal con ol oom. Mo eo e , aining
pu poses and complex scena io managemen be o e eal p oblems come in o he play a e also
wo impo an addi ional added alues.
13.4.4 Requi ed imme si e echnologies unc ionali ies
The ypes o imme si e echnologies used a e VR and XR.
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To eplica e he con ol oom en i onmen , he applica ion imme ses ainees in a MR by
accu a ely ec ea ing he con ol oom in a i ual space, allowing hem o wo k wi h iden ical
sys ems and in e aces. Ra he han elying on game con olle s o VR con olle s, he simula ion
in eg a es ac ual phones, lap ops, and communica ion ools, ein o cing eal-wo ld imme sion.
So, he use in e ac ion modes a e a mix o : VR Con olle s, Hand T acking, Voice Commands,
Eye T acking, Sma phones, eal con ol oom equipmen .
The use in e ace design uses bo h 2D and 3D elemen s wi h use eedback mechanisms like
hap ic eedback, audio eedback, and isual Feedback.
13.4.5 Technology laye s equi emen s
To moni o and inco po a e ainees' s ess le els in o he aining, sma wa ches and o he
wea ables can be in eg a ed wi h he inciden simula ion. This da a enables s ess le els based
adap i e aining adjus men s, c ea ing a mo e imme si e and pe sonalized expe ience.
An inciden simula ion is pa ially empowe ed by AI-based modules in mul iple use-cases. La ge
Language models (LLM) a e be used o ini ia e phone-calls o he ainee o imme si e
communica ion scena ios wi hin a aining se up. Pa e n-Recogni ion models unde s and he
s ess-le el o he ainee by inco po a ing da a om wea ables as well as oice analy ics.
Measu emen s o he ainee’s eac ion ime can help o u he s eamline he aining-p og am
by in elligen ly adap ing he simula ion o he ainee’s capabili ies.
The leading pla o ms o MR applica ions a e Me a Ques and Vision P o, as bo h p o ide
de elope SDKs ha p o ide access o a on - acing came a and augmen a ion o i ual
elemen s. This enables o, e.g., implemen mul i-panel con ol- oom en i onmen s in
combina ion wi h “see- h ough” elemen s in he eal-wo ld.
13.4.6 Ho izon al issues and cha ac e is ics
N/A.
13.5 XR-based Remo e Collabo a ion wi h IoT Con ex ual In eg a ion
13.5.1 Scena io
The applica ion de elops an open, e sa ile, inclusi e and scalable digi al wo kplace o
acili a e wo k and social ac i i ies be ween many simul aneous use s. I c ea es an imme si e
expe ience by in eg a ing ich con ex ual in o ma ion in o ideo s eams.
Figu e 13-5 – XR-based Remo e Collabo a ion
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13.5.2 Use and s akeholde s
N/A.
13.5.3 Implemen a ion in a i ual wo ld and added alues
N/A.
13.5.4 Requi ed imme si e echnologies unc ionali ies
The ypes o imme si e echnologies used a e Me a e se and XR (AR/VR) en i onmen s.
The use in e ac ion modes a e oice ecogni ion/commands, hand/mo ion acking, a a a s,
VR con olle s e.g. o na u al ges u es ecogni ion, i ual Assis an s, cha bo s. (The use
in e ace design conside s 2D/3D elemen s, and mul i-scene na iga ion.
13.5.5 Technology laye s equi emen s
Cloud-na i e IoT pla o m o dis ibu ed deploymen o e edge-cloud en i onmen s, eal- ime
sensing and ende ing, audio/ ideo/ ex , 3D da a ypes, local/cloud s o age, IoT 2D/3D objec s.
AI-powe ed mee ing assis an wi h ex ended capabili ies such as na u al speech in e ac ion,
mee ing summa isa ion o ansla ion, ich seman ic anno a ions o ask-o ien ed dialogue
modelling.
HTC Vi e Focus 3, Pico 4, Me a Ques 2/3, Windows, Uni y/WebXR, And oid, ALCATEL Rainbow
elecon e encing solu ion, IoT pla o m/se ices p o ided by In acom Telecom and ele an
Ja asc ip /C# SDKs).
13.5.6 Ho izon al issues and cha ac e is ics
Use da a anonymisa ion o pe sonal p i acy p ese a ion and secu i y p o ec ion, compliance
wi h p i acy p o ec ion egula ions (e.g., he EU AI Ac ), au hen ica ion and au ho iza ion
se ices o au ho ised pa icipa ion o con e ences, IoT access and use ole managemen a e
se e al ho izon al issues and cha ac e is ics o hese ypes o use cases.
13.6 Ad anced VR simula o o T aining Law En o cemen O ice s
13.6.1 Scena io
The applica ion is a VR-based simula o designed speci ically o aining police and law
en o cemen s a . T ainees a e imme sed in li elike 3D scena ios ha ange om complex c ime
scenes o ehicle acciden s and e o is inciden s. They can examine e idence, ques ion i ual
wi nesses, and make apid, c i ical decisions in high-p essu e scena ios.
The simula o p o ides eal- ime eedback and pe o mance analy ics, enabling ainees o
unde s and hei s eng hs and a ge a eas o imp o emen , while engaging in a sa e and
con olled lea ning en i onmen .
Each en i onmen is ca e ully c a ed wi h high- esolu ion ex u es, accu a e physics, and
dynamically adjus able condi ions, such as a ying wea he and ime-o -day se ings.
13.6.2 Use and s akeholde s
Use s a e s a membe s o law en o cemen o ice s ha a e o be ained.

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Figu e 13-6 – Ad anced VR Simula o o T aining Law En o cemen O ice s
13.6.3 Implemen a ion in a i ual wo ld and added alues
The applica ion uses hap ic, audio, and isual eedback inpu s om use s, allowing o eal- ime
in e ac ion and en i onmen al manipula ion based on ainee ac ions. O ice s can u ilise oice
commands, ges u es, and physical na iga ion ools o enhanced aining expe ience.
The simula o suppo s mul iplaye modes, enabling o ice s o ain collabo a i ely in scena ios
ha equi e eamwo k and coo dina ion. Scena ios can be cus omized o e lec local legal
amewo ks, speci ic c ime ends, and a ge ed aining goals, p o iding highly ele an and
adap able aining expe iences
13.6.4 Requi ed imme si e echnologies unc ionali ies
The ypes o imme si e echnologies used a e VR, XR, and MR. The use in e ac ion modes a e
based on VR con olle s, hand acking, and oice commands, using a use in e ace design
based on bo h 2D and 3D na iga ion.
The use eedback mechanisms a e hap ic eedback, audio Feedback, isual Feedback o
enhanced si ua ional awa eness.
13.6.5 Technology laye s equi emen s
The applica ion assumes a ne wo k o de ices equipped wi h came as, hap ics, mo ion, dep h,
and p oximi y senso s. The edge compu ing has o suppo CPU/GPU wi h la ency op imiza ion,
capable o eal- ime and 3D da a ypes, spa ial and spa ial- empo al da a p ocessing.
Rega ding he s o age, bo h local and cloud op ions should be made a ailable o scalable
da a handling, in compliancy wi h GDPR s anda ds.
The applica ion has a achable AI modules embedded ha complies o s ic e hical guidelines.
ML o scena io op imiza ion.
The pla o ms used a e Oculus Ri and Mic oso HoloLens.
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13.6.6 Ho izon al issues and cha ac e is ics
The applica ion inco po a es modules ha a e espec ing undamen al igh s du ing
in es iga ions including espec ing he digni y o indi iduals in ol ed in acciden s o c iminal
inciden s, ensu ing ai ea men , and unde s anding he e hical implica ions o hei ac ions. I
gene a es scena ios ha a e unbiased and inclusi e, p o iding a wide ange o si ua ions
wi hou any p ejudice o s e eo yping N/A.
S anda d echnologies ha a e ele an o he applica ion a e JSON and OpenXR o VR,
ensu ing compa ibili y wi h a a ie y o HW and SW sys ems, which allows lexibili y in deploymen
and in eg a ion.
The applica ion is buil on well-known pla o ms like Oculus Ri wi h SDKs o compa ibili y ac oss
de ices and compliance wi h OpenXR s anda ds.
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14 T us wo hiness
14.1 In oduc ion
The con e gence o ad anced echnologies such as edge IoT, AI, DT, imme si e iple s, and
spa ial compu ing is o ging a complex indus ial eal-digi al- i ual con inuum. This con inuum is
ealized h ough imme si e en i onmen s whe e use s can eel physically p esen wi hin a
compu e -gene a ed pe cep ual con ex . These indus ial imme si e solu ions a e ad ancing
he in eg a ion o AR), i ual eali y VR, MR, and XR wi h nex -gene a ion concep s like
me a e ses and he spa ial web (Web 4.0).
As hese echnologies become mo e in eg a ed in o c i ical sec o s, ensu ing hei
us wo hiness is a p ima y conce n o widesp ead adop ion and sa e implemen a ion [1][2].
T us wo hiness in his con ex is a mul i ace ed concep , encompassing he sys em's secu i y,
p i acy, eliabili y, and he e hical in eg i y o he i ual wo lds being c ea ed [3].
Signi ican s ides ha e been made in enhancing he us wo hiness o imme si e applica ions.
The de elopmen o secu e edge compu ing amewo ks has been a key ad ancemen ,
allowing o he local p ocessing o sensi i e da a gene a ed by imme si e de ices. This
app oach no only educes la ency o a mo e seamless use expe ience bu also minimizes he
exposu e o da a o po en ial cybe -a acks du ing ansmission [4]. In pa allel, ad anced
c yp og aphic echniques, such as homomo phic enc yp ion, a e being ac i ely esea ched o
enable secu e da a analysis in cloud en i onmen s, which would allow o compu a ion on use
da a wi hou dec yp ing i , hus p ese ing p i acy.
Ano he a ea o p og ess is he es ablishmen o in e ope abili y s anda ds. These s anda ds a e
c ucial o c ea ing a cohesi e and us ed digi al ecosys em, enabling secu e and seamless
in e ac ion be ween a ious imme si e pla o ms and eme ging me a e ses. Fu he mo e, AI is
being inc easingly le e aged o eal- ime h ea in elligence and anomaly de ec ion wi hin
hese imme si e en i onmen s.
This p oac i e app oach helps o ensu e he in eg i y o he sys em and he sa e y o i s use s.
Wi hin he bu geoning me a e se, he applica ion o blockchain echnology has shown
conside able p omise o secu ing digi al asse ansac ions, e i ying owne ship, and managing
use iden i ies in a decen alised and anspa en manne , which os e s g ea e use us [3].
The apid expansion o imme si e echnologies like VR, AR, and MR is in oducing complex
challenges a he in e sec ion o cybe secu i y, p i acy, and use us . Fo hese echnologies o
be widely accep ed and success ul, use s mus us hem, a eeling oo ed in ac o s like
dependabili y, including sa e y, secu i y, p i acy, eliabili y, main ainabili y, esilience, e c. This
us ela ionship is undamen al, as he e y na u e o imme si e expe iences equi es use s o
su ende a signi ican amoun o pe sonal da a and senso y con ol, making he need o obus
secu i y and p i acy measu es mo e c i ical han e e be o e.
The co e o he imme si e ecosys em is buil upon he collec ion and p ocessing o as amoun s
o XR da a. This da a goes a beyond adi ional pe sonal in o ma ion, encompassing sensi i e
biome ic da a in e ed om eye- acking and body mo emen s, as well as senso da a
cap u ing a use 's physical su oundings. This con inuous s eam o in o ma ion is essen ial o
c ea ing he sense o p esence and imme sion ha de ines XR. Howe e , he shee olume and
in ima e na u e o his da a c ea e unp eceden ed p i acy isks and secu i y ulne abili ies,
making hese sys ems a ac i e a ge s o cybe a acks ha could lead o iden i y he , aud,
and o he o ms o da a misuse.
The unique ope a ional equi emen s o imme si e applica ions ende adi ional p i acy
con ols la gely ine ec i e.
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Fo example, p i acy measu es like simple came a access indica o s a e inadequa e o de ices
ha mus cons an ly sense and map he en i onmen o unc ion co ec ly, inad e en ly
exposing no jus he use bu also bys ande s o su eillance. This highligh s a c i ical challenge:
secu i y and p i acy solu ions mus be s ong and highly usable. I p i acy con ols a e
cumbe some, con using, o in e e e wi h he imme si e expe ience, hey will likely be igno ed
o disabled, lea ing use s exposed.
T us and usabili y o he indus ial imme si e applica ions a e deeply in e dependen in he
con ex o imme si e secu i y and p i acy. A sys em ha is echnically secu e bu di icul o a
use o unde s and o con ol will no be us ed. Con e sely, a sys em ha p io i ises a seamless
use expe ience a he expense o obus secu i y will ine i ably lose use us h ough b eaches
and da a misuse. The e o e, building sa e and secu e indus ial imme si e applica ions equi es
a design philosophy ha in eg a es usable end- o-end p i acy and secu i y measu es om he
g ound up, ensu ing ha hey a e in eg a ed in o he sys em, in ui i e, and place minimal
cogni i e load on he use , he eby os e ing a us wo hy ela ionship be ween indi iduals, he
echnology and he imme si e applica ion.
14.2 Key Challenges in T us wo hiness o Imme si e Applica ions
Despi e hese ad ancemen s, o midable challenges o achie ing obus us wo hiness in
imme si e applica ions pe sis . A p ima y conce n is use p i acy, s emming om he as
quan i ies o sensi i e biome ic, beha iou al, and e en neu ological da a ha nex -gene a ion
XR de ices a e capable o collec ing [5]. P o ec ing his da a om misuse o unau ho ized
access is a complex echnical and e hical p oblem ha has ye o be ully esol ed. The shee
di e si y o ha dwa e, so wa e, and ne wo k condi ions p esen s ano he subs an ial hu dle,
making i di icul o gua an ee a consis en and secu e use expe ience ac oss a agmen ed
ecosys em o de ices and pla o ms.
The po en ial o pe cep ual manipula ion wi hin imme si e en i onmen s ep esen s a c i ical
and unique challenge. Malicious ac o s could heo e ically al e a use 's pe cep ion o eali y,
which could lead o signi ican physical o psychological ha m. This unde sco es he impo ance
o ensu ing he au hen ici y and in eg i y o all digi al con en and in e ac ions wi hin hese
i ual wo lds. The absence o uni e sally accep ed s anda ds o secu i y, da a p i acy, and
con en e i ica ion con inues o impede he de elopmen o a ully in e ope able and
us wo hy imme si e web.
14.3 Fu u e Resea ch T ends
Fu u e esea ch mus pu sue a holis ic and in e disciplina y app oach o building us in
imme si e sys ems. This in ol es de eloping comp ehensi e us models ha accoun o he
in ica e in e play be ween echnical, social, and e hical ac o s. The e is a p essing need o
he c ea ion o adap i e secu i y amewo ks ha can dynamically e ol e o iden i y and
neu alize eme ging h ea s in eal ime. Ad ancing esea ch in o explainable AI (XAI) will also
be c ucial o making he decision-making p ocesses o hese complex sys ems anspa en ,
he eby os e ing g ea e unde s anding and us om use s.
The in eg a ion o digi al wins wi h imme si e en i onmen s o indus ial applica ions, such as
p edic i e main enance and he simula ion o complex ope a ions, o e s a signi ican
oppo uni y o build highly eliable and us ed solu ions. The explo a ion o decen alised
a chi ec u es, pa icula ly hose based on blockchain and dis ibu ed ledge echnologies, will
likely play a cen al ole in he u u e o he me a e se, enhancing secu i y and empowe ing
use s wi h g ea e con ol o e hei pe sonal da a and digi al iden i ies [3]. Ul ima ely, he
es ablishmen o s anda dised p o ocols o secu e da a exchange and de ice in e ope abili y
will be he ounda ional pilla upon which he us wo hy nex -gene a ion spa ial web, o Web
4.0, is buil .
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Gaining consensus on such complex, mul i- ace ed issues among di e se s akeholde s wi h
compe ing in e es s is a slow and a duous p ocess.
Fu he mo e, he apid pace o inno a ion in imme si e echnology means ha s anda ds can
isk becoming obsole e be o e hey a e e en widely adop ed. The p ocess o de eloping and
a i ying a o mal s anda d is o en delibe a e and ime-consuming, while he echnology i sel
is e ol ing a an exponen ial a e. This c ea es a di icul balancing ac : s anda ds mus be s able
and obus enough o p o ide a eliable ounda ion, ye lexible enough o accommoda e
u u e echnological b eak h oughs. Ensu ing ha s anda ds can e ol e and adap wi hou
b eaking backwa d compa ibili y is a c i ical and ongoing challenge o he en i e indus y.
17.3 Fu u e Resea ch T ends in S anda disa ion and In e ope abili y o Imme si e
Applica ions
Looking o wa d, esea ch in imme si e in e ope abili y is mo ing beyond basic asse o ma s
and de ice APIs o add ess mo e complex and nuanced challenges. A key a ea o u u e
esea ch is seman ic in e ope abili y. This goes beyond simply being able o exchange da a; i
means ensu ing ha he meaning o ha da a is unde s ood consis en ly ac oss di e en
sys ems. This in ol es de eloping common on ologies and da a models ha can desc ibe he
p ope ies, beha iou s, and ela ionships o objec s and use s in a i ual wo ld. Fo example, a
seman ic s anda d would ensu e ha a "chai " objec is ecognized as a si able objec wi h
consis en p ope ies in any i ual en i onmen ha adhe es o he s anda d [30].
Ano he c i ical esea ch end is he de elopmen o amewo ks o c oss-pla o m iden i y and
da a managemen . Fo a uly in e ope able me a e se, use s need a pe sis en digi al iden i y
ha hey con ol and can use ac oss di e en i ual wo lds, along wi h he abili y o manage
hei pe sonal da a, asse s, and social g aphs in a secu e and p i a e manne . Resea ch in o
decen alised iden i y sys ems, le e aging echnologies like blockchain and e i iable
c eden ials, is explo ing ways o gi e use s so e eign y o e hei digi al sel es, b eaking down
he da a silos c ea ed by oday's pla o m-cen ic models [31].
Fu u e esea ch will also ocus on he in eg a ion o AI wi h s anda disa ion e o s. AI can be
used o au oma e he p ocess o con e ing asse s be ween di e en o ma s and o
dynamically b oke communica ion be ween sys ems ha do no sha e a common s anda d.
Fu he mo e, esea ch is needed o de elop s anda ds o he e hical and esponsible use o AI
wi hin imme si e en i onmen s, ensu ing ha AI-d i en a a a s and sys ems beha e in a
p edic able and us wo hy manne ac oss di e en pla o ms. The goal is o c ea e a dynamic,
in eg a ed ecosys em whe e s anda disa ion enables no jus echnical connec i i y, bu a uly
seamless and meaning ul exchange o expe iences ac oss a as ne wo k o i ual wo lds.
S anda disa ion and in e ope abili y a e he ounda ional pilla s upon which he u u e o he
indus ial eal-digi al- i ual con inuum will be buil . They a e he in isible in as uc u e ha will
enable he c ea ion o a pe sis en , open, and in e connec ed me a e se, a he han a
agmen ed collec ion o disconnec ed i ual expe iences. While signi ican p og ess has been
made in de eloping open s anda ds o ha dwa e, asse s, and communica ion, o midable
challenges ela ed o ma ke agmen a ion, echnological complexi y, and co po a e
compe i ion emain.
O e coming hese challenges will equi e a sus ained and collabo a i e e o om ac oss he
indus y, academia, and he public sec o . The u u e o esea ch lies in ackling highe -le el
p oblems like seman ic in e ope abili y, decen alised iden i y, and he s anda disa ion o AI
beha iou s. By in es ing in hese a eas, we can build he common language needed o unlock
he ull po en ial o imme si e echnologies, os e ing an en i onmen ich wi h inno a ion,
economic oppo uni y, and sha ed human expe ience. The jou ney is complex, bu he c ea ion
o a uly open and in e ope able spa ial web is a goal wo hy o he e o .

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18 Conclusions
The usion o dis up i e echnologies, including he IoT, edge compu ing, AI, and spa ial
compu ing, is d i ing a pe iod o unp eceden ed inno a ion. This con e gence spa ks he c oss-
pollina ion o ideas, c ea ing no el app oaches and ans o ming he indus ial landscape. The
esul is a new gene a ion o sma de ices, in eg a ed sys ems, and inno a i e se ices ha a e
ede ining business models and ope a ional e iciencies ac oss sec o s.
Imme si e echnology s ands a he hea o his ans o ma ion, o e ing a e olu iona y
app oach o c ea ing digi al expe iences ha eel angibly eal. By blu ing he line be ween
he physical and digi al wo lds, hese echnologies cul i a e a p o ound sense o p esence and
engagemen . They anspo use s in o ich i ual en i onmen s o augmen hei eal-wo ld
su oundings wi h in e ac i e digi al in o ma ion, enabling seamless in e ac ions ac oss physical,
digi al, and spa ial domains.
The b oad adop ion and success ul implemen a ion o imme si e expe iences a e
undamen ally enabled by ad ancemen s in IoT and edge compu ing. These echnologies
p o ide he c i ical in as uc u e o eal- ime da a p ocessing and dis ibu ed in elligence. By
b inging compu a ional powe close o he sou ce o da a, IoT and edge compu ing acili a e
he low-la ency in e ac ions equi ed o a luid and esponsi e connec ion be ween he
physical and i ual wo lds.
De eloping such echnologies equi es a holis ic and in e disciplina y app oach. The complexi y
o in eg a ing ha dwa e, so wa e, connec i i y, and use expe ience demands collabo a ion
ac oss di e se ields o expe ise, om ne wo k enginee ing o human-compu e in e ac ion. This
collabo a i e spi i is a i al complemen o he g ow h o he indi idual disciplines, se ing as a
powe ul d i e o knowledge c ea ion, esea ch, and genuine inno a ion.
Key esea ch and inno a ion e o s wi hin he IoT, AI and edge compu ing con inuum a e
ocused on c i ical challenges. These include designing esilien dis ibu ed a chi ec u es,
ensu ing in elligen connec i i y, and p o iding end- o-end secu i y o he e ogeneous sys ems.
Fu he mo e, p og ess in a eas like IoT-enabled digi al wins, imme si e iple s, swa m
in elligence, and es ablishing us wo hiness h ough igo ous e i ica ion and alida ion is
pa ing he way o a ue In e ne o In elligen Things.
Looking ahead, he e olu ion o he in e ne is poin ing owa ds Web 4.0, a new e a concei ed
as a decen alised online ecosys em. Buil upon blockchain echnology, his nex -gene a ion
web aims o shi con ol and da a owne ship om cen alised co po a ions back o he use s.
The p inciples o Web 4.0 p omise o c ea e a mo e open, anspa en , and use -empowe ed
digi al ealm, p o oundly changing how we in e ac wi h da a and i ual en i onmen s.
The con e gence o IoT, AI, digi al wins, and spa ial compu ing is o ging a obus indus ial
imme si e con inuum. This con inuum ep esen s a new pa adigm o digi al ans o ma ion,
whe e he dis inc ions be ween he eal, digi al, and i ual wo lds become inc easingly luid. I
allows human in e ac ion and indus ial p ocesses o anscend adi ional physical limi a ions,
opening new possibili ies o inno a ion and e iciency.
Wi hin his con inuum, indus ial imme si e solu ions a e e ol ing om s andalone applica ions
in o highly in eg a ed pla o ms. These pla o ms enable a con inuous and bidi ec ional low o
da a be ween physical asse s and hei dynamic digi al coun e pa s. This syne gy allows o
mo e inno a i e design p ocesses, highly e icien ope a ions, and mo e e ec i e,
con ex ualised aining p og ams ha undamen ally al e how we wo k, lea n, and collabo a e.
Se e al key echnological ad ancemen s ha e been ins umen al in enabling he widesp ead
adop ion o hese imme si e solu ions.
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The de elopmen o high- ideli y, ligh weigh , and un e he ed XR headse s has d ama ically
imp o ed use com o and mobili y, making hem p ac ical o dynamic indus ial se ings.
Simul aneously, p og ess in compu e ision and AI-powe ed spa ial mapping allows de ices o
unde s and and in e ac wi h he physical wo ld in eal ime.
The in eg a ion o digi al win and imme si e iple s’ echnologies is a pa icula ly impac ul
ad ancemen . I allows use s o c ea e de ailed, da a- ich i ual eplicas o physical asse s,
p ocesses, and en i e sys ems. When combined wi h imme si e en i onmen s, hese wins and
iple s become p ac ical ools o simula ion, emo e moni o ing, and p edic i e main enance,
unlocking ample alue and ope a ional insigh .
Suppo ing hese applica ions, he ma u a ion o cloud and edge compu ing has p o ided he
necessa y compu a ional powe o ende complex i ual scenes and p ocess as da ase s
wi h minimal la ency. This in as uc u e is c i ical o c ea ing he esponsi e, collabo a i e
expe iences ha a e essen ial o indus ial use cases, om he ac o y loo o he ope a ing
oom.
Imme si e indus ial applica ions, such as AR, VR, eal- ime digi al wins and imme si e iple s,
a e ans o ma i e o indus ial en i onmen s and place ex eme demands on ne wo k
in as uc u e. They equi e a combina ion o high da a a es o ideo s eams, ul a-low la ency
o eal- ime in e ac ion and con ol, and e y high eliabili y o p e en ope a ional ailu es.
5G/6G NPNs a e uniquely designed o add ess hese s ingen , end- o-end equi emen s h ough
a combina ion o key echnological ea u es such as ne wo k slicing and QoS con ol, whe e
NPNs enable he c ea ion o mul iple i ual ne wo ks, o "slices," on a single physical
in as uc u e. Each slice can be con igu ed wi h i s own gua an eed quali y o se ice (QoS)
pa ame e s. Fo an imme si e AR applica ion, a dedica ed slice can be p o isioned wi h high
bandwid h and low la ency, ensu ing ha he ideo eed emains smoo h and in e ac i e,
comple ely isola ed om o he ne wo k a ic like email o senso da a uploads.
The syne gy be ween NPNs and mul i-access edge compu ing (MEC) is c i ical. By deploying
compu e and da a s o age esou ces a he edge o he p i a e ne wo k, physically close o
he de ices and use s, da a p ocessing occu s on-si e. Fo emo e-con olled de ices, his
minimises he ound- ip ime o da a, educing la ency om hund eds o milliseconds (on a
ypical cloud se up) o jus a ew milliseconds. This immedia e p ocessing is wha makes eal-
ime con ol and imme si e o e lays possible.
In con as o public mobile ne wo ks, which can su e om unp edic able conges ion, an NPN
p o ides a "con olled adio en i onmen . By ope a ing on dedica ed o licensed spec um, he
ne wo k is immune o pe o mance deg ada ion om public use a ic. I ensu es consis en ,
p edic able pe o mance, which is essen ial o mission-c i ical indus ial p ocesses whe e a
d opped connec ion o lag spike could lead o p oduc ion hal s o sa e y inciden s.
5G s anda ds speci ically de ine URLLC o suppo applica ions equi ing less han 1ms la ency
and 99.999% eliabili y. NPNs enable o ganisa ions o ine- une ne wo k pa ame e s, p io i ising
URLLC a ic, which is c ucial o applica ions such as au onomous sys ems and collabo a i e
obo ic sys ems ha ely on ins an aneous communica ion o sa e and e icien ope a ion.
In an NPN, he da a can be kep wi hin he en e p ise's p i a e domain. This on-p emises da a
handling is undamen al o indus ial applica ions dealing wi h sensi i e in ellec ual p ope y,
p op ie a y ope a ional da a, o egula ed in o ma ion. This obus secu i y model builds us and
ensu es ha imme si e applica ions do no c ea e new ulne abili ies.
NPNs can be designed o p o ide uni o m, high-pe o mance co e age ac oss complex
indus ial en i onmen s like ac o ies, po s, o mines. Using echnologies like beam o ming and
op imisa ion o he wi eless cells’ placemen , he ne wo k can be ailo ed o elimina e dead
zones and deli e consis en signal s eng h o bo h s a iona y and mobile de ices, ensu ing
unin e up ed QoE o use s and de ices mo ing ac oss a la ge si e.
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All hese ea u es c ea e a con inuous cycle ha can gua an ee bandwid h and low la ency,
elimina e mo ion condi ions in VR and lag in AR, making he expe ience mo e na u al and
e ec i e. High eliabili y ensu es ha c i ical ope a ions o he imme si e applica ions a e ne e
comp omised. By p o iding secu e, cus omisable, and high-pe o mance ne wo king
en i onmen s, 5G/6G NPNs mo e imme si e echnologies om a no el y o a eliable and
indispensable indus ial ool, d ama ically imp o ing bo h ope a ional e iciency and he human
quali y o expe ience.
As hese echnologies ma u e, hey lay he g oundwo k o nex -gene a ion i ual wo lds,
including concep s like he me a e se and omni e se. While hese u u e en i onmen s o e
immense oppo uni ies, hei de elopmen is accompanied by signi ican echnical, socie al,
and economic challenges. A ca e ul and p oac i e app oach is equi ed o balance he
po en ial ewa ds agains he inhe en isks om he ea lies s ages o design and deploymen .
Ul ima ely, he con e gence o IoT and indus ial imme si e applica ions he alds a signi ican
echnological, cul u al, and economic shi . I p omises a mo e in eg a ed, in e ac i e, and
in elligen u u e, al e ing how we pe cei e and in e ac wi h bo h he digi al and physical
wo lds. The oppo uni ies o unlocking unp eceden ed capabili ies and d i ing p og ess ac oss
nea ly e e y sec o o he economy and socie y a e uly p o ound.
This posi ion pape gi es an o e iew o edge IoT indus ial imme si e applica ions ac oss
indus ial sec o s such as cul u e and he i age, manu ac u ing, au omo i e, ene gy, buildings,
mobili y and anspo a ion, heal hca e, ag icul u e, and ou ism, highligh ing how imme si e
echnologies a e ca alysing a p o ound ans o ma ion ac oss hese di e se ange o indus ies,
mo ing beyond niche applica ions o become a co ne s one o mode n digi al s a egy. By
blending he physical and digi al wo lds, hese echnologies a e unlocking unp eceden ed
e iciencies, enhancing sa e y, and c ea ing en i ely new ways o design, ope a e, and in e ac
wi h complex sys ems. F om he ac o y loo o he ope a ing oom, imme si e solu ions a e
ede ining he bounda ies o wha is possible.
The au omo i e indus y has been a angua d in his echnological shi , embedding imme si e
ools h oughou he en i e ehicle li ecycle. In he design phase, i ual eali y enables global
eams o collabo a e on ull-scale digi al p o o ypes, signi ican ly accele a ing de elopmen
and educing eliance on cos ly physical models. On he assembly line, augmen ed eali y
p o ides echnicians wi h in e ac i e, in-si u ins uc ions, d ama ically imp o ing accu acy and
e iciency. Fo consume s, imme si e show ooms o e engaging and pe sonalised ways o
explo e and cus omise ehicles.
In manu ac u ing, imme si e echnologies a e he engine o he " ac o y o he u u e."
Companies a e le e aging digi al wins o hei p oduc ion lines o simula e and op imise layou s
in i ual eali y be o e commi ing o physical changes, a p ocess known as i ual
commissioning. This d as ically cu s down on se up imes and mi iga es p oduc ion isks. VR also
p o ides a sa e, epea able en i onmen o aining wo ke s on complex o haza dous
machine y, while AR-powe ed emo e assis ance connec s on-si e echnicians wi h global
expe s, minimising down ime.
The ag icul u al sec o is le e aging imme si e ech o ad ance p ecision a ming. By using
augmen ed eali y, a me s can isualise eal- ime da a om d ones and senso s di ec ly o e
hei ields, enabling highly a ge ed applica ions o wa e , e ilise s, and pes icides. This da a-
d i en app oach, o en linked o a comp ehensi e digi al win o he a m, op imises esou ce
use and boos s c op yields sus ainably.
In he ene gy sec o , imme si e applica ions a e c i ical o enhancing sa e y and ope a ional
e iciency. VR is used o high-s akes eme gency aining, allowing wo ke s o p ac ice
p ocedu es o scena ios like oil ig blowou s o wind u bine main enance in a isk- ee
en i onmen . On-si e, echnicians use AR and MR o o e lay eal- ime da a and main enance
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eco ds on o complex equipmen , imp o ing accu acy and wo ke sa e y in powe plan s and
subs a ions.
Heal hca e is one o he mos impac ul domains o hese echnologies. Vi ual eali y is
e olu ionising su gical aining by allowing su geons o ehea se complex p ocedu es in hype -
ealis ic simula ions. In he ope a ing oom, augmen ed eali y p o ides su geons wi h "X- ay
ision," o e laying 3D pa ien scans di ec ly on o he body o guide ins umen s wi h inc edible
p ecision. Fu he mo e, VR is p o ing o be a powe ul ool o pain managemen and men al
heal h he apy.
The A chi ec u e, Enginee ing, and Cons uc ion (AEC) sec o has e olu ionised i s wo k lows
wi h imme si e echnology. A chi ec s o e clien s imme si e VR walk h oughs o expe ience
and e ine designs be o e cons uc ion begins. On-si e, AR is used o o e lay building in o ma ion
models (BIM) on o he physical s uc u e, ensu ing accu acy and p e en ing cos ly clashes
be ween sys ems like plumbing and elec ical.
The anspo a ion and logis ics sec o s a e also ealising signi ican gains. U ban planne s use VR
o simula e a ic lows and es new in as uc u e, while AR applica ions guide wa ehouse
wo ke s h ough picking o de s wi h hands- ee, isual cues, boos ing speed and accu acy.
In he i age and ou ism, imme si e ech o e s powe ul new ways o expe ience cul u e, om
b inging museum a e ac s o li e wi h AR o c ea ing i ual econs uc ions o his o ical si es,
making ou sha ed his o y mo e accessible and engaging han e e be o e.
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Con ibu o s
Edi o s:
O idiu Ve mesan, SINTEF, No way
Vale io F ascolla, In el, Ge many
Re iewe :
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Con ibu o s (alphabe ic o de ):
Alain Pagani, Ge man Resea ch Cen e o A i icial In elligence, Ge many
Albena Miho ska, Resea ch and De elopmen and Inno a ion Conso ium, Bulga ia
And eas El Sae , KONNECTA Sys ems, G eece
Ángel Ma ín Na as, VICOMTECH, Spain
A is ea Za ei opoulou, KONNECTA Sys ems, G eece
A u K ukowski, In acom Telecom, G eece
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Bjö n Debaillie, imec, Belgium
Cian O Mu chu, Tyndall Na ional Ins i u e, I eland
Daniela Buleand a, SIMAVI, Romania
E idy Lukau, Fokus F aunho e , Ge many
F ances Clea y, Wal on ins i u e, SETU, I eland
F ancesco Chinello, Aa hus Uni e si y, Denma k
F ançois Fische , FSCOM, F ance
Geo ge Suciu, BEIA, Romania
Geo ge Tsaki is, KONNECTA Sys ems, G eece
Guido Pe icone, B embo N.V., I aly
Hazel Pea oy, Wal on Ins i u e, I eland
Ignacio Lacalle, Uni e si a Poli ecnica de Valencia, Spain
Ilia Pie i, In acom Telecom, G eece
Jesus Angel Ga cia Sanchez, Ind a Sis emas, Spain
Joachim Hilleb and, Vi ual Vehicle Resea ch, Aus ia
Joao Sousa, CCG, Po ugal
Kons an inos Koumadi is, Aa hus Uni e si y, Denma k
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Kons an inos Loupos, INLECOM Inno a ion, G eece
Leonidas Vala anis, KONNECTA Sys ems, G eece
Ma ia Xezonaki, In acom Telecom, G eece
Ma in Se ano, Insigh SFI esea ch Cen e o Da a Analy ics, I eland
Ma hias Ha mann, imec, Belgium
Mi ko P esse , Aa hus Uni e si y, Denma k
Monica Flo ea, SIMAVI, Romania
Na alie Samo ich, Ene cou im, Po ugal
O idiu Ve mesan, SINTEF, No way
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Pie i Ilia, In acom Telecom, G eece
Ranga Rao Venka esha P asad, Technical Uni e si y Del , Ne he lands
Ronald Maandonks, Signi y, The Ne he lands
Roumen Nikolo , Vi ech, Bulga ia
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Se gio Gusme oli, Poli ecnico di Milano, I aly
Sil ia Romana O a iani, SIAD Macchine Impian i S.p.A., I aly
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Kos as Naskou, ICCS, G eece
Udayan o Dwi A mojo, Aal o Uni e si y, Finland
Vale io F ascolla, In el, Ge many
Vasileios Ka agiannis, Aus ian Ins i u e o Technology, Aus ia
Ve onica An onello, TXT e- ech, I aly
Ve onica Quin una Rod iguez, O ange, F ance
Vladimi Poulko , Technical Uni e si y o So ia, Bulga ia