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Analysis o he Beha io o an Indoo Posi ion
Sys em Based on Finge p in s and IEEE 802.15.4
A. Ve ónica Medina*1, José A. Gómez2, José A. Ribei o3, En ique Do onzo o4, Se gio Ma ín5
Depa amen o Tecnología Elec ónica, Se ille Uni e si y, TAIS G oup (h p://ma ix.d e.us.es/g upo ais/)
ETSI-INF, A da. Reina Me cedes s/n, Se ille, Spain
*1 [email protected]; 2jgo[email p o ec ed]; 3jose. ibei [email protected]; 4en iquedz@d e.us.es; 5s[email p o ec ed]
Abs ac - This pape p esen s an analysis o he beha iou o an indoo posi ion sys em based on inge p in s and IEEE 802.15.4 ha
has been adap ed o be es ed in a compe i ion, called E AAL (E alua ing AAL Sys ems h ough Compe i i e Benchma king), hold
bo h in Mad id ( es s) and Eindho en ( esul s). The objec i es o his analysis a e o de e mine he bes algo i hm ha should ha e
been applied in o de o ob ain he bes esul s in he compe i ion o use hem in o he en i onmen s. Among he di e en
combina ions ha can be applied, i.e., he way he signa u e da abase is illed in and he algo i hm uses o de e mine he closes
loca ion poin , he bes esul s a e ob ained using a global signa u e da abase whe e each signa u e en y is calcula ed by he
medium o samples signa u es da abase and he closes loca ion is de e mined by a cen oid algo i hm wi h he pa ame e c se o 1.3.
In his way, he e o made imp o es he one ob ained in he E AAL, which is educed by 100 cen ime es.
Keywo ds- IEEE 802.15.4; RSSI; Cen oid; Indoo posi ion; ZigBee; WSN; Bi Cloud; OpenMAC
I. INTRODUCTION
WSNs (Wi eless Senso Ne wo ks) a e p esen ed in many applica ions, and examples o WSN applica ions a e ound in
Ambien Li ing [1-4] o Sma building [5-9] esea ching ields o sol ing da a acquisi ion p ocess. Depending on i s
applica ions, ambien o use senso s and ac ua o s can be used o making decisions. WSNs a e composed by mo es. A mo e is
a de ice ha con o ms o he IEEE 802.15.4 s anda d, so a WSN has his echnology as based.
The knowledge o a subjec ’s posi ion is e y use ul in hese kinds o sys ems because depending on i he decisions o be
made a e di e en . As s a ed in [11] and [12], an amoun o indoo loca ion acking sys ems ha e been p oposed in he
li e a u e, based on Radio F equency (RF) signals, ul asound, in a ed, o some combina ion o modali ies.
Using RF signal s eng h, i is possible o de e mine he loca ion o a mobile node wi h an accep able accu acy. Gi en a
model o adio signal p opaga ion in a building o o he en i onmen , ecei ed signal s eng h can be used o es ima e he
dis ance om a ansmi e o a ecei e , and he eby o iangula e he posi ion o a mobile node. Howe e , his app oach
equi es de ailed models o RF p opaga ion and does no accoun o a ia ions in ecei e sensi i i y and o ien a ion.
An al e na i e app oach is o use empi ical measu emen s o ecei ed adio signals, known as RSSI, Recei e Signal
S eng h Indica o , o es ima e loca ion. By eco ding a da abase o adio ‘‘signa u es’’ along wi h hei known loca ions, a
mobile node posi ion can be es ima ed by acqui ing he ac ual signa u e and compa ing i o he known signa u es in he
da abase, also known as inge p in s. A weigh ing scheme can be used o es ima e loca ion when mul iple signa u es a e close
o he acqui ed signa u e.
All o hese sys ems equi e he signa u e da abase o be manually collec ed p io o sys em ins alla ion, and ely on a
cen al se e (o he use ’s mobile node) o pe o m he loca ion calcula ion. Se e al sys ems ha e demons a ed he iabili y
o his app oach, one o hose is Mo eT ack [11-12]. Mo eT ack is based on deploying a speci ic WSN o de e mine loca ion.
Mo eT ack’s basic loca ion es ima ion uses a signa u e based app oach ha is la gely simila o RADAR [10] ha ob ains a
75 h pe cen ile loca ion e o o jus unde 5 m, bu in Mo eT ack dec eased he loca ion e o by 1/3.
We ha e implemen ed a simila sys em o Mo eT ack, a signa u e-based localiza ion scheme, bu using o he mo es and
di e en so wa e, he Bi Cloud S ack [13], a Zigbee and Zigbee-PRO implemen a ion, and OpenMac S ack [14], an IEEE
802.15.4 implemen a ion. The way he messages a e sen and how he RSSI is calcula ed is also di e en as he one used in
Mo eT ack. We ha e also compa ed bo h implemen a ions in [15] and p esen ed in [16] an adap a ion o ou sys em in he
E AAL compe i ion.
In his pape , an analysis o he beha iou o he adap ed indoo posi ioning sys em is p esen ed in o de o de e mine he
bes algo i hm o be applied in an en i onmen simila o he one es ed in he E AAL.
This pape is s uc u ed in he ollowing way. An o e iew o ou indoo posi ioning p o o ype and he adap a ion made o
he E AAL compe i ion a e shown in Sec ions II and III. In Sec ion IV he me hodology ollowed o ga he all he in o ma ion
o be analysed is explained. The simula ions made a e p esen ed in Sec ion V. Finally conclusions a e es ablished in Sec ion VI.
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II. PROTOTYPE OVERVIEW
In ou p o o ype (Fig. 1), a building o o he a ea is popula ed wi h a numbe o mo es ac ing as ixed nodes, one o hem
being he coo dina o , C, and a se o mo es as mobile nodes, he ones whose posi ion is going o be de e mined. Each ixed
node sends o C pe iodic beacon messages, beacon 2, which consis o an n- uple o he o ma {MobileID, RSSI}, whe e n is
he numbe o mobile nodes, MobileID is a unique iden i ie o a mobile node, and RSSI is he signal s eng h which each ixed
node ecei ed he las beacon message sen by MobileID node. The beacon message sen by a mobile node is di e en om he
one sen by a ixed node, o di e one om o he s, he mobile node beacon messages a e called beacon 1. No all ixed mo es
ecei e beacon 1 messages, and his depends on he co e age a ea. In his case hey send a beacon 2 wi h a ze o alue in RSSI
co esponding o ha mobile node.
Fig. 1 Sys em O e iew. M1 is a mobile node, F1-F5 a e ixed nodes, and C is he coo dina o , also a ixed node. M1 pe iodically sends a beacon message,
beacon 1, o in o m he o he s node ha is p esen , all ixed node ha ecei es i , sa e he RSSI o ha message in a able. Fixed node pe iodically sends a
message o C, beacon 2, o in o m abou he RSSI ha hey ecei e om mobiles node, M1 in his case.
The loca ion es ima ion p oblem consis s o a wo-phase p ocess: an o line collec ion o e e ence signa u es o se he
signa u e da abase, ollowed by an online loca ion whe e he mobile nodes posi ion a e es ima ed.
A. O line Phase
As in o he signa u e-based sys ems, he e e ence signa u e da abase, R, in he o -line phase, is acqui ed manually by a
use wi h a mobile node and a PC connec ed o C. The e e ence signa u e da abase consis s o a numbe o e e ence
signa u es. Each e e ence signa u e, shown as black do s in Fig. 1, is o med by a se o signa u e uples o he o ma {sou ce
ID, mean RSSI}, whe e sou ce ID is he ixed node ID and mean RSSI is he mean RSSI o a se o beacon messages ecei ed
o e some ime in e al. The mean RSSI is used because i is he way Mo eT ack did, bu i is possible o use ano he c i e ia.
Each signa u e is mapped o a known loca ion by he use acqui ing he signa u e da abase (P1-P5 in Fig. 1).
B. Online Phase
In he online phase, gi en a mobile node’s ecei ed signa u e, s, ecei ed om he ixed nodes, and he e e ence signa u e
se R, he mobile node’s loca ion can be es ima ed in he ollowing way. The i s s ep is o compu e he signa u e dis ances,
om s o each e e ence signa u e i∈ R. We employ he Manha an dis ance me ic, as we e done in Mo eT ack,



T
s RSSI RSSIs M )()(),(
(1)
whe e T is he se o signa u es uples p esen ed in bo h signa u es, RSSI(i) is he RSSI alue in he signa u e appea ing in
signa u e i and RSSI(i)s is he RSSI alue in he signa u e appea ing in signa u e s.
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Gi en he se o signa u e dis ances, he loca ion o a mobile node can be calcula ed in se e al ways applying he Cen oid
algo i hm
Cen oid algo i hm conside s he cen oid o he se o signa u es wi hin some a io o he nea es e e ence signa u e.
Gi en a signa u e s, a se o e e ence signa u es R, and he nea es signa u e we selec all e e ence signa u es ∈ R ha
sa is y
)*,(
),(
s M
s M
(2)
o some cons an c, empi ically-de e mined. The geog aphic cen oid o he loca ions o his subse o e e ence signa u es is
hen aken as he mobile node’s posi ion. Small alues o c wo k well, gene ally 1.1 o 1.2. I c=1 he posi ion es ima ion is he
posi ion o he nea es signa u e sa ed in he signa u e da abase. We used in ou p o o ype c=1.2 as in Mo eT ack.
C. P o o ype Uses and Tes s
This p o o ype was used in a esea ch p ojec ha ied o make an In elligen Building
1
. The Building had o adap he
en i onmen o make i s use s eel com o able by con olling ai -condi ioning, music, e c., by means o he WSN deployed in
i ha senses he en i onmen . The use s o he building had o ca y a mo e ( he mobile mo e) and he decision make so wa e
in o med he ac ua o so wa e o change he en i onmen al de ices as use equi emen s by using he ou pu o ou sys em
(es ima ed posi ion and senso s in o ma ion) and o he pa ame e s hey es ima ed. The ocus o ha esea ch p ojec was only
an accu acy o oom posi ioning.
The p o o ype (Fig. 2) was deployed o e hal loo o ou Depa men A ea, measu ing oughly 225 m2. A e es ing how
he di e en kinds o ma e ials a ec he RSSI alue and ha a mo e can co e an a ea o 4-5 me e s, we de e mined ha a
numbe o 7 ixed mo es we e enough o co e he whole a ea. Ou p o o ype was es ed in o de o know i i is possible o
de e mine i a mobile mo e is placed in a oom, i.e., i did no ma e exac ly whe e i was inside he oom, so he p ecision
equi ed was no e y high. This was his way, because he kind o applica ions o whom ou indoo posi ion solu ion was
es ed did no equi e mo e p ecision.
Based on empi ical measu emen s, we de e mined ha he p ecision o ou p o o ype was abou 77%, i.e., he igh oom
was de e mined in ha pe cen age being he accu acy among 0 me e o less han 1 me e om he eal posi ion. The es one
was bad posi ion de e mina ion, no he igh oom, bu he accu acy was among 0.5 me e s o less han 4 me e s om
Fig. 2 P o o ype in e ace
III. ADAPTATION TO THE EVAAL COMPETITION
As men ioned p e iously, he design equi emen s o ou p o o ype we e only o de e mine he ac ual posi ion in a oom o
a use in a building, so he p o o ype accu acy was oom accu acy. In spi e o he ac ha one o he mos impo an d awbacks
was ha he Sma House Li ing Lab o he Poly echnic Uni e si y o Mad id had only wo ooms we decided o compe e in
he second edi ion o E AAL Compe i ion. This implied ha he equi ed accu acy was me e s (e o less han o equals o 0.5
me e s go he highe sco e, highe han 4 me e s go no sco e) and he oom accu acy was subs i u ed by a eas o in e es
(AOI), so he beha iou o how ou p o o ype was going o wo k was an incogni a.
1
Heal h In elligen Technologies O ien ed o Heal h and com o in In e io En i onmen s (TECNO-CAI) app o ed p ojec a he i h call o
CENIT p og am by he Inno a ion Science Minis y o Spain (CDTI and Ingenio 2010 P og am).
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Among o he hings, we had o adap he use in e ace (Fig. 3) o he new a ea and loo plan . The a ea was app oxima ely
100 m2. We also had o decide he numbe s o mo es equi ed and he place whe e each one was going o be placed. The
p o o ype also had o in e ac wi h a domo ic bus deployed in he Li ing Lab, and he benchma k so wa e. Al hough he
domo ic bus e en s we e ecei ed in he p o o ype, such as a ligh swi ch when i was swi ched on, o name one o hem, hey
we e no aken in o accoun in de e mining he ac ual posi ion.
Fig. 3 E AAL p o o ype in e ace
The compe i ion esul s a e shown in Table 1. The e we e i e mains opics o be sco ed: accu acy, a ailabili y, ins alla ion
complexi y, use accep ance and AAL en i onmen in eg a ion. Accu acy measu ed he posi ion e o in me e s in 75 h
pe cen ile and AOI success, in ou case, we only go poin in AOI success. A ailabili y es ed how o en he p o o ypes sen
measu emen s, we go he highe sco e in his i em among all compe i o s. Ins alla ion complexi y ied o e alua e how easy
he p o o ype could be ins alled, due o he ush, we did no pay a en ion o his i em as i is going o be explained in he nex
pa ag aph. Use accep ance ocused on how com o able he p o o ype was om use ´s poin o iew and AAL en i onmen
in eg a ion checked how easy he p o o ype could be in eg a ed in AAL en i onmen ( o ins ance, i i go s anda d in e aces).
We go a inal sco e o 4.2 poin s.
TABLE 1 EVAAL COMPETITION RESULTS
Fig. 4 Li ing Lab emula ion pho os
Fig. 5 Li ing Lab emula ion plan iew
Fig. 6 Tes ing me hodology
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All he compe i o s knew he e alua ion c i e ia be o e he es day in Li ing Lab. In ou case, we did no pay a en ion
because we ocused on adap ing ou p o o ype o he Li ing Lab. We did no e en ha e ime le o es co ec ly he adap ed
p o o ype p epa ed o he E AAL. Fo a quick es ing, we deployed he Li ing Lab in wo o ou labo a o ies, ma king on he
loo he di e en ooms and AOIs as shown in Fig. 4. The emula ion was no qui e good due o he ac ha he e was a wall
be ween he ki chen and he li ing- oom zone (Fig. 5).
Ou compe i ion esul s we e no e y good bu hey we e wo se han we expec ed. In ou p e ious and quick es , wi hou
a deep s udy, he poin o poin accu acy was 3, 1 me e s in 75 h pe cen ile, bu he esul in Li ing Lab was 4.6 me e s. The
AOI accu acy esul in ou labs was 59% being 40% he igh AOI and 19% no he igh AOI bu a suba ea in ha AOI. In
Mad id we go a 20% in AOI accu acy which he igh AOI sco e was 5.5%. So he wall in he labs we es ed he adap ed
p o o ype should ha e made he emula ion no as eal as we hough .
IV. METHODOLOGY
The inge p in algo i hm we use o de e mine he posi ion has many pa ame e s ha can be se depending on he
en i onmen i is going o wo k so he accu acy esul s could be di e en i hey a e no se adequa ely. These pa ame e s a e,
among o he s, he way each signa u e poin is calcula ed, how samples a e ga he ed, how he dis ance be ween wo signa u es
is measu ed, e c. We es ed ou p o o ype in he Li ing Lab in Mad id wi hou s udying deeply he igh combina ion o hem
in ha en i onmen . In his sec ion we explain he me hodology ollowed o ga he he da a in o de o make simula ions o es
which pa ame e s a e he mos app op ia es, o achie e he bes esul s, i.e., o imp o e he esul s ob ained in he E AAL
compe i ion.
The whole me hodology applied is shown in Figu e 6. Fi s we go om he WSN, using he p o o ype so wa e, o each
poin in he signa u es da abase a se o samples, samples da abase, in o de o de e mine la e i s signa u e by using. Each
sample sa ed had he ollowing in o ma ion: yea , mon h, day, hou , minu e, second, x-coo dina e, y-coo dina e, o ien a ion,
whe e o ien a ion can e e o no h, sou h, eas , wes , o global. I o ien a ion is global i means ha he sample was sa ed
while he mobile mo e was u ning a ound. In his way, di e en signa u es da abase can be calcula ed acco ding o di e se
c i e ia.
Fo acking pu pose, some ex a poin s we e aken, in his case, he o ien a ion was no global. A acking applica ion was
also de eloped o de ine di e en acks o es .
Di e en signa u e da abases we e calcula ed wi h he sample da abase poin s using a da a mining ool called Clemen ine.
A simula ion p og am was also de eloped o y how di e en algo i hms beha e using he same da abase and ack. The
ou pu o his p og am o di e en scena ios le us know which one would be he bes combina ion.
V. SIMULATIONS
Se e al es s ha e been made. Fo each one, a speci ic da abase, ack and algo i hm we e ied. The mos signi ican
change among di e en simula ion es s is how he signa u e da abase poin s we e calcula ed. Fi s ly, wo kinds o signa u e
da abase we e possible, one global and he o he di ec ional (no h, wes , sou h, eas ). Secondly, each poin in he signa u e
da abase was se calcula ing he mean, mode, max, min, e c, o all he sample poin s acqui ed p e iously.
As p esen ed in Sec ion II, in he o -line phase he acqui ed signa u e is compa ed wi h he ones sa ed in he signa u e
da abase applying he Manha an dis ance o de e mine he closes signa u e poin s (one o mo e depending on c pa ame e ) in
he signa u e da abase. In he E AAL compe i ion, he Euclidian dis ance was used o measu e he e o made, so we ha e also
ied his dis ance, bo h o compa e a signa u e wi h he signa u es da abase and o measu e he accu acy e o . The ac ual
posi ion is de e mined using he cen oid algo i hm. This algo i hm has he c pa ame e . Depending on i , he esul s we e
di e en . We ied om 1 o 1.7, highe alues had wo se esul s.
As one can image, he amoun o in o ma ion ob ained o be analysed and compa ed was oo much. Fo each signa u e
da abase ob ained you had o simula e he di e se ack o be analysed and c pa ame e s o be es ed, so he e we e hund eds o
combina ions. Manage all hese esul s app op ia ely, we go he nex conclusions.
VI. CONCLUSIONS
Fig. 7 and Fig. 8 shows espec i ely o a pa ame e c=1.3 he esul s ob ained using he same ack wi h a signa u e
da abase global o he signa u e da abase di ec ional. The abo e g aphic in Figs. 7 and 8 ep esen s he ela ionship be ween
he sample dispe sion in pe cen age and he e o made in cen ime es. This shows ha he elec ion o a speci ic da abase
oge he wi h a good coe icien c migh imp o e he esul s. The g aphics below ep esen s he success in AOI. The sco e was
simila o he E AAL compe i ion. 0 ailed, 1 success, and 0.5 ailed bu i was in a suba ea in he AOI. The conclusions a e
he same, changing pa ame e s migh imp o e esul s.
In o de o compa e adequa ely he di e en combina ions and o de e mine he bes , we decided o use he e o made in
he 75 h pe cen ile, i.e., he same c i e ia used in E AAL compe i ion. In Fig. 9 i is shown among all he es s made, he one

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which e o was below 365 cen ime es and in Fig. 10, i is shown he success in AOI o he bes esul s.
The bes esul s a e ob ained using a global signa u e da abase, whe e he poin s in i a e calcula ed using he medium o
he squa es o samples da abase wi h c=1.3 and he medium o samples da abase wi h c=1.4. The e o is espec i ely, 360 cm
and 361 cm. Al hough in he i s case, he e o is less, i does no imply a be e esul in he AOI success as shown in Figu e
10. Tha is no w ong because one calcula ed coo dina e could be close o he eal one bu ha coo dina e belongs o ano he
AOI. So i can be concluded ha using a global da abase using he medium o samples wi h c=1.3, he esul s ob ained in he
E AAL compe i ion a e imp o ed in one me e .
Finally, we ealized ha we did no measu e co ec ly he e o made by ou p o o ype and he i s Li ing Lab emula ion
we made when we es ed i be o e he compe i ion day, ha is why ou sco e in accu acy was wo se han we expec ed.
Fig. 7 C=1.3-di ec ional signa u e da abase
Fig. 8 C=1.3-global signa u e da abase
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Fig. 9 E o made in pe cen ile 75
Fig. 10 AOI accu acy
VII. ACKNOWLEDGMENT
This wo k has been ca ied ou wi hin he amewo k o wo esea ch p og ams: (P08-TIC-3631) – Mul imodal Wi eless
in e ace (IMI) unded by he Regional Go e nmen o Andalusia.
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