Coverage and scheduling in directional sensor networks via hybrid mayfly optimization and graph coloring

Abstract Directional Sensor Network (DSN) consists of sensors with a limited sensing angle designed to monitor the targets efficiently within a specific field of view. Activating these battery-powered sensors simultaneously depletes their limited energy and affects the overall network performance. Sensor scheduling addresses these limitations by partitioning the working directions of the sensors into disjoint cover sets. Hence, this paper proposes a hybrid Mayfly Optimization and Vertex Coloring Algorithm (MO-VC) by focusing on energy-aware scheduling with optimal sensor deployment. The mayfly optimization algorithm determines the optimal positions of sensors by balancing both exploration and exploitation to maximize the target coverage. The vertex coloring-based scheduling technique constructs directional cover sets which satisfies the coverage requirements while minimizing redundant activities. The efficiency of the proposed algorithm is evaluated using the mathematical upper bound on the maximum number of cover sets. Further, the proposed algorithm is compared with several metaheuristic hybrid algorithms, such as Grey-Wolf Optimization with Vertex Coloring Algorithm, Harris Hawks Optimization with Vertex Coloring Algorithm, Salp Swarm Algorithm with Vertex Coloring, Firefly Algorithm with Vertex Coloring, Arithmetic Optimization Algorithm with Vertex Coloring, and Q-Learning with Vertex Coloring Algorithm. The simulation results show that the proposed MO-VC outperforms in terms of coverage efficiency and determines the maximum number of directional cover sets. Graphic abstract