An Energy-Aware Adaptive Probabilistic Tracking Mechanism Based on Quantization for Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) enable applications where target state estimation is essential. To deal with the energy source and communication bandwidth constraints, an energy-aware adaptive probabilistic tracking mechanism based on quantization was proposed. According to the relationship between the sensing radius and node properties which include stored information and position, a part of redundant nodes were removed under the condition on accuracy. An energy optimization model was established using the quantitative observations and an adaptive sampling interval strategy to reduce traffic for communication between sensor nodes. After that, a probabilistic sensor selection algorithm based on the sensing model of the node is creatively proposed to further reduce energy. In order to show the ascendant functions of the proposed mechanism, numerical simulation results including two scenarios, the single target and multiple Targets, showed that the algorithm can achieve the required tracking accuracy, effectively reduce energy consumption, and distinctly improve the performance of WSNs.