Meerkats: A Power-Aware, Self-Managing Wireless Camera Network for Wide Area Monitoring

Transcription

1 Mrkats: A Powr-Awar, Slf-Managing Wirlss Camra Ntwork for Wid Ara Monitoring C. B. Margi 1, X. Lu 1, G. Zhang 1, G. Stank 2, R. Manduchi 1, K. Obraczka 1 1 Dpartmnt of Computr Enginring, Univrsity of California, Santa Cruz 2 Volkswagn of Amrica, Palo Alto {cintia,xylu,gfan,manduchi,katia}@so.ucsc.du, ganymd@stank.ch Abstract W introduc Mrkats, a wirlss ntwork of battryopratd camra nods that can b usd for monitoring and survillanc of wid aras. On distinguishing fatur of Mrkats (whn compard, for xampl, with systms lik Cyclops [1]) is that our nods ar quippd with sufficint procssing and storag capabilitis to b abl to run rlativly sophisticatd vision algorithms (.g., motion stimation) locally and/or collaborativly. In prvious work [9, 8, 7] w analyzd th nrgy consumption charactristics of th Mrkats nods undr diffrnt duty cycls, involving diffrnt powr stats of th systm s componnts. In this papr w prsnt an analysis of th prformanc of th survillanc systm as a function of th imag acquisition rat and of th synchronization btwn camras nods. Our ultimat goal is to optimally balanc th trad-off btwn application-spcific prformanc rquirmnts (.g., vnt miss rat) and ntwork liftim (as a function of th nrgy consumption charactristics of ach nod). 1 Introduction Thr ar many applications of scintific, social, and stratgic rlvanc that rquir monitoring of vnts in wid aras ovr long priods of tim. Continuous and prvasiv monitoring oftn ncssitats a larg numbr of ntworkd snsors. Wiring th snsor ntwork for powr and communication is, in most cass (.g., outdoors), too xpnsiv and not practical, hnc th nd for battry-opratd, wirlss dploymnts. Thr is widsprad agrmnt within th snsor ntwork community that multi-tir dploymnts, comprising both low- and high-lvl snsors, such as camras, hav grat potntial for a wid-rang of currnt and upcoming applications. Visual snsors can covr largr filds of viw and xtract mor substantial information about th scn than othr simplr snsors such as tmpratur, humidity, and prssur [4, 1, 2, 11, 6]. Givn that camras ar considrably mor powr-hungry than simplr, lowr-lvl snsors, visual snsor systms push th nvlop of nrgy consrvation in snsor ntworks vn furthr. This papr concntrats on a spcific wirlss ntworks (Mrkats) dvlopd at UCSC, consisting of battry opratd snsor nods quippd with wbcams. Each nod is basd on th Stargat board, and th nods communicat using 82.11b. Evn though tchnology advancs nabl mor procssing powr and storag capability with smallr form factors, w contnd that application rquirmnts incras at th sam or highr rats. Thrfor, our prmis is that fforts in dvloping low-powr platforms (.g., Cyclops [1]) and rsarch in fficint rsourc managmnt such as Mrkats ar complmntary to on anothr. Our main challng in Mrkats is to optimally balanc th trad-off btwn application-spcific prformanc rquirmnts (.g., vnt miss rat) and ntwork liftim. For xampl, a highr imag acquisition rat lads to bttr prformancs but shortr liftim, du to incrasd nrgy consumption. Diffrnt stratgis for data procssing and transmission also influnc this trad-off. Procssing an imag bfor transmission, in ordr to prform vnt dtction, isolat a rgion of intrst, and xtracting faturs such as motion flow, may rduc th amount of data bing transmittd. But local procssing is nrgy consuming in itslf, and th savings in transmission nrgy may b offst by th additional nrgy rquird for procssing. Hnc, th main focus of our work is in th charactrization of prformanc and nrgy consumption for a givn rsourc managmnt stratgy. Each nod oprats according to a spcific duty cycl, switching its componnts (camra, procssor, radio) into diffrnt oprational stats (slp, idl, activ), and prforming spcifid tasks. Each spcific duty cycl rquirs a crtain tim to complt and uss a crtain amount of nrgy. Likwis, for a crtain camra placmnt, a spcific duty cycl dtrmins th probability that a moving objct in th nvironmnt (.g., and intrudr) is missd, maning that it transitd through th camra fild of viw without a pictur bing takn of it. A thorough charactrization of nrgy consumption and xcution tims for diffrnt oprational duty cycls was givn in [9, 8, 7]. In this work, w concntrat on prformanc analysis. This papr is organizd as follows. Sc. 2 dscribs our tstbd, highlighting its hardwar and softwar componnts. Sc. 3 introducs a modl for prformanc analysis for a singl nod as wll as for cooprating nods. Sc. 4 has th conclusions. 2 Th Mrkats Tstbd Currntly, th Mrkats tstbd is composd of ight visual snsor nods, ach of which consists of a Stargat, battry, wbcam, and IEEE 82.11b wirlss card. A laptop acts as th information sink.

2 Figur 1. Visual snsing nod in th Mrkats tstbd R s o u r c M a n a g r Visual Procssing Mrkats Visual Snsor Nod Hardwar Communication Figur 2. Mrkats softwar organization. 2.1 Hardwar Th Mrkats nod (shown in Fig. 1) is basd on th Crossbow s Stargat 1 platform, which has an XScal PXA255 CPU (4 MHz) with 32MB flash mmory and 64MB SDRAM. PCMCIA and Compact Flash connctors ar availabl on th main board. Th Stargat also has a daughtr board with Ethrnt, USB and srial connctors. W quippd ach Stargat with an Orinoco Gold 82.11b PCMCIA wirlss card and a Logitch QuickCam Pro 4 wbcam connctd via USB. Th QuickCam can captur vido with rsolution of up to 64x48 pixls. W us a customizd 7.4 Volt, 1 Ah, 2 cll Lithium-Ion (Li-Ion) battry and an xtrnal DC-DC switching rgulator (with fficincy of about 8%). Th oprating systm is Stargat vrsion 7.3 which is an mbddd Linux systm (krnl ). Th choic of Crossbow s Stargat as th Mrkat s nod main componnt was basd on a numbr of considrations. First, sinc our focus is not on hardwar dsign, it mad sns to us off-th-shlf componnts. Choosing a platform that runs an opn sourc oprating systm was also an important factor. And, sinc w slctd a wbcam as th visual snsor, w ndd a board with a USB connctor. Finally, w ndd a platform that providd rasonabl procssing and storag capabilitis. An important fatur providd by th Stargat is its battry monitoring capability. This is achivd through a spcializd chip (DS2438) on th main board. Two krnl moduls provid accss to th battry monitor chip and rtriv information about th battry s currnt stat. 2.2 Softwar Th Mrkats nods softwar organization, shown in Fig. 2, consists of thr main componnts, namly th Rsourc Managr, Visual Procssing, and Communication moduls Rsourc Managr Th Rsourc Managr is th main thrad of control running on th Mrkats nod. It controls th activation of th 1 wbcam and wirlss ntwork card in ordr to prform imag acquisition/procssing and communication-rlatd tasks (.g., transmit an imag), rspctivly, as ndd. For nrgy consrvation, th Rsourc Managr has th Mrkats snsor nod oprating on a duty-cycl basis, i.., th nod priodically waks up, prforms som task as ndd, and gos back to ithr idl or slp mod. Whras slp is th mod with th lowst powr rquirmnts, idl mod has a numbr of variations. At a minimum, th procssor is awak and rady to work, vn though thr ar no activ procsss running. Th othr variations of idl ar: procssor and wirlss ntwork card rady; procssor and wbcam rady; and procssor, wirlss ntwork card, and wbcam rady. Ths variations corrspond to th cass whr th nod is rady to ngag in communication-rlatd tasks, imag acquisition/procssing tasks, or both. An accurat powr consumption analysis for th diffrnt lmntary tasks forming a duty cycl, along with a numbr of diffrnt duty cycl configurations and rlatd nrgy masurmnts, was prsntd in [8] Visual Procssing Th Visual Procssing modul prforms all vision-rlatd tasks, including imag acquisition, comprssion, and procssing. It is invokd by th Rsourc Managr aftr th wbcam has bn activatd. Th goal is to dtct vnts, in th form of moving imag. Upon compltion, Visual Procssing rturns control to th Rsourc Managr with a paramtr flagging whthr an vnt has bn dtctd, as wll as a st of paramtrs including th numbr of moving blobs in th imag and th vlocity of ach blob. If an vnt is dtctd, th rlvant portion of th imag is JPEG comprssd and transmittd to th sink. Moving blobs in th imag ar dtctd using a fast motion analysis algorithm dscribd in [5]. Th algorithm is comprisd of thr stags. First, local diffrntial masurmnts ar usd to dtrmin an initial labling of imag blocks, using a total last squars approach with fast implmntation. Thn, blif propagation is usd to impos spatial cohrnc and rsolv aprtur ffct inhrnt in txturlss aras. Finally, th vlocity of th rsulting blobs is stimatd via last squars rgrssion. On th Mrkats nod, th motion analysis algorithm, applid on a pair of conscutiv imags, taks about.9 s and consums.16 C (Coulomb) Communication Communication btwn nods and th sink is basd on 82.11b links. Multi hop routing is prformd using th Dynamic Sourc Routing (DSR) protocol [3]. This is an ondmand routing mchanism spcially dsignd for multihop wirlss ad-hoc ntworks. Th vrsion of DSR running on th Mrkats nods was portd from th DSR krnl modul availabl for th PocktPC Linux [12]. Two typs of data ar handld by th application layr in our currnt implmntation: control packts (xchangd btwn nods via UDP for synchronization and alrting); and imag data (transmittd from nods to th sink via TCP). Th sink runs a multithradd srvr program that listns for 2 Ths masurmnts wr obtaind using an HP 341A digital multimtr connctd to th board.

3 connction rqusts from snsor nods, opns a connction, rcivs imag fils and rndrs imags on th sink s consol. In our xprimnts, w obsrvd sporadic instability problms using th 82.11b links. In ordr to minimiz th ffct of this instability, w implmntd a simpl fault rcovry procdur. Whn control packts ar transfrd via UDP, th rcivr is rquird to snd an acknowldg mssag (ACK) back to sndr. If within in a fixd priod of tim th sndr dosn t rciv an ACK from th rcivr, th sndr r-snd th sam control mssag during th nxt duty cycl. In th cas of imag data bing snt from a camra nod to th sink via TCP, a timr is st up at th sndr to monitor th stablishmnt of a TCP connction. If th TCP connction is not built within a fixd priod of tim, th sndr considrs that transmission faild, and tris to st up a TCP connction again in th nxt duty cycl. With ths simpl procdurs, th rliability of data transmission ovr 82.11b is at an accptabl lvl for our xprimnts Mastr Slav Coordination Two nods may coordinat whn tracking a moving objct in th scn. In our xprimnts w considrd a simpl mastr slav scnario. Th mastr nod acquirs and procsss imags on a rgular basis. If it dtcts an vnt, it snds a short alrt packt to th slav nod. Unfortunatly, Mrkats nod ar not intrruptibl whil in slp mor, and thrfor th slav nod nds to priodically wak up and listn for mssags from th mastr; if it rcivs an alrt packt, it taks and comprsss an imag. Fig. 3 shows th synchronization stratgy usd in our tst. Th mastr nod acquirs imags at tims kt, whr T is th cycl priod. If an vnt is dtctd at th n th cycl, an alrt mssag is snt at tim nt + T 2, whr T 2 is a known constant. Th slavs listns for mssags during a tmporal window starting at tims kt + T 2. Th siz of this window dpnds on th xpctd dlay, as wll as on th uncrtainty of clock synchronization btwn th two nods. MASTER Imag captur+proc. Radio SLAVE Imag captur+proc. Radio No vnt dtctd Evnt dtctd nt nt+t 2 (n+1)t (n+1)t+t 2 Figur 3. Th synchronization schm for mastr/slav organization. Fig. 4 shows th currnt drawn by th mastr and slav nods ovr two cycls, without and with an vnt dtctd. Not that in this particular xprimnt, th slav startd in idl mod (rathr than in slp mod), but thn is put to slp at th nd of ach subsqunt cycl. 3 Prformanc Analysis Th goal of th ntwork is dtct and track moving bodis within th ara covrd. Idally, vry tim a body ntrs th fild of viw (FOV) of a camra, th camra would tak on or mor imags of it. Th visual data is usd for vnt dtction, data transmission in th chosn rprsntation, and Currnt (A) Currnt (A) Tim (s) Rmov wirlss modul Rmov wirlss modul Tim (s) Listn for mssags Idl Figur 4. Th tim profil of currnt drawn for th mastr (top) and slav (bottom) masurd ovr a cycl with no vnt dtctd and a cycl with an vnt dtctd. activation of narby nods which ar likly to s th body nxt. Howvr, du to th finit acquisition rat of th camras, it is possibl that a moving body travrss a camra s FOV without bing dtctd, and it is thrfor important to assss th probability of this occurrnc. In our notation, th prsnc of a moving body in th ntwork is dnotd by th vnt X 1. If th body ntrs th i th camra FOV (FOV i ), w will say that th vnt Fi 1 occurrd. Evry tim a body circulating in th ara covrd by th ntwork ntrs th i th camra s FOV and is not dtctd, w will say that a miss vnt for camra i occurrd, dnotd by Mi 1. Mor in gnral, on may considr th cas of n bodis circulating in th ntwork (vnt X n ), r of which ntr th FOV i at som point (vnt Fi r ), with th i th camra missing k of th bodis in its FOV (vnt Mi k ). W can safly assum that Mi k is indpndnt of X n givn Fi r (sinc objcts outsid th camra s FOV cannot b dtctd anyway), so that P(Mi k Fr i,xn ) = P(Mi k Fr i ). W will furthr assum that P(Mi k Fr i ) is binomial, maning that ach miss vnt is indpndnt from th othrs. This maks sns in th cas of rar vnts, that is, whn two bodis ar unlikly to appar at th sam tim in th sam FOV. W will also postulat that P(Fi r X n ) is binomial, a rasonabl assumption in th cas of indpndntly moving bodis. A possibl masur of prformanc of a camra nod is th ratio of th xpctd numbrs of miss vnts to th xpctd numbr of bodis in th ntwork ( miss rat ): MR i = E[M i ]/E[X], whr th E[ ] rprsnts th xpctation oprator. Lt P M F = P(Mi 1 F1 i ) and P F X = P(Fi 1 X 1 ). Using th total probability thorm, and rmmbring that th conditional distributions of intrst ar binomial, it is not difficult to show that MR i = P M F P F X. In th nxt two subsctions w will show how, in som Load wirlss modul Listn for mssags Transmit alrt mssag

4 practical situations, th two factors P M F and P F X can b stimatd for nd nods (nods that ar at th dg of th ntwork, or nar points of high flux, such as nar an ntry door) and intrnal nods, which ar normally in th nighborhood of on or mor nd nods. 3.1 End Nods Considr th cas of Fig. 5(a), with a camra placd nar a door, or an ara of rlativly high flow. For simplicity s sak, w rprsnt a FOV as a triangl, which approximats th trac of th actual FOV assuming that th camra is not too high on th ground. If th camras ar high (.g. on th ciling) pointing down, thn th FOV tracs will tak diffrnt shaps. W will assum that prsons walk through th door at tims that ar modld by a Poisson point procss of unknown dnsity λ. W furthr assum that prsons walk through th door in a rctilinar motion, with constant but unknown vlocity v and orintation φ that can modld by suitabl probability distributions p v (v) and p φ (φ). For xampl, in our simulations w modl v as a truncatd Gaussian random variabl, and φ as a uniform random variabl. Not that prior information on th vlocity is oftn availabl (.g. th avrag spd of walking). W furthr assum that th orintation and th vlocity of motion ar statistically indpndnt. As shown in Fig. 5, th dirction of motion φ dtrmins th lngth l 1 (φ) of th path from th door to FOV i, and th lngth l 2 (φ) of th path ovrlapping FOV i. Togthr with th vlocity v, ths path lngths dtrmin th amount of tim t 1 (φ,v) = l 1 (φ)/v that it taks to go from th door to FOV i, and th amount of tim t 2 (φ,v) = l 2 (φ)/v th moving prson will b within FOV i. Lt Φ b th st of orintations that ovrlap FOV i. Thn, P F X = R φ Φ p φ(φ) dφ. As for P M F, th probability of misdtction givn that th prson walks in th camra s FOV, it dpnds on th imag acquisition policy of th camra. Undr priodic imag acquisition (with priod T i ), a prson walking through FOV i is not dtctd if, for som m, mt i < t in < t out < (m+1)t i, whr t in = t + t 1 (φ,v) is th tim at which th prson ntrs FOV i, t out = t +t 1 (φ,v)+t 2 (φ,v) is th tim at which th prson xits FOV i, and t is th tim at which th prson walks through th door. Sinc t is, by hypothsis, an outcom of a Poisson procss, it is not difficult to s that th condition abov is vrifid with probability 1 min(t 2 (φ,v),t i )/T i. Hnc: Z Z ( P M F = p φ (φ)p v (v) 1 min(t ) 2(φ,v),T i ) dv dφ φ Φ v T i (1) Fig. 5(b) shows th rlationship btwn th imag acquisition priod T i and th miss rat MR i for th situation in Fig. 5(a). This information (possibly containd in a look up tabl) can b usd by th Rsourc Managr to dcid a suitabl imag acquisition rat for th camra. In practic, th paramtrs ndd to stimat th miss rat ar known only with a crtain dgr of approximation. Ths paramtrs includ th location and orintation of th camra, as wll as th statistical distribution of orintation and vlocity. Th hypothsis of rctilinar motion at MR i Fram rat 1/T i [1/s] (a) (b) Figur 5. (a): A possibl layout of an nd nod. Th triangular shap rprsnts th nod s FOV, whil th angular sctor rprsnts th possibl dirctions of motion. (b): Th miss rat as a function of th imag acquisition rat (1/T i ). constant spd may not always b accurat. Howvr, uncrtainty about th camra gomtry can b takn into account by suitabl modification of th xprssions for P F X and P M F. Likwis, uncrtainty about th actual distributions of v and φ can b modld by incrasing th varianc of th modl distributions Intrnal Nods An intrnal camra nod is normally alrtd about th possibl arrival of a moving body by on or mor othr nd or intrnal nods. Of cours, in addition to this ractiv bhavior, an intrnal nod may also follow a policy of rgularly timd imag acquisitions, to account for moving bodis that may hav bn missd by othr narby nods. An vnt dtction by th i th nod at tim t is accompanid by som gomtric information about th moving body. At a minimum, it is known that a moving body was locatd within FOV i at tim t. Diffrnt lvls of gomtric information may b xtractd by visual analysis, including: th orintation of motion with rspct to th camra axis; th actual position of th body in th ground plan; th dirction of motion; th vlocity of th body. Givn th gomtric information availabl (togthr with its uncrtainty), and th location and orintation of narby camras (which may also b known to a dgr of uncrtainty), th control algorithm (distributd or localizd at th i th nod) nds to dcid: (1) Which (if any) narby camras nd to b alrtd; (2) How many imags ach of such camras should tak; (3) What ar th optimal tims for th imag acquisition. Intuitivly, if a vry rliabl prdiction of th body s motion could b mad, only th camra whos fild of viw will b intrsctd nxt by th body s path should b alrtd, and just on imag should b takn at any tim th body is within this fild of viw. Du to uncrtainty in th knowldg of th camra and moving body gomtry, this prdiction will b only approximat, maning that mor than on camras may nd to b alrtd, and mor than on imag may hav to b takn. Our stratgy is to comput, for ach narby camra of indx j, th miss rat MR j as a function of th numbr of imags N j it may tak, and of th tims t j = {t j,1,...,t j,nj } at which th imags ar takn. For ach valu of N j, th tims t j that minimiz th corrsponding miss rat can b 3 Not that p φ (φ) and p v (v) can, in principl, b larnd by analyzing th data collctd by th camra.

5 t j, MR j (a) (b) (c) Figur 6. (a): A possibl layout of an intrnal nod. An vnt has bn dtctd at tim t by anothr snsor, which stimatd that a body is moving within th spcifid angular sctor. (b): Th miss rat as a function of th tim t j,1 at which a singl imag is takn by th camra. (c): Th miss rat as a function of th tims at which two imags t j,1 < t j,2 ar takn by th camra (th cross rprsnt th pair of tim instants that minimiz th miss rat). computd, rsulting in th optimal (dcrasing) squnc of valus MR j (N j ). Basd on this knowldg, th control algorithm can allocat th numbr of imags to b takn by ach camra, balancing th nd for a low miss rat with th availabl nrgy at ach nod. A simpl xampl of computation of th squnc MR j (N j ) in th cas of on narby nod dtcting an vnt is shown in Fig. 6. For simplicity, w assum that th location of th body at tim t is known xactly, that th body is moving of rctilinar motion at constant spd, and that th distribution of vlocitis, p v (v) and of dirctions, p φ (φ) ar modld in th sam way as in Sc Again, not that any lvl of uncrtainty (about th location in which th body was sn, th motion dirction and vlocity) can b injctd in our modl by suitably modifying th probability distribution involvd. Fig. 6(b) shows th plot of MR j (1) as a function of th imag acquisition tim t j,1 for th cas of Fig. 6(a). In this cas, MR j (1) =.63. (For dtails about this computation, plas s [1].) In ordr to rduc th miss rat, th nod might tak two snapshots (at an incrasd nrgy cost). Th problm is thn to comput th optimal tims t j,1 < t j,2. Fig. 6(c) shows th miss rat as a function of (t j,1,t j,2 ), with th optimal pair rprsntd by a cross. Th corrsponding miss rat, MR j (2), is qual to.43; as xpctd, it is smallr than MR j (1). This simpl xampl shows how, at last in principl, it is possibl to optimiz th acquisition tim whn coordinating tracking of a moving body. Howvr, in th practical implmntation of this schm, a numbr of systm rlatd constraints must b takn into account. For xampl, as discussd in Sc , a slav Mrkats nod can rciv control and alrt packts from a mastr nod only in spcific tim windows. Th intrval btwn two conscutiv rcption windows indirctly affcts both th miss rat (th optimal imag acquisition tim may b missd du to a long intrval) and th nod lif tim (a short intrval limits th amount of tim in which a slav nod can b kpt in slp mod). t j,1 t j,1 4 Conclusions This papr introducd Mrkats, a wirlss ntwork of battry-opratd camra nods that can b usd for monitoring and survillanc of wid aras. Our work focuss on th analysis of th trad-offs btwn prformanc and ntwork liftim. In this papr, w concntratd on simpl modls that rlat th miss rat with th imag acquisition rat of th camra nods, as wll as on th synchronization btwn cooprating nods. Our nxt stp will b to intgrat ths modls into th Rsourc Managmnt modul, whos purpos is to slct suitabl duty cycls for th diffrnt nods in ordr to nsur th rquird miss rat whil maximizing th ntwork liftim. 5 Rfrncs [1] J. Boic, X. Lu, C. B. Margi, G. Stank, G. Zhang, R. Manduchi, and K. Obraczka. Mrkats: A Powr-Awar, Slf-Managing Wirlss Camra Ntwork for Wid Ara Monitoring. Tchnical Rport ucsccrl-5-4, Univrsity of California Santa Cruz. rsarch/rports/ucsc-crl-5-4.pdf. [2] W.-C. Fng, B. Cod, E. Kaisr, M. Sha, W.-C. Fng, and L. Bavoil. Panopts: Scalabl low-powr vido snsor ntworking tchnologis. In MULTIMEDIA 3: Procdings of th lvnth ACM intrnational confrnc on Multimdia, pags , Nw York, NY, USA, 23. ACM Prss. [3] D. B. Johnson and D. A. Maltz. Dynamic sourc routing in ad hoc wirlss ntworks. In Imilinski and Korth, ditors, Mobil Computing, volum 353. Kluwr Acadmic Publishrs, [4] P. Kulkarni, D. Gansan, and P. Shnoy. Th cas for multi-tir camra snsor ntworks. In Intrnational Workshop on Ntwork and Oprating Systms Support for Digital Audio and Vido (NOSSDAV 25), 25. [5] X. Lu and R. Manduchi. Fast imag motion computation on an mbddd computr. In 2nd IEEE Workshop on Embddd Computr Vision, Nw York, Jun 26. [6] C. M., J. Rich, and F. Zhao. Distributd attntion in larg scal vido snsor ntworks. In Proc. IEE Intllignt Distributd Survillanc Systms, 24. [7] C. Margi. Enrgy consumption trad-offs in powr constraind ntworks. PhD thsis, Univrsity of California, Santa Cruz, 26. [8] C. B. Margi, R. Manduchi, and K. Obraczka. Enrgy consumption tradoffs in visual snsor ntworks. In 24th Brazilian Symposium on Computr Ntworks (SBRC 26), Curitiba, Brazil, Jun 26. [9] C. B. Margi, V. Ptkov, K. Obraczka, and R. Manduchi. Charactrizing nrgy consumption in a visual snsor ntwork tstbd. In 2nd Intrnational IEEE/Crat-Nt Confrnc on Tstbds and Rsarch Infrastructurs for th Dvlopmnt of Ntworks and Communitis (TridntCom 26), Barclona, Spain, March 26. [1] M. Rahimi, R. Bar, O. I. Irozi, J. C. Garcia, J. Warrior, D. Estrin, and M. Srivastava. Cyclops: In situ imag snsing and intrprtation in wirlss snsor ntworks. In SnSys 25), 25. [11] A. Rajgarhia and F. Stann. Privacy snsitiv monitoring with a mix of IR snsors and camras. Tchnical Rport ISI-TR [12] A. Song. Picont ii - a wirlss ad hoc ntwork for mobil handhld dvics Acknowldgmnts This work was supportd by NASA, Intllignt Systms Program, undr contract NNA4CK89A.