An Improved Grid-Based K-Coverage Technique Using Probabilistic Sensing Model for Wireless Sensor Networks

Coverage of an area, with one or multiple sensors, is one of the fundamental challenges in wireless sensor networks. Since a sensor life span is limited and reliable data is of great importance, sensitive applications like fire\leakage alarm systems, intrusion detection, etc. need multiple sensors to cover the region of interest, which is called K-coverage. Most of the studies that have been carried out on K-coverage evaluation have used binary sensing model. In this paper, we propose a grid-based K-coverage evaluation technique using probabilistic sensing model to increase evaluation accuracy and decrease evaluation time. The proposed technique is implemented using NS-2 simulator, and its results are compared to probabilistic perimeter-based and binary grid-based techniques. The results indicate that the proposed technique improved accuracy by 14% and 24% compared to the mentioned techniques respectively. It also shows 7% decrease in evaluation time compared to probabilistic perimeter-based technique.