Enhancing wireless sensor networks : A discrete mathematical model for OLEACH-based clustering and routing

This paper suggests a comprehensive description of some cluster head protocols, optimization techniques, and metaheuristic-based approaches for efficient energy operation in WSNs. A mathematical framework for cluster head selection, multi-hop transmission, and network topology is suggested for optimization by machine learning, game theory, and deep learning models such as Multi-Criteria Decision-Making (MCDM) and Butterfly Ant Colony Optimization (BOA-ACO). Moreover, an optimized LEACH protocol (OLEACH) is presented with improvements cluster formation and transmission techniques to improve network lifetime and load balancing. According to simulation results, OLEACH outperforms the conventional protocols MR-LEACH, BPDA, CNNDA, and FDEAM by greatly increasing throughput by 33.3%, network lifetime by 60%, and packet delivery rate by 98%. Through the best cluster head selection, OLEACH also minimizes long-distance transmissions, increases routing efficiency, and lowers energy usage by 42.16%. This work demonstrates the assurance of intelligent routing and clustering methods to ensure the best WSN performance, scalability, resilience, and sustainability for future applications of IoT.