Improved clustering algorithms for target tracking in wireless sensor networks

In recent years, there has been a growing interest in wireless sensor networks because of their potential usage in a wide variety of applications such as remote environmental monitoring and target tracking. Target tracking is a typical and substantial application of wireless sensor networks. Generally, target tracking aims basically at estimating the location of the target while it is moving within an area of interest and consequently report it to the base station in a timely manner. However, achieving a high accuracy of tracking together with energy efficiency in target tracking algorithms is extremely challenging. In this article, we propose two algorithms to enhance the adaptive-head clustering algorithm, formerly lunched, namely, the improved adaptive-head and improved prediction-based adaptive head. Particularly, the first algorithm uses dynamic clustering to achieve impressive tracking quality and energy efficiency through optimally choosing the cluster head that participates in the tracking process. On the other hand, the second algorithm incorporates a prediction mechanism to the first proposed algorithm. Our proposed algorithms are simulated using Matlab considering various network conditions. Simulation results show that our proposed algorithms can accurately track a target, even when random moving speeds are considered and consume much less energy, when compared with the previous algorithm for target tracking, which in turn prolong the network lifetime much more.