Spatio-temporal fusion for reliable moving vehicle classification in wireless sensor networks

Transcription

1 Proceeings of the 2009 IEEE International Conference on Systems, Man, an Cybernetics San Antonio, TX, USA - October 2009 Spatio-temporal for reliable moving vehicle classification in wireless sensor networks Chunting Liu, Hong Huo, Tao Fang Institute of Image Processing & Pattern Recognition Shanghai Jiao Tong University Shanghai , China Deren Li LIESMARS Wuhan University Wuhan , China Abstract One of the important tasks in sensor networks is classifying moving vehicles. Fusion of large amount of sensor measurements can improve network performance an reuce the consumption of sensor network resource. We stuy using continuous measurements of multiple sensor noes to improve the classification performance by spatio-temporal an fault etection. Time series ecisions of single sensor noe are aggregate to make a reliable classification estimation. A center combines local classification ecisions an evaluates the correctness of these ecisions. A correctness status is sent back to each sensor noe. Base on the status, sensor noes can ajust their temporal result. Simulation results emonstrate the valiity of our metho. Inex Terms Wireless sensor networks, spatio-temporal, fault etection, classification I. INTRODUCTION Wireless sensor networks (WSNs) are compose of a large number of tiny sensor noes. These sensor noes are ensely eploye in the network covere area, an have limite communication, computation capability an storage capacity. Moving vehicle classification is an important task in WSNs[][2]. Limitation of sensor noes an interference of the observe environment make sensor s ata prone to be faulty. Implementing reliable classification nees the collaboration of multiple sensor noes. A lot of research focus on information for moving vehicle classification in WSNs have been reporte[3][4][5][6]. In this paper, we stuy algorithm for reliable classification in WSNs. Collaborative signal processing an fault management are crucial to reliable sensor network applications. In scenarios of moving object observation, ensely eploye sensor noes sample the phenomena perioically, measurements of istribute sensor noes form a general observation of the moving object in space an time imension[][4]. Both the spatial an temporal sensor measurements can be utilize to improve sensor network performance[3][7]. Limitation of sensor noes in sensing an communication an interference of observe environment make sensor s ata prone to be faulty. To improve sensor network performance, fault management algorithm is necessary. Fault etection is an important way of fault management in WSNs. In many fault etection algorithms[8][9], a faulty sensor is usually etecte an exclue in the processing proceure. However in some cases, sensors may be temporally faulty an the faulty information may be also useful. Moreover, Implementing fault etection an both rely on the collaboration of spatio-temporal sensor ata[0][][2]. Therefore, we inten to combine an fault etection in one processing proceure. We propose a scheme to implement fault etection in the process, an make use of the fault etection result into the proceure. Both spatial an temporal sensor ata are use in the process. Possibly faulty sensors can be etecte at the center base on classification ecisions of multiple sensor noes an the etection result can conuct subsequent temporal at local sensor. We propose this metho base on the spatio-temporal correlation nature of sensor measurements[3]. In a moving target observation event, classification ecisions shoul be consistent in space an time imension. Therefore we aggregate time series of local ecisions to improve the reliability of local classification, then the local ecisions of multiple sensors are fuse at center to ecie the vehicle type. Fault etection is implemente at the center by comparing the local ecisions with the spatial result. If a local ecision is etecte as faulty, it is obvious most other sensors have ecisions ifferent than it, its temporal result may be unreliable. We use the fault etection results to ajust the temporal of local sensors through Dempster-Shafer (DS) framework. Only har ecision labels are communicate to the center an the center return a -bit status information to inicate the correctness of the local ecision, each sensor can ajust its temporal result without knowing local ecisions of other sensors, so our metho oesn t a much communication cost. The computation overhea is also low because the an etection operation are simple. Simulation results show that the classification performance is improve base on our metho. The rest of this paper is organize as follows. Section II introuces the backgroun of single moving vehicle classification problem. Section III presents the implementation of the spatio-temporal an fault etection proceure. In section IV simulations an results are presente. Section V raws the conclusions. II. PROBLEM FORMULATION We consier the scenario of a single vehicle moving through an observe area. The sensor noes are groupe as clusters an the cluster heas act as centers to perform spatial. Assume a cluster of n sensor noes S i,i=,...,n} /09/$ IEEE 5253

2 participate in the classification process. Some moalities of signals emitte by a moving vehicle, such as acoustic, seismic or infrare, can be sample by the sensor noes. Distribute sensor noes observe the object continuously. The measurement of S i at time t enote as x i (t) is moele as: Time series ata of sensor i... Block k... Temporal x i (t) =s i (t)+n i (t), () where s i (t) is the emitte signal of a moving vehicle measure at location of S i, n i (t) is aitive Gaussian noise. Processing unit of sensor noe can extract time or frequency omain features an use these features to istinguish vehicle type. Let X i enote an l-imensional feature vector extracte by sensor noe i from a perio of raw samples. Assume there are M types of vehicles, C j enotes the jth type of vehicle, the likelihoo probability of X i committe to C j is p(x i C j )= (2π) /2 Σ j exp( /2 2 (X i μ j ) T Σ j (X i μ j )), (2) where j =,...,M, μ j an Σ j are mean an covariance of C j. Each sensor noe makes a classification ecision locally through the maximum a posteriori (MAP) rule: an u =arg max p(c j X i ), (3) j=,...,m D i = g(u). (4) Fig.. History ata mik, Z ik, Temporal upating (a) m ik, Spatial Sen D i,k to center Status l i return to sensor i S S 2 S n... D,k l D 2,k (b) l 2 D nk, (a) Temporal processing in local sensor, (b) Framework of spatial l n where p(c j X i ) is the posterior probability of C j, D i is a ecision label, function g(u) output an M-imensional vector with only the uth element is an others are 0. If these sensor noes are reliable, observations of the same object may have consistent classification results. However, faults are always inevitable in WSNs. There are many types of faults. [0] presents a taxonomy of classification of faults in sensor networks. In this stuy, we assume the sensor harware an the signal processing algorithms are fault tolerant an communication links between sensor noes an center are reliable. We only consier the faults cause by ranom measurement noise, the sensor ecisions are ranomly faulty with probability. III. SPATIO-TEMPORAL FUSION AND FAULT DETECTION The basic concept of our algorithm is to etect an correct ecision faults when fusing the sensor ata in space an time omain to achieve reliable classification. The proceure inclues temporal, temporal upating an spatial. Fig. shows the signal processing proceure at local sensor noe an the center. A. Temporal In the temporal step, each block of T ecisions are combine at local sensor noe to improve local classification performance. The ecision label of noe S i at time t erive from (4) is enote as D i,t, which is an M-imensional vector, the block inclues ecision labels from time (k )T + to kt. Let Z i,k enote the temporal result of the kth block of noe S i. Averaging of the labels are compute Z i,k = T kt t=(k )T + D i,t. (5) Note that Z i,k nee not to be a har label. The har ecision label D i,k will be given in the temporal upating step. B. Temporal upating The temporal upating step upates the kth block of temporal result Zi,k base on previous temporal upating result D i,k an its status l i to generate the kth temporal upating result D i,k. The status l i that sent back by the center inicates the consistency of D i,k with the spatial result. Intuitively, if l i =, we think D i,k is correct, otherwise it is faulty. We use DS theory[4] to implement temporal upating. DS theory is an appropriate metho to eal with an represent uncertain information. The frame of iscernment is efine as C Θ = C,...,C M }. Denote m i,k (C) as the basic probability assignment (BPA) function of D i,k.letc j enote the class that has the maximum ecision value, i.e. for any j =m, j, m M, thereared i,k (C j ) >D i,k (C m ). 5254

3 The BPAs of D i,k are efine as: D i,k (C j ) l i =, m i,k (C j )= 0 l i =0. m i,k (Cj C 0 l i =, )= D i,k (C j ) l i =0. m i,k (C m )=D i,k (C m ) m =,...,M,Θ, m =j, (6) where Cj C = C Θ \C j is the complement of C j.definem i,k (C) as the BPA function of Z i,k : m i,k (C m )= Z i,k (C m ) m i,k (C Θ )=0. m =,...,M To avoi evience conflict between m i,k an m i,k,weuse the combination rule propose by Yager[5] to allocate the conflict evience to the frame of iscernment C Θ : m i (C j )= m i,k (A)m i,k (B) A B=C j m i (C Θ )=m i,k (C Θ )m i,k (C Θ )+ m i,k (A)m i,k (B). an The local ecision D i,k is mae by: A B=φ (7) (8) u i,k =arg max m i(c j ), (9) j=,...,m,θ D i,k = g(u i,k ). (0) After ecision making, we sen D i,k to the center base on the following principle: ) If C j,j =,...,M has the maximum element in m i, noe S i sens D i,k to the center to perform spatial. 2) If m i (C Θ ) > m i (C j ) for all j =,...,M or there are more than one classes have the maximum ecision value, S i oes not sen D i,k to the center an the status l i is set to be. S i will combine the next block of temporal result to make a vali ecision. C. Spatial an fault etection Fusion center receives the temporal upating results D i,k,i=,...,n} of each sensor an perform spatial. The final ecision is mae base on: n u s =arg max D i,k, () an D k = j=,...,m i= g(u s ) if u s = g(m +) otherwise, (2) where D k is (M +)-imensional an enotes the carinality. After spatial, the center use D k to evaluate the correctness of D i,k an sen a status l i return to sensor i to inicate the correctness. If D i,k is juge as faulty, a status l i =0will return, otherwise, l i = With fault etection,n=,t=3 Without fault etection,n=,t=3 With fault etection,n=,t=9 Without fault etection,n=,t=9 With fault etection,n=3,t=9 Without fault etection,n=3,t= Fig. 2. comparison of with an without fault etection IV. SIMULATION This section presents the simulation results to illustrate the performance of our algorithm. We simulate n sensor noes that generate M-imensional har ecision labels, where the labels are ranomly faulty with probability. In the simulation, the number of classes M is set to be 2. Our simulation results are average over 50 runs. At each run, 2000 ecision labels per noe are generate. Define n c as the number of correct classification ecisions that were mae by the center, n f is the number of faulty ecisions. p c is efine as: n c p c =. (3) n c + n f Fig. 2 compares the classification rate of our metho with spatio-temporal metho that without fault etection. The spatio-temporal metho that without fault etection is computing average of each block of labels at the temporal step an making a ecision, an sening the ecision to the center to perform spatial. Fig. 2 shows our metho performs better than the metho without fault etection for ifferent n an T. Fig. 3 an Fig. 4 stuy how the block length T an number of sensor noe n impact classification performance. Fig. 3 shows the classification rate as a function of the sensor fault probability for T =3, 5, 9, 5 an n =3, 5, respectively. We can see that our metho has higher classification rate for all the T an n when 0.2, an increasing T an n both can improve the classification performance. Therefore, T can be increase to iminish the nee of number of sensors that participate in the process, an increasing n can reuce ecision elay. Fig. 4 investigates the relationship of classification rate with n. Fig. 4 also shows that the classification rate is improve along with increasing n. The improvement is iminishe when n reaches a certain extent. There are three operations in our metho that can ensure reliable classification: temporal, temporal upating an 5255

4 5 5 T=3,n=3 5 T=5,n=3 T=9,n=3 T=5,n= T=3,n=5 5 T=5,n=5 T=9,n=5 T=5,n= Fig. 3. with respect of for T =3, 5, 9, 5 an n =3, 5 Correction probability n=3 n=5 n= n= Fig. 5. Fault correction probability of temporal an temporal upating for ifferent n, T = = =0.20 =0.25 =0.30 =0.35 = n (a) = =0.20 =0.25 =0.30 =0.35 = n (b) Fig. 4. with respect of n, (a)t =3,(b)T =9 Correction probability T=3 T=5 T=9 T= Fig. 6. Fault correction probability of temporal an temporal upating for ifferent T, n =5 spatial. Temporal combine a series of local ecisions to improve the classification reliability. Temporal upating amen the temporal result base on the previous temporal upating result an its correctness status. Consequently, faults of local ecisions can be correcte. Fig. 5 an Fig. 6 show the fault correction capability of the temporal an upating steps for ifferent T an n, respectively. The fault correction probability p t fc is efine as the ratio of the number of faulty ecision labels D i,t that were correcte to the number of all the faulty D i,t s. The correction probability exhibits similar tren as classification rate an is greatly improve by increasing T. Fig. 7 shows the fault etection probability p FD of the center for ifferent n. The fault etection probability is efine as the ratio of the number of faulty reports that sent to the center to the number of faulty reports etecte by the center. The results show that p FD is improve along with increasing n. Although the temporal fault correction probability an spatial fault etection probability are limite with respect of large, combining 5256

5 Fault etection probability 0.3 n=3 n=5 n=9 0.2 n=5 n= [0] F. Koushanfar, M. Potkonjak an A. Sangiovanni-Vincentelli, On-line fault etection of sensor measurements, Sensors, Proc. of IEEE, vol. 2, pp , October [] J.R. Chen, S. Kher an A. Somani, Distribute fault etection of wireless sensor networks, Proc. of workshop on DIWANS, pp , [2] M.H. Lee an Y.H. Choi, Fault etection of wireless sensor networks, Computer Communications, vol. 3, no. 4, pp , [3] M.C. Vuran, Ö.B. Akan an I.F. Akyiliz, Spatio-Temporal Correlation: Theory an Applications for Wireless Sensor Networks, Computer Networks, vol 45, pp , [4] G. Shafer, A Mathematical Theory of Evience, Princeton, NJ: Princeton University Press, 976. [5] R.R. Yager, On the Dempster-Shafer framework an new combination rules, Information Sciences, vol. 4, pp , 987. Fig. 7. Fault etection probability of spatial for ifferent n, T =3 the three steps together still can achieve better classification performance. V. CONCLUSIONS This paper stuies a metho for reliable classification in WSNs. We implement fault etection an correction in a spatio-temporal process. Possibly faulty sensors are etecte after spatial an the etection result is use to conuct following temporal proceure. Faults can be correcte in the temporal, temporal upating an spatial steps. Simulation results show that spatio-temporal with fault etection has better classification performance than the metho without fault etection, an increasing T an n can both improve the classification performance. REFERENCES [] D. Li, K. D. Wong, Y.H. Hu an A.M. Sayee, Detection, classification an tracking of targets, IEEE Signal Processing Magazine, vol. 9, no. 2, pp. 7-29, March [2] M.F. Duarte an Y.H. Hu, Vehicle classification in istribute sensor networks, Journal of Parallel an Distribute Computing, vol. 64, no. 7, pp , July [3] X.L. Wang, H.R. Qi an S.S. Iyengar, Collaborative multi-moality target classification in istribute sensor networks, Proc. 5th Int. Conf. on Information Fusion, Annapolis, USA, pp , July [4] A. D Costa, V. Ramachanran an A.M. Sayee, Distribute classification of gaussian space-time sources in wireless sensor networks, IEEE Journal on Selecte Areas in Communications, vol. 22, no. 6, pp , August [5] M.F. Duarte an Y.H. Hu, Distance-base ecision in a istribute wireless sensor network, Telecommunication Systems, vol. 26, no. 2-4, pp , [6] Q. Pan, J.M. Wei, H.B. Cao, N. Li an H.T. Liu, Improve DS acoustic-seismic moality for groun-moving target classification in wireless sensor networks, Pattern Recognition Letters, vol. 28, no. 6, pp , December [7] M. Ding, D.C. Chen, A. Thaeler an X.Z. Cheng, Fault-tolerant Target Detection in Sensor Networks, Wireless Communications an Networking Conference, vol.4, pp , March [8] M.J. Yu, H. Mokhtar an M. Merabti, Fault Management in Wireless Sensor Networks, IEEE Wireless Communications, vol. 4, no. 6, pp. 3-9, [9] R. Rajagopal, X.L. Nguyen, S.C. Ergen an P. Varaiya, Distribute online simultaneous fault etection for multiple sensors, Int. Conf. on IPSN, pp , April