An Energy-Aware Sleep Scheduling Coverage Protocol for Wireless Sensor Network

The accomplishment of optimal coverage in Wireless Sensor Networks (WSNs) involves random and dense sensor deployment. However, this approach can result in needless energy usage due to the coverage of the same area by redundant sensors. To conserve energy, it is crucial to activate only the necessary sensors at any given time. With the utilization of the WSNs’ Optimal Coverage Problem (OCP), it is possible to determine the minimum required number of sensors for monitoring a designated area while retaining the desired rate of coverage. One effective approach to tackle this problem is by using sleep scheduling protocols. These protocols schedule redundant sensors to alternate between sleep and active modes, and thus, mitigate the usage of energy as well as prolong the network’s longevity. Additionally, with the sensors’ division into the Disjoint Sets of Covers (DSCs), it is possible to ensure complete coverage of the region of interest or the specific targets. Also, the network’s longevity further extension is possible via an increase in the number of DSCs. However, the OCP as well as the DSC problems are regarded as a Non-deterministic Polynomial (NP)-hard problems of optimization. Evolutionary Algorithms (EAs) are able to offer promising solutions to effectively address these challenges. In this work, we propose the utilization of algorithms such as the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), the Ant Colony Optimization (ACO) as well as the Artificial Bee Colony (ABC) on the WSNs for overcoming the OCP. Through extensive experimentation, we are able to showcase the proposed approaches’ superior performance over the other technologically advanced techniques, which in turn will result in the WSNs’ enhanced performance as well as better conservation of energy.