Research On Target Tracking Algorithm In Wireless Sensor Networks

Wireless sensor networks(Wireless Sensor Networks, WSN) is a new cross-technology, which integrates wireless communication technology, sensor technology, microelectronics, embedded computing and distributed information processing technology. Because of fast deployment capabilities, self-organizing network characteristics, and high coverage and high fault tolerance, etc, wireless sensor networks has achieved widely concern. Target tracking is a basic function of many applications in wireless sensor networks, and as the sensing radius, storage capacity, communication capability, data processing capability of sensor nodes, network bandwidth and node energy are very limited, the tracking system must manage and dynamically scheduled network resources to build a suitable network topology, in order to develop high-precision, high-efficiency target tracking algorithm.Realizing the target tracking in wireless sensor networks with limited resources is a challenging task. And this article optimizes the network topology by clustering mechanism, so as to balance energy consumption and prolong the lifetime of the network. After analyzing advantages and disadvantages of target tracking algorithm in WSN, this paper designs a Clustering System based on Energy Balance(CSEB), which takes into account the limited resources of the target tracking in WSN. Energy balance and tracking accuracy are the two main aspects of solving the optimal clustering issues of target tracking in WSN. With regards to this, we introduce a new energy balance indicator- the standard deviation of the residual energy of nodes. It not only saves the total energy consumption, but also takes into account the extent of the distribution of the residual energy of nodes in the network. CSEB mechanism implemented in this article uses particle swarm optimization(PSO) to select tracking sensors, and for the application of wireless sensor networks, the original PSO algorithm has been improved. The improved binary particle swarm optimization(BPSO) algorithm is to optimize so that the sensor node scheduling problem is transformed into a multi-objective constrained optimization problem to find the optimal cluster. Simulation results show that, CSEB clustering algorithm has obvious advantages of the tracking accuracy, network energy balance, network lifetime, compared with the clustering mechanisms respectively based on the energy consumption and the extended Kalman filter. The proposed CSEB algorithm tolerates energy-imbalanced networks, and has been significantly improved in terms of energy efficiency of the sensor nodes, to balance the energy consumption of the entire network, and to extend the network lifetime.