On Coverage and Connectivity with Reduced Sensing Redundancy Based on Genetic Algorithm

Coverage and connectivity are two essential metrics in Wireless Sensor Networks (WSNs) for target monitoring and transmitting the data to the base station. A sensor consumes most of its energy in sensing and transmission. Energy consumption is to be reduced to increase the lifetime of the sensors. To enhance the robustness of the network, the targets must be monitored by multiple sensors. A sensor network achieves k-coverage if every target is monitored by a minimum of k sensors. Targets covered by more than k sensors result in redundant sensing. In the literature, most of the studies worked on achieving k-coverage but not on reducing redundant sensing. In this study, we propose a genetic algorithm-based method that considers four objectives: maximization of the k-coverage, minimization of the number of sensors, maximization of m-connectivity, and minimization of sensing redundancy. To achieve this goal, we have designed our fitness function accordingly. The proposed method tries to minimize the targets that create redundant sensing also resulting in less power consumption. Simulation results show the efficacy of the proposed algorithm. The proposed method also performs better compared to two relevant works existing in the literature.