Target Tracking Algorithms Based On Node Collaboration In Wireless Sensor Networks

Wireless sensor networks (WSNs) are self-organizing networks formed by many tiny low-cost sensors, which have been widely used in many different applications such as military battlefield, environmental monitoring, health care, intelligent home, urban transportation, space exploration and so on. As one of the basic application in WSNs, target tracking mainly aims to solve the problem that WSNs spontaneously organize nodes to monitor, locate and track the moving target that enters the sensing region.This thesis analyzes the existing target tracking algorithms in WSNs, and proposes a target tracking algorithm based on neighborhood collaboration (TNC). First, we discuss the node weight for each sensing node in its neighborhood space. Then, a weighted centroid algorithm based on the node weight is proposed to estimate the location of the target, and determine the trajectory of the target through cooperation between nodes. The simulation results show that the proposed TNC algorithm has a good performance in tracking accuracy and energy consumption.This thesis proposes a target tracking algorithm based on collaboration tree (TCT) to tackle the problem that there is large computation or complex update in the current target tracking algorithms based on the tree. Based on heterogeneous nodes, TCT no longer mandates super nodes to collaborate only with each other, but also fully uses ordinary nodes around the super node to implement the monitoring and tracking tasks by building a collaboration tree. TCT algorithm also presents different node scheduling mechanisms for super nodes and ordinary nodes. By using the coordination of the two types of nodes, we achieve the goal of monitoring and tracking. Simulation results show that the proposed TCT algorithm has better performance than existing target tracking algorithms based on the tree.Target tracking algorithms focus on low energy consumption and high tracking accuracy. By making full use of node collaboration, the proposed two algorithms minimize energy consumption of nodes, prolong the network lifetime and ensure accurate tracking, which will be valuable for other related target tracking research and study.