Evolutionary-based coverage control mechanism for clustered wireless sensor networks
Many clustering protocols have been proposed for Wireless Sensor Networks (WSNs). However, most of these protocols focus on selecting the optimal set of Cluster Heads (CHs) in order to reduce or balance the network’s energy consumption and unfortunately, how to effectively cover the network area is often overlooked. Coverage optimization in WSNs is a well-known Non-deterministic Polynomial (NP)-hard optimization problem. In this paper, we propose a Genetic Algorithm (GA)-based Coverage Control Mechanism (GA-CCM) for clustered WSNs. GA-CCM provides an add-on mechanism that is designed to be integrated with any centralized clustering protocol to enhance its energy efficiency. GA-CCM finds the optimal set of active nodes that provides full area coverage and puts the redundant sensors into sleep mode to save energy. Extensive simulations of GA-CCM on 25 different WSNs topologies are conducted. Performance results are evaluated and compared against several well-known clustering protocols as well as a coverage-aware clustering protocol. Results show that GA-CCM always achieves full area coverage while minimizing the redundancy degree and the number of active nodes. To further evaluate the performance of GA-CCM as an add-on to existing clustering protocols, we integrate it with a Particle Swarm Optimization based CH selection protocol (PSO-CH), a comprehensive clustering protocol that considers many clustering objectives. To the best of our knowledge, PSO-CH has the lowest overall energy consumption among well-known clustering protocols. Experimental results show that this integration of GA-CCM to PSO-CH further improves its performance in terms of energy efficiency and packets delivery rate.
|Lecture Notes in Computer Science|
|Organisation||School of Information Technology|
Elhabyan, R. (Riham), Shi, W, & St-Hilaire, M. (2018). Evolutionary-based coverage control mechanism for clustered wireless sensor networks. In Lecture Notes in Computer Science. doi:10.1007/978-3-030-02931-9_6