An Improved Honey Badger Algorithm for Coverage Optimization in Wireless Sensor Network

Abstract

One of the critical indicators for a significant impact on the quality of service of wireless sensor networks (WSN) is coverage that directly determines the monitoring capability of the target monitoring area. This paper suggests a node coverage optimization strategy solution with an improved Honey-badger algorithm (called IHBA) to address the problems of WSN nodes' uneven distribution and low coverage in the random deployment. The honey-badger algorithm (HBA) is a recently developed metaheuristic algorithm with several advantages, e.g., robust process and ease of implementation; still, HBA has limitations in avoiding the local optimum trap when dealing with complicated node coverage optimization situations. The IHBA is implemented by modifying and updating equations with elite reverse learning and multi-direction strategies to prevent its original algorithm drawbacks. The obtained results on testing the benchmark function and the optimal WSN node coverage of the IHBA compared with the other algorithms in the literature. Compared results show that the IHBA algorithm provides effective optimal performance, convergence speed, and increasing feasible coverage. Significantly, the coverage rate archived of the IHBA is 87%, but the other methods are only below or equally 84% in the same comparison conditions.