Study On Distributed Target Tracking Algorithms Of Wireless Sensor Networks For Energy-Efficient Optimization

Wireless sensor networks (WSN), which integrate numerous technologies, such as sensors, micro-electromechanical systems, wireless communication, distributed information processing, have become a frontier research field for self-autonomous system behavior of swarm intelligence. For both military areas and civilian areas, the problem of target tracking has always been one of the key researches in wireless sensor networks, which focuses on the state estimation through multi-sensor collaboration to process and fuse information in the constraints of sensor energy and communication bandwidth. As far as tracking accuracy and energy consumption to prolong network lifetime, target tracking issues are investigated in depth based on relevant analysis of domestic and overseas, combining with the distributed nature of wireless sensor networks in this paper. The major research contents and productions are specially stated in the following areas:The advantage of networks organized by various types of sensor nodes is studied and a multi-sensor cooperative target tracking algorithm based on multi-modality information is proposed for many types of sensing information in wireless sensor networks. In order to reduce the communication traffic, the Gassian cost reference particle filter is ran on the sensor and the mean and variance of the estemated state are transmitted in the target tracking process. Simulation results show that the proposed multi-modality collaborative tracking algorithm meets tracking accuracy, and the communication cost is lower and the energy consumption is less than the centralized tracking algorithm, thereby the tracking performance of network is improved.Taking into account uncertainties of the motion form of the target and the nonuniformity of random distribution of wireless sensor networks, a new distributed lightweight target tracking algorithm, which combines the predicted residual energy and scheduling history of candidate sensor nodes, is proposed according to the motion state and the sensor density in a local mornitoring region. On the basis, the cluster head and the size of the cluster are respectively determined in the tracking process of wireless sensor networks. For dense wireless sensor networks, a local sub-optimal clustering algorithm for target tracking is performed with the coverage probabilistic detection model and an optimization of target tracking accuracy is compted, taking advantage of the posterior Cramer-Rao lower bound theory. By presenting the simulation experiments for different tracking forms and complex network environment, the proposed lightweight clustering strategy can dynamicly determine the size of the tracking cluster to save energy in the context of guaranteeing tracking accuracyAn adaptive sensor scheduling algorithm for monitoring a target in a local monitoring region of wireless sensor networks is presented for energy-efficient optimization in the constraint of the sensor detection. According to the detection probability of an individual sensor, the joint detection probability (JDP) is established. Based on sensors’own properties, a decision function model is built based on the property of the sensor. Further, an optimization scheme is designed to satisfy the problem of sensor scheduling subject to JDP and decision functions for candidated sensors, and an improved particle filter is proposed to solve the problem of degeneration. Compared with the general clustering algorithms, the proposed adaptive sensor scheduling algorithm can achieve an effective sensor scheduling. Meanwhile, this algorithm not only guarantees the tracking accuracy, but also reduces the energy difference between nodes and delines the whole energy consumption of wirless sensor networks.Based on the analysis of two typical sensor selection mechanisms in the target tracking process for wireless sensor networks, the sensor assessment mechanism and the sensor selection strategy under the condition of K-coverage are proposed through exploiting the efficient information and energy consumption model. Comparative experiments confirm that the algorithm can effectively reduce redundancy sensors to cut down redundant information and the accumulated energy consumption is significantly decreased. An energy partition method is designed on the basis of energy consumption model of the sensor to solve the problem of energy banlence in wieless sensor networks. Further, a cluster-head selection mechanism and the cluster member selection strategy are put forward respectively. To determine the optimal tasking sensors, the posterior Cramer-Rao lower bound is employed to ensure the tracking accuracy. Compared with other methods, active sensors in this algorithm are widely distributed to effectively control the sensor density in the local region, and energy consumption among sensors are balanced so as to prolong the network lifetime in the tracking process.For the problem of redundant information easily generated by densely deployed wireless sensor networks, a target state estimation framework based on quantization of measurements from sensors is proposed adaptive tracking mechanism based on quantization of observations was proposed in the sense of the sensor selection strategy. It reduces the communication traffic through exploiting the quantized measurement and increases the sampling interval to decrease the wake-up frequency of candidated tasking sensors. On this basis, an energy optimization model is devised to decrease the whole energy consumption of wireless sensor networks. Simulation experiments verified that the algorithm can allocate the bandwidth by quantization mechanism to reduce the traffic and extend the tracking sampling intervals as soon as possible to significantly reduce energy consumption of wireless sensor networks.