Maximizing the Lifetime of $k$-Discrete Barrier Coverage Using Mobile Sensors

Barrier coverage is an essential issue in wireless sensor networks for its various applications. Traditional barrier coverage, however, has a tight requirement that sensor nodes have to form continuous barriers. In many scenarios, this requirement can be relaxed to some extent while supporting the surveillance task well, thus easing the deployment of sensors. In this paper, we focus on maximizing the network lifetime under a novel k-discrete barrier coverage model, whose goal is to cover some specific discrete points of interest (POIs) by deploying sensors in k lines to form barriers. First, we formulate the problem of scheduling sensors with limited moving energy to achieve the maximal network lifetime while ensuring k-discrete barrier coverage as the max-lifetime for k-discrete barrier coverage with limited-moving-cost (KLCML) problem and then analyze the theoretical upper and lower bound of the problem and the coverage probability of the coverage model. Second, we propose a schedule algorithm, called KLCML, which uses the information on redundant sensors to maximize the network lifetime while achieving the k-discrete barrier coverage. Different from the existing works, the KLCML combines the redeployment method and the sleeping schedule method seamlessly to prolong the network lifetime intensively. Comprehensive simulations demonstrate that our analytical result about the coverage probability matches the simulation result well, and that the proposed KLCML algorithm prolongs the network lifetime effectively while achieving the k-discrete barrier coverage under different settings of parameters, such as the number of sensor nodes, the standard deviation of sensor offsets in deployment and the density of the POIs to be monitored.