Ahmad Namazi Nik - Department of Information and Communication Technology, Payame Noor University, Tehran, Iran
Abbas Ali Rezaee - Assistant Professor ,Department of Information and Communication Technology, Payame Noor University, Tehran, Iran

An important application of wireless sensor networks is the tracking of objects moving through a monitored area. The use of particle filters for target tracking in sensor networks has become popular in recent years because they are able to process observations represented by nonlinear state-space models whit non-Gaussian noises. The particle filter consists of three basic steps: sampling, weight update and resampling. One of the main limitations of the proposed schemes is that their implementation in a wireless sensor network demands a prohibitive communication capability, because they assume that all the sensor observations are available to every processing node in the weight update step. In this paper, we use a machine learning technique, namely support vector machine to overcome this drawback and save energy consumption of sensors. Support Vector Machine is a classifier which attempts to find a hyperplane that divides the two classes with the largest margin. Given labeled training data, SVM outputs an optimal hyperplane which categorizes new examples. The training examples that are closest to the hyperplane are called support vectors. Using our approach, we could compress sensor observations and only support vectors will be communicated between neighbor sensors which lead to a communication cost reduction. Our simulation results show significant reduction in the amount of transmitting data over the network.

Distributed Particle Filter, Support Vector Machines, Target Tracking, Wireless Sensor Networks