A Hybrid Optimization Strategy for k-Coverage Maximization in Wireless Sensor and IoT Networks

In this paper, we propose a combination of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to create a hybrid optimization technique which meets k-coverage criteria in Wireless Sensor Network (WSN) and Internet of Things (IoT). The goal of this approach is to increase the coverage area alongside minimizing the deployment cost of the sensors. Another aim is to reduce the power consumption. This technique is helpful to determine the optimal position of the sensors. It helps to ensure that each target point in the network is getting the coverage of at least k sensors. In this way we can enhance the reliability and robustness of the network. From experimental results, we can conclude that with optimal sensor utilization and minimized redundancy, our proposed approach achieves 97.14% coverage ratio with k-coverage equals 3. According to performance metrics, it is clear that there is consistent energy efficiency through the cycles. The approach converges in less than 3 second. So, this approach is applicable for real time applications. It is a hybrid metaheuristic approach which is very efficient in managing various challenges associated with sensor deployment. From this approach we can get solutions of coverage enhancement, energy efficiency and improved sensor utilization.