Probability-Based Prediction and Sleep Scheduling for Energy-Efficient Target Tracking in Sensor Networks

Cluster Based Trustable Target Detection and Tracking Scheme for Wireless Sensor Networks

“…Initially, all the nodes are provided with the energy of 60 joules. The energy consumption, network lifetime and the tracking accuracy is measured and compared against PPSS [13] and MCTA [14].…”

“…The major drawback of this technique is the computational complexity. In [13], a Probability based Target Prediction and Sleep Scheduling (PPSS) is presented. This work predicts the movement of the target by means of kinematics and probability theory.…”

A Distributed Energy-Efficient Algorithm for Cellular Target Tracking in Wireless Sensor Networks

“…Recent developments in target tracking have heightened the need for effectively maximizing the target tracking quality [1,2]. Jiang et al…”

“…Sensors are generally equipped with the capabilities of sensing, processing, and communicating. One of the most important issues in wireless sensor networks is energy consumption of sensors since these networks consist of a large number of battery-limited sensors which are typically used in the high-risk areas and thus the battery replacement is practically impossible [1]. The importance of energy efficiency of sensors is further significant in target tracking, as one of the most important applications of wireless sensor networks [2].…”

“…Target tacking in wireless sensors networks consists of accumulating information about target movement by sensors and delivering related information to other sensors or a base station to make an appropriate decision about the targets. Idle listening is a major source of energy waste for target detection and measurement [1]. In particular, sleep scheduling is one of the most commonly-used mechanisms for reducing the energy consumption during idle listening in order to extend the network lifetime [3].…”