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.