A Two-Phase Genetic Algorithm Approach for Sleep Scheduling, Routing, and Clustering in Heterogeneous Wireless Sensor Networks

Heterogeneous Wireless Sensor Networks (HWSNs), which comprises super nodes and normal sensors, offer a promising solution for monitoring diverse environments. However, their practical deployment is constrained by the limited battery life of sensors. To address this issue, clustering and routing techniques have been employed to conserve energy. Nevertheless, existing approaches often struggle suboptimal energy distribution, limited network lifetime, and weak network coverage. Additionally, they mostly failed to exploit other energy saving techniques such as sleep scheduling. This paper proposes a novel Genetic Algorithm (GA)-based approach to optimize sleep scheduling, routing, and clustering in HWSNs. The method comprises two phases, namely join sleep scheduling and routing tree construction, and clustering of normal nodes. Inspired from the concept of unequal clustering, the HWSN is split into some rings in the first phase, and the number of awake super nodes in each ring keeps the same. This approach addresses the challenges of balancing energy consumption, enhancing energy efficiency, and network lifetime. Furthermore, including network coverage and energy-related criteria in the proposed GA yields long-lasting network operation. Through rigorous simulations, we demonstrate that our proposed algorithm reduces energy consumption and network coverage by 16.7% and 24.9%, respectively, and extends network lifetime by 532 rounds.