Learning Sensor Data Characteristics in Unknown Environments.

Transcription

1 Learig Sesor Data Characteristics i Ukow Eviromets. Tatiaa Bokareva tbokareva@cse.usw.edu.au The Uiversity of NSW Nirupama Bulusu bulusu@cs.pdx.edu Portlad State Uiversity Sajay Jha sjha@cse.usw.edu.au The Uiversity of NSW Abstract Ad hoc Wireless Sesor Networks derive much of their promise from their potetial for autoomously moitorig remote or physically iaccessible locatios [3]. As we begi to deploy sesor etworks i real world applicatios [8], cocers are beig raised about the fidelity ad itegrity of the sesor data. I this paper, we motivate ad propose a olie algorithm that leverages a Competitive Learig Neural Network (CLNN) for characterizatio of a dyamic, ukow eviromet. Based o the proposed characterizatio sesor etworks ca autoomously costruct multimodal views of their eviromets ad derive the coditios for verifyig data itegrity over time. I. INTRODUCTION For widespread adoptio of sesor techology, robust ad high-itegrity operatio of the sesor etwork is of paramout importace. This is challegig because sesor etworks are ofte Ad-Hoc, deployed i ukow eviromets with limited kowledge of the pheomea beig observed. I previous work, we have made the case for a self-healig sesor etwork architecture called SASHA[2], that is ispired by the Natural Immue System (NIS). The success of the NIS largely lies i its ability to lear to distiguish betwee hosts ad foreig cell i our bodies, kow as self ad o-self sets respectively. I [2] we outlied that sesor data costitutes a major part of the self-set i a sesor etwork. I this paper, we propose a techique for the costructio of such a self-set that leverages the Competitive Learig Neural Network (CLNN). CLNN uses a clusterig procedure for detectig atural groupigs i data, which we call the multimodal view of data. The ability of detectig such groupigs is a importat fuctioality of Wireless Sesor Networks (WSN). First of all, i ukow eviromets, distiguishig ormal ad aomalous behavior of sesor readigs is a difficult task, maily because of the lack of kowledge of ormal data behavior. Secod, idividual sesor devices have very limited computatioal capacity which bouds the amout of computatio that ca be performed o these devices. The approach for checkig sesor data itegrity must scale to large etworks, miimize the eergy overhead required to commuicate sesor data, ad adapt to system ad evirometal dyamics, such as ode failures ad the atural chagig eviromet. To address the first problem, we propose to leverage CLNN for the costructio of a probabilisitic ad multimodal view of the sesor eviromet. To address the secod problem, we exploit i-etwork processig i a hierarchical sesor etwork, where the classificatio of sesor readigs is performed at cluster-heads or also kow as moitorig odes i SASHA rather tha o a cetralized base statio. Figure 1 shows the geeric architecture of a hierarchical sesor etwork where each ode is commuicatig its sesor data to a closest cluster head. The cluster head classifies the iput data ad performs most of the sigal processig before forwardig the data to a base statio. The stregth of usig CLNN is that it does ot require us to collect a priori data for iferece of ormal data behaviors, makig it suitable for large scale, remotely deployed sesor etworks. Furthermore, i the past similar procedures were successfully used for digital image aalysis as a classifier of multimodal data[9][1]. Their ability of deducig uderlyig patters i sesor data is a very importat aspect for the classificatio of sesor readigs as the umber of such patters is geerally ot kow i advace. Aother advatage is that clusterig techiques ca reduce the amout of iformatio to be trasmitted over the radio which has bee idetified as a major source of eergy cosumptio. Further, leveragig the hierarchical structure of a sesor etwork ca reduce the umber of hops that this iformatio eeds to travel through, which ca potetially lead to further eergy savigs. However, the reductio i the iformatio space requires higher preprocessig of data. Aother drawback is that CLNN suffers from the usual problems associated with equivalet learig procedures. It relies o parametric assumptios about uderlyig structure of data classes ad it require a large amout of traiig time depedig o the characteristics of the pheomea beig observed. It also lacks sigificat theoretical properties ad is very heuristic i ature. Nevertheless, we believe that CLNN is well suited for the classificatio of sesor readigs especially if the udelyig physical process is ot kow. Paper Cotributios This paper motivates ad proposes a usupervised learig procedure for modelig iheret classes of sesor data based o which we ca defie a framework for data itegrity verificatio. We motivate the use of data clusterig as a buildig block for modelig of sesor data. This approach distiguishes high cofidece from low-cofidece clusters by assigig a additioal probability to each cluster, while preservig the fie-graied features withi the data. We have evaluated our approach usig physical sesor read-

2 Mica Stargate Fig. 1. Base Statio Data Stargate Stargate A example of a hybrid sesor etwork igs ad empirically assessed its accuracy ad performace o a cotiuous samplig applicatio. Our evaluatio demostrates that CLNN is a promisig approach for idetifyig the correct classes i sesor readigs eve i the presece of evirometal ad system dyamics. II. APPROACH As metioed earlier, sesor etworks ca be deployed to study a ukow physical pheomeo, sometimes i remote or hostile eviromets, where the iheret patters of uderlyig data are simply ukow ad hece it is difficult to decide what kid of probabilistic models to use for the represetatio of such data. Moreover, the eviromet may be dyamic ad vary with time. Therefore, our primary desig goal was to come up with a solutio, that did ot require a priori kowledge of the uderlyig physical process. I the rest of this sectio, we provide a very brief overview of CLNN. Refereces [4] ad [5] provides a more i depth itroductio to the theory of competitive learig. A. Competitive Learig Neural Network (CLNN) CLNN tries to fid the relevat iformatio withi a set of iput vectors by dividig its elemets ito clusters, where similar elemets are grouped ito oe cluster. If the pheomeo ca be categorized by several distict features the separate clusters will lear these features ad each of them ca have a separate represetatio. CLNN used i this study was iitialized with 8 output uits which were coected to all 8 iput uits by meas of the weight matrix. Each row of represets a coectio betwee a output uit ad!. Whe a iput vector "$#! is preseted to CLNN at some time# oly oe output uit will be selected as the wier by meas of calculatig the smallest Euclidea distace betwee a row vector of weights ad a iput vector "$#! &%(' )*$#!,+-.#! '/' #!,+-.$#! ' 12 (1) where ) $#! is a vector of weights that is attached to the wiig euro,.$#! is a iput vector of sesors readigs at a time # ad $#! is a weighted vector attached to losig euros. Oce a wier is selected, the correspodig vector of weights )*#! is shifted toward the iput vector.#!. ) $# ) $#!43;:<$"$#!9+= ) #!! (2) where >@?A:<?B5 is a costat learig rate ad C)*#D3657 is a updated weight vector of a wiig euro. As a result, vector E)*$#*3F57 will be located closer to the iput vector "$#! i the Euclidea space ad hece, whe a similar iput vector is preseted to CLNN, the same wiig euro will have a greater chace to wi the competitio. To avoid the problem of a sigle euro wiig the competitio all the time, we apply a simple rule kow as leaky learig. Here the weights of losig euros are updated i a similar maer to the wiig oe. $#43G5H98A $#!.3I:KJ$"$#!,+- $#! L1M2ON 8P (3) where : J?Q?R:. I this maer, eve losig clusters have a chace to lear ad capture more fie graied variatios i a pheomeo. We cotiue weight update util either the umber of iteratios (epochs) reaches a certai threshold or the distace betwee a S) ad "$#! becomes egligible. The represetatio of a wiig uit * is set to a vector TU VW, where T ad V are the lowest ad highest value of sesor readigs see by durig the traiig period. We also calculate its probability of wiig the competitio i future as follows: Y8[Z\ (4) where Z is the \ umber of times cluster wis the competitio out of times. Hece, at the ed of a traiig period, each cluster will lear some aspects of a measured pheomea which are defied by the itervals of sesor readigs ad the probability of their future occurreces. For example, lets assume that every euro wo a competitio at least oce, the the output of the CLNN at the ed of the traiig period is a collectio of leared itervals ad the probabilities of sesor readig to fall withi these itervals: 8` T av *bo8` TbH avba ]C^ \_\ cs8` Tdc avkce *b ch 8 fs8` Tdf avkfe HgE8` T(g* avwg fh Hg hs8` Tdh avkhe hh is8` Tdi avkie S8` Td avke ih H III. ANALYSIS I this sectio, we preset aalysis of the learig part used i CLNN. The actual process of learig i eural etworks is usually associated with a search i a multidimesioal errorsurface for a optimal state that miimizes the error fuctio. It was show that miimizig the error i learig for the Kohoe learig rule, quatio (2), correspods to miimizig the error fuctio jk ) $#! by followig its egative gradiet [7]: jko)*$#!y8 l5 _m po o $ ) $#!,+- #!! o oc (5) 2

3 { ƒ z z where $#! is a iput vector at a particular istace of time #, ) #! is a weight vector of equal dimesios associated with a wiig euro ad Z is a umber of traiig examples for which was a wier. The aalysis of this learig rule ca be divided ito two parts. Firstly it ca be show that the jk$ ) $#!! ideed seeks to fid a miimum for equatio (2), i.e., it tries to fid a optimal way of groupig sesor readigs ito clusters. Secodly it ca be demostrated that uder certai assumptios jk$ ) $#!! coverges to the equilibrium state which is proportioal to the coditioal probability of a cluster wiig whe the iput vector $#! is preset. From [4], [5], [1] ad [7] we ca see that miimizig the error fuctio: jk$ ) $#!!98 5 l _mqoo $ ) #!,+ $#!ooc (6) is equivalet to miimizig the equatio of a learig rule ) $# GO) $#!.3I:$ $#!&+= ) $#!! (7) where )*$#! is the weight vector at the time #, E) $#,3r57 is a updated weight vector, #! is the curret iput vector of sesor readigs ad: is a learig rate. I order to achieve the equilibrium state we eed to move weighted vector s)*#! from its curret positio toward the gradiet descet directio of jk )*#!!, such that: t G8`+S:Au jk$ ) $#!! u ) $#! ad by substitutig it ito equatio (7) we get ) $# A ) $#!43 t where u jk ) u ) #! 8v$ ) #!,+- #!! u O) $#!&+= $#! u ) $#! u $ ) #!,+= $#! u )*#! 8 u $ )b*$#!&+=db #!! u )ab #! u $ ) #!,+- $#! u ) $#! M8 w5 5 which meas that, u jko) u O)*$#! 8A ) $#!,+- #! ad t P8x+S:_- )*#!,+= $#!Y8P:_- #!,+-O)*$#! ad therefore ) $# ) $#!43 t 68G ) #!43y:$ $#!,+- ) #!! which is equivalet to equatio (7). The learig rule i equatio (7) brigs the wiig euro s weight vector closer to the traiig example.#!. Now lets cosider the ormalized learig rule. Accordig to [5] ad [1] we ca do this by settig t 68P:<7z #!!{ { +- )*#!! Let be the wiig euro whe vector "$#! is preset ad 8[ 5 if the is the wier for >.#! otherwise Let $#! be the probability that " #! is preseted ad } be the probability of wiig the competitio for this iput vector. The the equilibrium state ~ for the cluster ca be expressed as: ~<8rm t $ $#! (8) hece ~_8 z :.* w $ ƒ +- ) $#! #!! }Y8 : z w $ $ $#!! "+ : z ) $#! $ $#!! (9) where.$#! { 8 z $#!{ { (1) At the equilibrium state ~8r> ad if all clusters cotai the same umber of vectors the ) #!Y8 z { (11) z $ Graphically, S)*#! at the equilibrium state correspods to the ceter poit of the cluster. Fially, we preset the complexity aalysis of the learig phase. Note that the computatioal complexity is very much implemetatio depedet ad i this sectio we preset the aalysis of the simplest implemetatio. Algorithm 1 shows the implemetatio of simple competitive learig. If we assume that we trai the ]s \ CLNN o umber of iput vectors, the the complexity of CLNN is defied as followig: ]s ˆ 8 \ 4 s ˆ Z c.3 ˆ Z.3IŠS Œ ŽV Y ˆ Z "3 ˆ ˆ Z!Y8 \ 4 s ˆ Z c.36 l 3IŠ Œ Ž V H4 ˆ Z IV. EPERIMENTAL RESULTS (12) I this sectio, we describe the evaluatio of the proposed algorithm o physical sesor readigs ad the experimetal results obtaied. We also explai some of the choices made for the implemetatio of CLNN. The objectives of our evaluatio were three-fold: Is the proposed algorithm effective? Ca it lear the data characteristics i the presece of oise ad atural chages i the pheomea? If it ca lear, the how much oise i sesor readigs ca be preset or how frequetly the pheomeo ca vary before the algorithm loses its ability to deduce the correct classes of sesor readigs? 3

4 Algorithm 1 The simple implemetatio of the CLNN 1: set 8P>, Sb 8R5 l* OcE8 l >,..., 8x57>*>*>. 2: while the traiig time is ot over do 3: accept a iput vector.#! 4: for all the weights vector do 5: calculate the Euclidea distace betwee "$#! ad O ˆ : Z c 6: ed for 7: pick a wier based o the equatio (1) 8: update T, V ad of the :ˆ Z 9: while (umber of epochs y> ) or (the distace betwee "$#! ad )*$#! tha the miimum error tolerace) do 1: update ˆ weights (S) ) of based o the equatio (2): Z 11: update the weights ˆ of losig clusters by applyig equatio (3): Z 12: calculate ˆ the ew distace betwee ) ad.#! : Z 13: ed while 14: update the fial 15: ed while } : ˆ Z Is the algorithm efficiet? How log should we trai CLNN i order for it to correctly ifer the data classes? A. Implemetatio For the evaluatio of the proposed algorithm we set up a sesor etwork of 3 micaz[6] motes i a grid topology. Each mote collects eight samples of light sesor readigs per miute ad seds these samples to a clusterig ode. We choose light as the sesig modality for our experimets because it is easy to itroduce temporal ad permaet oise ito sesor readigs by castig a shadow over a sesor or by coverig its light sesor with a paper cup. Moreover, it is relatively straightforward to cotrol the light itesity, i a idoor settig, allowig for repeatable experimets. We limit the umber of data samples to 8 because TiyOS packet size does ot support more tha 29 bytes of payload ad we reserved 4 bytes for a ode s Cartesia coordiates. This is also why the umber of iput ad output uits of CLNN was set to 8. CLNN was traied o every received packet. There are several importat parameters that affect CLNN performace. First of all, the mai problem of slow covergece associated with eural etworks is related to iitializatio of its weight values. I this study we divided the etire spectrum of possible sesor readigs ito 8 zoes ad each vector of weights was iitialized to values from a particular zoe. As was metioed earlier, we do ot kow the exact distributio of sesor readigs i our lab, hece, we would possibly eed to lear the etire spectrum of sesor readigs. For example, the correctly operatig light sesor ca ot retur a egative value or a value of above 5 l >*> uits. Therefore, weights of the euro were iitialized to., weights for the output euro b were iitialized to 125, weights of c were iitialized to 25 uits, etc. This permits our CLNN to lear a etire spectrum of possible sesor readigs. Secod, there are two related parameters that affect the rate of CLNN learig. Namely, umber of epochs that we iterate over each iput vector.#! ad the amout of time we are allocated for the traiig period. With a loger traiig period, more iput vectors i CLNN will be leared, ad larger umber of clusters will lear these iput vectors. With a larger umber of iteratios over the same iput sample ad smaller traiig period, clusters will lear more about each iput vector but CLNN will lear over a lower umber of samples. The trade off betwee these choices is largely depedet o the measured characteristics of the pheomeo. If we expect our eviromet to have a lot of oisy data the the traiig period should be loger. This will allow CLNN to capture more of the o-oisy data ad therefore it would predict the correct patters i sesor readigs with a higher level of cofidece. Subsectio IV-B cotais the empirical evaluatio of the umber of traiig epochs that we would eed for the stable eviromet. We test our algorithm o three differet types of geeric pheomea. First, we kept the light level i our office o durig the traiig period ad itroduced faulty odes by coverig them with a white paper cup (Subsectio IV-C). Secod, we itroduced dyamics ito pheomeos behavior by castig a time varyig shadow over sesors ad by switchig the light off for approximately half of the traiig period (Subsectios IV-D ad IV-E). B. Varyig the Traiig Period ad Epoch Frequecy This subsectio presets the results for evaluatio of the trade-off betwee the umber of epochs ad the duratio of the traiig period. Figure 2 shows the covergece of weight vectors with 1 epochs ad the traiig period of 2 hours. As ca be see from this figure eve the furthest weight vector leared the pheomeo i over a hour, the ormal light readigs i our lab should lie betwee approximately 85 ad 95 uits ad the top most cluster 7 should cotai the represetatio of these readigs. Remarkably, at approximately 15:2, for o apparet reaso we received a oisy data. As ca be see from Figure 2, CLNN immediately lears these oisy values. The level of light i a computer lab is a reasoably stable eviromet therefore, we do ot eed all the output uits to lear the iput vectors ad hece, we do ot eed such a log traiig period. To study the impact of umber of epochs o the learig rate of CLNN, we kept the traiig period costat at 5 miutes ad vary the umber of iteractios for each iput vector. Figure 3 show the learig rates of weights for 1, 15, 5 ad 1 epochs respectively. As expected the fastest learig rate occurred at the 1 epochs. However, the rate of learig did ot icrease proportioally to the umber of epochs. We ca see that the weights coverge twice as fast if the umber of epochs is icreased from 1 to 15. But whe the umber 4

5 1 Data distributio for 2 hours Light : 14: 15: 16: time Fig. 2. The covergece of weights ad received sesor readigs for the 2 hour traiig period, with 1 epochs. Nearly all the weights coverge withi a 2 hour period. The o-covergig weight at the right corer idicates a radom source of oise. (a) 1 epochs (b) 15 epochs (c) 5 epochs (d) 1 epochs Fig. 3. Covergece of weights with 1, 15, 5 ad 1 epochs, after a 5 miute of the traiig period. Oly the closest wiig weight coverges to the correct sesor readigs after 1 epochs. However, two closest wiig weights, coverged after 15 epochs. No sigificat differece is oticed i the covergece of the weights to the correct sesor readigs betwee 15, 5 ad 1 epochs. This idicates that 15 epochs may be more tha sufficiet for the algorithm. 5

6 Fig. 4. The trajectory of weights for the scearios with the e ad e of faulty odes. 1.8 Probability of leared itervas to cotai correct sesor values cluster cluster 1 cluster 2 cluster 3 cluster 4 cluster 5 cluster 6 cluster Data distributio with the 5% of faulty odes Probability to wi.6.4 Light Number of faulty odes 6 45: 46: 47: 48: 49: 5: time Fig. 5. Cofidece levels of clusters to cotai the correct readigs as a fuctio of the umber of faulty odes after the traiig period. Fig. 6. Received sesor readigs for the sceario with e of faulty odes. of epochs is icreased from 15 to 1 we did ot observe such sigificat icrease i the learig rate. This suggests that o the least capable hardware, settig the umber of epochs to 15 is sufficiet. C. Varyig the Number of Faulty Nodes This subsectio studies the umber of faulty odes that ca be preset durig the traiig period before the algorithm loses its ability to distiguish betwee the correct ad aomalous patters i sesor data. For this experimet we keep the light level costat i the room ad vary the umber of odes covered by the white paper cup, which we assume to be the permaet oise. Figure 6 shows the distributio of data durig the evaluatio of our algorithm with > š of the odes beig faulty ad as ca be see, the biggest divisio i space betwee sesor readigs ca be made at the value of 85. As was stated i the previous subsectio, the top most cluster 7 should cotai the correct readigs which represets the costat level of light i the lab ad these readigs should lie betwee 85 ad 95 uits. Rest of the clusters are cotaiig oisy readigs which lies below 8 uits, clusters 6, 5, 4, 3 ad 2, or they ever wo a competitio, clusters 1 ad i this case. Figure 5 shows the cofidece level for the data cotaied i each cluster. As ca be see the correct cluster 7 has the highest cofidece level for the most cases. The rest of the clusters either ever wo a competitio or have captured the oise i the eviromet. As expected, the cofidece level of the correct cluster decreases as the umber of faulty odes icreases ad i order to deduce the correct readigs, a cofidece level should be œr>w. As ca be see from Figure 6 the algorithm correctly leared ad predicted sesor readigs whe up to *> š of odes were faulty. However, the cofidece level for the correct data i this case, was lower tha >K. Hece, i the presece of the costat oise the algorithm ca deduce the correct readigs whe up to >š of odes are workig correctly. As it was metioed earlier the learig i CLNN is achieved by meas of movig the weight vectors. Hece aother importat aspect i uderstadig the behavior of CLNN is to trace the trajectory of the weights for each cluster durig the traiig period. By lookig at their trajectory we ca see how CLNN deduces the correct patters i a data set ad why the cofidece level of the correct cluster is higher. Figure 4 shows the trajectory of average weight values for 6

7 1 Data distributio of the Experimet Light : 52:2 52:4 53: 53:2 53:4 54: 54:2 54:4 55: time Fig. 7. Trajectory of weights ad the sesor readigs for the secod experimet. each cluster for the scearios with the percetage of the odes varyig form>š to >š 1. The crosses i the graphs represet the average value of sesor readigs cotaied i received packets ad the small dots represet the average of weight vectors. As ca be see from Figure 4 whe there is o faulty ode preset i the eviromet the data falls withi a sigle cluster ad all the weights of losig clusters are slowly movig toward this data. The top two clusters 6 ad 7 are followig the o-oisy data. But cluster 6 begis to trace the pheomeo much later tha the cluster 7 ad this is why its cofidece level is lower. As more faulty odes are itroduced ito the etwork we ca see that more ad more of the data values are fallig ito separate clusters that are lyig below 8 uits. Hece ew clusters of data begi to emerge. As more of the data is gettig shifted away from the top clusters, the cofidece level i the correct data reduces. Nevertheless, as there are multiple clusters that lear the oisy readigs, their probability of wiig is small. Eve whe all the odes are faulty, the probability of wiig for cluster 5 which leared most of the oisy readigs is below >K ž. D. Dyamics: Time Varyig Shadows This experimet studies whether the proposed algorithm is robust ad ca adapt to dyamics of the eviromet ad i the presece of a temporal oise i the sesor readigs. We study the performace of the algorithm usig the sceario of a movig shadow. Here we cosider the correct sesor readigs to be the costat level of light i the office ad the oise to be the temporal shadow readigs. I this experimet the temporal oise is itroduced by a perso slowly movig i the strait lie carryig a large box for the duratio of the traiig period. We repeated this experimet 4 times. Figure 7 shows the distributio of the received readigs (the right figure) ad the trajectory of weights for the secod experimet 1 Due to the space restrictio the results for the experimets with Ÿ7 Ÿ e ŸŸ7 Ÿ S e, e ad! e of faulty odes are omitted. Probability Probability of a iterval to cotai correct sesor readigs cluster_ cluster_1 cluster_2 cluster_3 cluster_4 cluster_5 cluster_6 cluster_ evet simulatio umber Fig. 8. Cofidece levels associated with each cluster after the traiig period for all four experimets with a movig shadow. (the left figure). 2 The vertical lie i the left figure represets the cut off low boudary for the cluster 7. Figure 8 shows the cofidece levels i the leared values for all four experimets. It shows that the algorithm idetifies the correct sesors readigs (cluster 7), with a high level of cofidece (œp>w ). Rest of the clusters either ever wo the competitio or leared the temporal shadow readigs ad oise. The readigs located below 1 uits are the radom oise. As we will see i the ext experimet the dark office correspods to the readigs of 4 uits. E. Dyamics: Light ad Dark This experimet studies whether the algorithm ca adapt to dyamics of the pheomeo where we have multiple distict, ad time-varyig features of a pheomeo. We kept the lights o i our lab for the approximately half of the traiig period. We the tured the light off for the remaiig time of the traiig period. I this experimet we cosider two clusters to cotai the correct readigs, cluster 7 (85 to 95 uits) 2 Due to the space limitatio the results for the other 3 experimets are omitted. 7

8 1 Data distributio of the Light ad Dark 9 8 Light : 43: 44: 45: 46: 47: time Fig. 9. Figure o the left cotais the distributio of the received data. Figure o the right represets the trajectory of the weights ad the distributio of sesor readigs for the Light ad Dark experimet. ad (4 uits). Figure 9 shows the correspodig levels of cofidece i the leared data. It shows that the algorithm idetifies the correct bright light sesors readigs (cluster 7), with a level of cofidece of approximately >W. Cluster, cotaied dark light readigs ad it has a cofidece levels of approximately >K ž. Clusters 1, 2, 3, 4, 5 ad 6 capture the trasitioal characteristics of the pheomeo. We iteded for our pheomeo to have two equally distict characteristics. However, it is ot possible to switch light at precisely half way through the simulatio, as there are always few millisecods of a huma error. Also odes do ot take their readigs at exactly the same time, hece some odes i oe samplig period recorded both the light ad dark readigs. Therefore, the duratio of the dark traiig period was slightly shorter tha that of the light period. Figure 9 shows that there was a trasitioal period of approximately 3 secods. This is why the cofidece level of the dark feature is slightly lower tha oe for the bright oe. V. CONCLUSION I this paper, we motivated ad demostrated the use of CLNN for classificatio of data retured by sesor odes. We empirically demostrated that CLNN is capable of successfully learig the evirometal data patters eve i the presece of faulty odes. Based o classificatio of such patters, we ca filter aomalous data i a sesor etwork, without requirig a priori kowledge of the eviromet. We evaluated the proposed algorithm o three geeric light data collectio sesor etwork applicatios, where each applicatio has differet characteristics of a physical pheomeo. We empirically demostrated that the proposed algorithm is robust uder sesor oise ad it idetifies the correct patters is sesor data, eve with > š faulty sesor odes. As the umber of faulty odes icreases, the level of cofidece i data gradually decreases. Fially, the algorithm works correctly eve i the presece of evet dyamics. For example, whe a movig shadow is itroduced, the algorithm still ifers the bright light sesor Probability Probaility of a iterval to cotai correct sesor readigs cluster_ cluster_1 cluster_2 cluster_3 cluster_4 cluster_5 cluster_6 cluster_7 1 2 Iterval Labels Fig. 1. Cofidece levels i the leared data values that where recorded i each cluster. readigs with a high degree of cofidece, while preservig the fier-graied data correspodig to the presece of a shadow. REFERENCES [1] Yuichiro Azai. Patter Recoqitio ad Machie Learig. Academic Press Ic, Iwaami Shote Publishers, [2] Tatiaa Bokareva, Nirupama Bulusu, ad Sajay Jha. Sasha: Towards a self-healig hybrid sesor etwork architecture. I Proceedigs of the 2d IEEE Workshop o Embedded Networked Sesors (EmNetS-II), Sydey, Australia, May 25. [3] Edited by D. Estri ad W. Micheerad G. Boito. Evirometal cyberifrastructure eeds for distributed sesor etwork. Scripps Istitute of Oceaography, Agust [4] Rumelhart D. E ad Zipser D. Feature discovery by competitive learig. I Cogetive Sciece., volume 9, pages , [5] Mohamad H. Hassou. Fudametals of Artificial Neural Networks. MIT Press, [6] Crossbow Techologies Icorporated. Crossbow techologies. [7] Be Krose ad Patrick va der Smagt. A itroductio to eural etworks, November [8] Uiversity of Califoria at Berkeley. Habitat moitorig o great duck islad. [9] Joh A. Richards. Remote Sesig Digital Image Aalysis. A Itroductio. Spriger-Verlag, [1] Philip H. Swai ad Shirley M. Davis. Remote Sesig: The Quatitative Approach. McGraw-Hill, Ic,