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

Heterogeneous wireless sensor networks (HWSNs), comprising super nodes and normal sensors, offer a promising solution for monitoring diverse environments. However, their 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 with suboptimal energy distribution 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 tree construction, and clustering of normal nodes. Inspired by 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 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, on average, our algorithm reduces energy consumption and improves network coverage by 23% and 21.9%, respectively, and extends network lifetime by 501 rounds, compared to the state-of-the-art methods.