Implementation of an event-triggered smart sensor network architecture based on the IEEE 802.15.4 standard

IMPLEMENTATION OF AN EVENT-TRIGGERED SMART SENSOR NETWORK ARCHITECTURE BASED ON THE IEEE 802.15.4 STANDARD E ico Meneses Le˜ao ∗Luiz A onso Guedes ∗ F ancisco Vasques ∗∗ ∗Depa men o Compu e Enginee ing and Au oma ion - UFRN - Na al - B azil ∗∗ Depa men o Mechanical Enginee ing - Uni e si y o Po o - Po o - Po ugal Abs ac : A sma ansduce is he in eg a ion o a senso /ac ua o elemen , a p ocessing uni , and a ne wo k in e ace. Sma senso ne wo ks a e composed o sma ansduce nodes in e connec ed h ough a communica ion ne wo k. This pape p oposed an e en d i en a sma senso ne wo k a chi ec u e (asynch onous da a) and i s espec i e implemen a ion based in he IEEE 802.15.4 s anda d. The e en s a e de i ed om a da a comp ession algo i hm embedded in o he sma senso , which comp esses da a om he senso . The a chi ec u e also suppo s con igu a ion and moni o ing ac i i ies o all dis ibu ed sys em. Keywo ds: sma senso , ne wo k, a chi ec u e, e en s, IEEE 802.15.4, comp ession algo i hm. 1. INTRODUCTION Au oma ion ac i i ies a e essen ial o he com- pe i i eness inc ease in all indus ial sec o s. F om a sys emic app oach, indus ial au oma ion can be cha ac e ized as a se o echniques ha enable he cons uc ion o ac i e subsys ems wi h he capabili y o in e ac wi h he indus ial p ocesses o con ol, moni o ing, and supe ision p oposes. In he las ew decades, he au oma ion echnolo- gies ha e been e ol ing om a s ongly cen al- ized echnology o an essen ially dis ibu ed ech- nology. Wi hin his new app oach, componen s a e in e connec ed by digi al communica ion ne - wo ks. Thus, he adi ional senso s based in he 4-20mA s anda d a e being eplaced by digi al de ices. These de ices may ha e senso s, ac ua- o s, and con ol unc ionali ies and a e endowed o digi al p ocesso s and communica ion sys ems. Wi hin his con ex , a sma senso is de ined as he in eg a ion o an analog o digi al senso o an ac ua o elemen , a p ocessing uni , and a ne wo k in e ace (Elmen eich, 2006). In his way, he a chi ec u es o he lowes le el o indus ial au oma ion a e cha ac e ized o using a se o he sma ansduce s, usually connec ed h ough a communica ion ne wo k wi h eal ime p ope ies. The dis ibu ed app oach p o ides a signi ican imp o emen in he lexibili y and scal- abili y aspec s o he indus ial p ocesses; how- e e , i also b ough new scien i ic and echnolog- ical challenges, such as he need o new models and algo i hms o eal ime and sa e communi- ca ions, conside ing inancial and en i onmen al es ic ions. The sma senso design mus deal wi h in e - changes among de ices, and also wi h in e op- e abili y and a ailabili y o in o ma ion in eal ime. The ne wo ked ope a ion o a sma senso ha uses s anda dized in e aces allows sha ing in o ma ion and esou ces. Thus, i allows he in- eg a ion o all con ol and supe ision p ocesses, o example. The objec i e o his pape is o p opose an e en - igge ed sma senso ne wo k a chi ec u e and i s implemen a ion based IEEE 802.15.4 s an- da d (IEEE 802.15.4 S anda d, 2006). This A - chi ec u e is simila o he OMG (Objec Manage- men G oup) s anda d (Kope z and Wien, 2003). Howe e , di e en ly o he o iginal ime- igge ed OMG p oposal, he p oposed app oach uses an asynch onous e en - igge ed mechanism o ans- mi he da a om he sma senso s. This asyn- ch onous beha io is a consequence o he imple- men a ion o a da a comp ession algo i hm in o he sma senso s, which sends only he ele an da a om he aw da a mass. Thus, he p o- posed e en - igge ed app oach sa es an impo - an amoun o communica ion bandwid h. The es o he pape is o ganized as ollows: Sec ion 2 desc ibes he p oposed e en - igge ed sma senso ne wo k a chi ec u e and i s cha - ac e is ics. In o de o alida e he p oposed a - chi ec u e, he Sec ion 3 p esen s a p o o ype im- plemen a ion using he IEEE 802.15.4 s anda d and some expe imen al esul s ha we e ob ained om he p o o ype. The pape is concluded in Sec ion 4. 2. EVENT-TRIGGERED SMART SENSOR NETWORK ARCHITECTURE The p oposed e en - igge ed sma senso a - chi ec u e was b ie ly in oduced in (Le˜ao e al., 2007). In his pape , a mo e de ailed p esen- a ion is made. I is based on he OMG’s (Objec Managemen G oup) s anda d. The choice o his s anda d was mo i a ed by i s simple and well de ined da a access in e ace. Howe e , di e en ly o he ime- igge ed OMG s anda d (Kope z and Wien, 2003), he p oposed a chi ec u e ollows an e en - igge ed app oach. Tha is, he sma sen- so s send h ough he ne wo k only he ele an in o ma ion abou he p ocess. The ans e o his in o ma ion is igge ed by asynch onous e en s. Figu e 1 shows he basic componen s o he p o- posed a chi ec u e. This a chi ec u e is composed by clus e s. In u n, each clus e can ha e one mas e node and up o 255 sla e nodes, which a e in e connec ed by a ield ne wo k. The mas e node is a mos powe ul p ocessing de ice and i is designed o manage he clus e . I can also communica e wi h o he mas e nodes h ough a supe ision ne wo k. The e may be edundan shadow mas e s o suppo aul ole ance. Thus, i is he esponsible o managemen , con ol, and con igu a ion ac i i ies h oughou he sys em. ... Mas e DM Clien CP RTDB and DB ... Mas e DM RTDB and DB Supe isionNe wo k Clus e A Clus e B Clien RS FieldNe wo k Sla e Sla e Sla e Sla eSla eSla eSla eSla e Clien RS Clien CP Fig. 1. E en -T igge ed Sma Senso A chi ec- u e. 2.1 P oposed Mas e Node S uc u e The mas e node has wo da abase ypes: a eal ime da abase (RTDB) and a adi ional da abase (DB). The RTDB has o gua an ee he em- po al deadlines o i s ansac ions. Thus, some da a will be alid jus o a speci ic ime in e - al (Ramam i ham, 1993). The mas e node logs in i s da abases he mos ele an in o ma ion om he sma senso s. These da abases a e ac- cessed h ough he diagnos ic and main enance in- e ace (DM) using he supe ision ne wo k. The mas e node accesses in o ma ion om he sla e nodes using he RT clien ( eal ime clien ) and he CP clien (con igu a ion and planning clien ). 2.2 P oposed Sla e Node S uc u e A sla e node equi es wo ypes o in e aces o accessing i s da a, a comp ession algo i hm and a bu e o s o e empo a y da a. Such empo a y da a will be applied o he da a comp ession al- go i hm, as shown in Figu e 2. The comp ession algo i hm is esponsible o selec ing he ele an da a in o ma ion, and hus i gene a es an asyn- ch onous low o da a. This beha io leads o un- p edic able iming in e als be ween consecu i e da a ans e s om he senso . Howe e , he ime o sending a da um is no o ally unp edic able, due o he minimum and maximum ime p o ided he comp ession algo i hm o he ansmission, as will be shown ahead. So, an e en - igge ed app oach would be e icien o he communica ion o hese con ol- ela ed da a. The sma senso will ha e a la ge au onomy, as i will send only he ele an da a. The e o e, he e is a signi ican educ ion o he ans e ed da a, comp ession Algo i hm Bu e RS In e ace CP In e ace Fig. 2. Sma T ansduce wi h a Embedded Com- p ession Algo i hm. esul ing in a smalle bandwid h u iliza ion. This way, i is possible o connec a la ge amoun o sma senso s o he ne wo k. Howe e , he sma senso s become mo e complex and he e o e de- mand a la ge p ocessing capabili y. Ne e heless, wi h he g owing echnological p og ess i is pos- sible o design low-cos sma senso s wi h high p ocessing capabili ies. Being so, he embedded comp ession algo i hm is one o he esea ch a - ge s ha mus be add essed o he a chi ec u al ne wo k o e en - igge ed sma senso s. Senso s equi e wo ypes o in e aces o access he da a (RS and CP), whe e he communica ion is suppo ed by wo di e en communica ion mod- els (publishe -subsc ibe and clien -se e ): •RS in e ace - eal- ime se ice in e ace. I is used o ans e eal- ime da a o he clus e . The da a gene a ed by he sma senso s will be published in he ne and consumed by he unc ions o he sys em. •CP in e ace - con igu a ion and planning in e ace. Th ough his in e ace i is possible o iden i y new nodes connec ed o he ne - wo k, o ans e new con igu a ion pa ame- e s o he senso , as alues o comp ession de ia ion, maximum and minimum ime o he comp ession algo i hm, besides in o ma- ion as he iden i ica ion o he senso in he dis ibu ed sys em. 2.2.1. Publishe -Subsc ibe Model The commu- nica ion model used by he RS in e ace is he eal- ime publishe -subsc ibe model (RTPS). This model o exchanging da a a o s he message ex- change wi h ime pa ame e s amid de ices be- ween wo en i ies: he publishe , esponsible by sending he messages, and he subsc ibe s, espon- sible o consuming hese messages, in case hey in e es hem (Dolejs e al., 2004; Oce a, 2002). The messages sen by a sla e node h ough he RS in e ace will be consumed by he a ious unc- ions o he sys em conce ned by such da a. These messages can be send as commands o he ac u- a o s o s o ed in da abases in acco dance wi h hei equisi es; hey can be eal- ime da abases (RTDB) o adi ional da abases (DB), as shown in Figu e 3. Senso RS subsc ibe s publishe diagnos ic() con ol_ unc ion() senso _his o ic() ope a ion_ ime() Con olle DB RTDB RTDB Fig. 3. Publishe -Subsc ibe Model o P oposed A chi ec u e. The da a packe s sen by sma senso s h ough he RS in e ace con ain a ious ields wi h impo - an in o ma ion om he sma senso s, which will be consumed by he sys em unc ions o di - e en inali ies. Fo example, he ope a ion ime() unc ion will access he TIME ield and will s o e his da um in he eal- ime da abase. Such unc- ion in o ms he supe ision ne wo k abou he unc ioning ime o a de e mined sma senso . The senso his o ic() unc ion will access he in- o ma ion in he DATA ield, in o de o make a ailable he his o ic ile o lags o a sma sen- so . The packe ’s TAG ield is esponsible o iden- i ying he senso ; he TYPE ield by he iden i i- ca ion o he ype o he packe o be ans e ed, while he QUALITY ield will con ain in o ma- ion abou he quali y lag o he da um (good, egula , bad, no de e mined). The CRC ield is esponsible o con olling he packe ’s e o . 2.2.2. Clien -Se e Model The CP in e ace is accessed di ec ly by he mas e node, which can pe o m con igu a ion ac i i ies, can exchange pa- ame e s and can econ igu e o new nodes (Fig- u e 4). As an example o he con igu a ion o a new node, a mas e node h ough i s unc ion new de ice() s ays moni o ing he sys em in o de o sea ch new nodes connec ed o he ne wo k. Thus, a new sma senso will ask i s inclusion in he sys em and will be gi en by he mas e node, h ough he unc ion con igu e(), he necessa y pa ame e s o s a unc ioning in he ne wo k. F om his poin onwa ds, a e con i ming he e- cei ed pa ame e (con i m()) and ha ing ecei ed he au ho iza ion o ans e i s da a (s a ()), he senso will s a sending i s da a packe s h ough he RS in e ace. Figu e 5 shows he econ igu a ion p ocedu e o a sma senso . The mas e node using he eques econ igu e(ID) unc ion econ igu es he pa ame e o a pa icula sla e node. Then, he sla e node is au ho ized o send i s packages. Some impo an sys em unc ions a e p esen ed as ollows: • eques _inclusion(): sla e node equisi es o mas e node i s inclusion in he ne wo k. CP con igu e() ... (Pa ame e s) (Id) Mas e Sla e eques _inclusion() econ igu e() Fig. 4. Clien -Se e Model o P oposed A chi- ec u e. MASTER SLAVE eques _ econ igu e() econ igu e(pa ame e s) con i m() s a () ansmi (packe ) ansmi (packe ) Fig. 5. Recon igu a ion P oceeding o a Sla e Senso . •con igu e(): mas e node sends con igu a- ion pa ame e s o a new sla e node. • econ igu e(): mas e node sends econ ig- u a ion o a sla e node. • ansmi (): sla e node sends a de e mined packe o he ne wo k. •ope a ion_ ime(): esponsible o he ime unc ioning o a sma senso . 3. CASE STUDY This sec ion p esen s a case s udy implemen a- ion o he p oposed e en - igge ed sma sen- so ne wo k a chi ec u e. The implemen a ion uses he IEEE 802.15.4 (IEEE 802.15.4 S an- da d, 2006) s anda d as communica ion in as- uc u e. The p o o ype has been implemen ed upon he F eescale Semiconduc ion de elopmen ki . 3.1 Implemen a ion based on IEEE 802.15.4 The IEEE 802.15.4 s anda d de ines he physical and MAC laye s o low cos , low powe , and low a e de ices (Ba on i e al., 2007). The embedded so wa e was de eloped in acco dance o SMAC (Simple MAC ) p emisses. 3.2 De ini ion o he Tes En i onmen Figu e 6 shows he en i onmen de ined o ex- pe imen ally assess his a chi ec u e, whe e i is de ined wo sla e sma nodes and one mas e sma node connec ed h ough a ield ne wo k. The supe ision sys em can manage he sma senso ne wo k h ough he mas e node. RS-232 RS-232 RS-232 MASTER SLAVERS DB Senso Values Senso Values Fig. 6. S uc u e o a Sma Senso . Fo ou applica ion, a eal senso has been em- ula ed using a PC equipped wi h a se ial po and he ne wo k in e ace has been a wi eless communica ion boa d o F eescale. To implemen he emula ion p ocess o he eal senso s, we used wo da abases con en eco ds ex- ac ed om a eal- ime gas dis ibu ion moni o - ing sys em; speci ically, we choose an ou le a i- able, wi h 10000 eco ds. Fo es ing e ec s, a p o- g am w i en in ANSI C was de eloped, i is capa- ble o ead each eco d om he da abase and o send i h ough RS-232 po (se ial inpu /ou pu ) o he wi eless communica ion boa d o F eescale. The mas e node, on he o he hand, is composed o a F eescale wi eless communica ion boa d con- nec ed in a PC equipped wi h a so wa e able o s o e he ecei ed alues by mas e boa d in he da abase and gene a es he cu e o he ele an alues o each sla e senso connec ed upon he ield ne wo k. 3.3 De ini ion o he P o o ype Func ions The sla e nodes suppo he plug-and-play pa adigm. This pa adigm is easily implemen ed because each sla e node has an only MAC add ess as i s iden i i- ca ion on he ne wo k. Thus, he mas e node can de ec he sla e nodes in au oma ic way. When a sla e node is connec ed o he ne wo k i equi es o he mas e node i s con igu a ion. Then he mas e node eply wi h he ollowing pa ame e s: senso ID, minimum and maximum ime and com- p ession de ia e o he comp ession algo i hm. All he sla e nodes ha e an implemen a ion o he comp ession algo i hm Swinging Doo (B is ol, 1990). Thus, when a new da a is gene a ed, he comp ession algo i hm decides i i is ele an o no , hen his da a can be send o no o he mas e boa d. To send a da a o mas e node, he sla e node mus build a packe wi h he ollowing ields: senso iden i ica ion (ID), cu en ime and alue. Then, when he mas e node ecei es any da a om he sla e node, i mus s o e ha da a in i s da abase o he ac i i ies o supe ision ne wo k. Ano he impo an unc ion implemen ed in he p o o ype is he econ igu a ion o a sla e senso . Fo example, h ough he mas e node, i is pos- sible o econ igu e he comp ession algo i hm o any sma senso connec ed o ield ne wo k. 3.4 Tes Scena ios To alida e he p o o ype, we de ined wo es scena ios. Theses scena ios a e desc ibed as ollow. 3.4.1. Scena io 1 The scena io 1 is de ined as ha ing only one sma senso connec ed o he ne wo k wi h pa ame e s econ igu a ion. In ha way, he sla e senso eques s o he mas e node i s ini ial con igu a ion. The ollowing pa ame e s a e passed o he sla e node: •ID: 1; •Minimum ime: 3 s; •Maximum ime: 10 s; •Comp ession de ia e: 5 m3/day. The sla e senso is econ igu ed a e 3150 sec- onds. The new con igu a ion o he sla e senso is isualized as ollow: •Minimum ime: 5 s; •Maximum ime: 20 s; •Comp ession de ia e: 9 m3/day. 3.4.2. Scena io 2 The scena io 2 de ines he execu ion o wo sla e senso , in which he second sla e senso is connec ed 2000 seconds la e han i s sla e senso . Sla e senso 1 is con igu ed wi h he pa ame e s as ollow: •ID: 1; •Minimum ime: 3 s; •Maximum ime: 10 s; •Comp ession de ia e: 5 m3/day. Sla e senso 2 is con igu ed wi h he pa ame e s as ollow: •ID: 2; •Minimum ime: 5 s; •Maximum ime: 20 s; •Comp ession de ia e: 5 m3/day. The mas e node is esponsible by s o ing he g aph gene a ion senso da a wi h he ele an da a om each sma senso . The sma senso 1 uses he da a s o ed in da abase 1 while sma senso 2 uses he da a s o ed in da abase 2. 3.5 Resul s This sec ion p esen s he ob ained esul s om all es scena ios desc ibed abo e. Figu es 7 and 8 show he aw da a om he senso 1 and senso 2, espec i ely. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 460 480 500 520 540 560 580 600 620 640 660 680 O iginalDa a ins an (seconds) alues(cubicme e s/day) O iginalValues Fig. 7. O iginal Da a om he Sla e Senso 1. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 540 560 580 600 620 640 660 680 O iginalDa a ins an (seconds) alues(cubicme e s/day) O iginalValues Fig. 8. O iginal Da a om he Sla e Senso 2. 3.5.1. Scena io 1 In his scena io was de ined he ope a ion o only one sla e wi h econ igu a- ion du ing i s ac i i y. The supe ision sys em eques s he econ igu a ion o he sla e node com- p ession pa ame e s 3150 seconds a e i s ini ia- ion. Fo his scena io, i is calcula ed he com- p ession a es and he mean squa e e o s o all senso ac i i y and be o e senso econ igu a ion and a e senso econ igu a ion ac i i ies. Figu e 9 shows he sla e senso ele an da a. Fo his scena io he comp ession o all senso ac i i y was 90.76% wi h a mean squa e e o o 44.25. The comp ession a e be o e he econ igu a ion was o 85.52% wi h a mean squa e e o o 86.76. A e he sla e senso econ igu a ion, he comp ession a e was o 93.16% wi h a mean squa e e o o 24.71. 3.5.2. Scena io 2 In his scena io was de ined he ope a ion o wo sla e sma senso . The sla e 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 460 480 500 520 540 560 580 600 620 640 660 680 Comp essedValues Comp essionRa eo 90.76% ins an (seconds) alues(cubicme e es/day) Fig. 9. Rele an Da a o Sla e Senso 1 wi h econ igu a ion. senso 2 was connec ed 2000 seconds a e he sla e senso 1. Fo his scena io, i is calcula ed he comp ession a e and he mean squa e e o o each sla e senso . The comp ession a e o sla e senso 1 was 85.80% wi h he mean squa e e o o 41.13. The comp ession a e o sla e senso 2 was 90.16% wi h he mean squa e e o o 32.58. Figu e 10 and 11 shows he ele an da a o he 1 and 2 sla e senso , espec i ely. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 460 480 500 520 540 560 580 600 620 640 660 680 Comp essedValues Comp essionRa eo 85.80% ins an (seconds) alues(cubicme e es/day) Fig. 10. Rele an Da a o Sla e Senso 1. 4. CONCLUSION The pape p esen ed an e en - igge ed sma sen- so ne wo k a chi ec u e and i s implemen a ion based IEEE 802.15.4 s anda d. The main ea u e o his a chi ec u e is he in eg a ion o a da a comp ession algo i hm wi h simple implemen a- ion in o he sma senso s. This app oach sa es ne wo k bandwid h, because he senso s only send ele an da a. 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