OMEECBR: A Novel Optimized Metaheuristic-Driven Energy-Efficient Cluster-Based Routing Protocol for IoT-Enabled WSNs

The Internet of Things (IoT) connects devices via the internet, with wireless sensor networks (WSNs) ensuring efficient data flow and extending network lifespan. While IoT has many applications, challenges like load balancing, energy use, security, and storage persist. Designing IoT-assisted WSNs with energy economy in mind is crucial, and multi-hop routing and clustering approaches help achieve this. To address this issue, an optimized metaheuristic-driven energy-efficient cluster-based routing (OMEECBR) is presented. The goal of the suggested OMEECBR model is to maximize the network's lifespan and energy consumption. The OMEECBR model uses the Sand Cat Swarm Optimization Algorithm (SCSO) technique for an effective clustering process. The fitness function involves energy, node degree, traffic density, inter-cluster distance, intra-cluster distance, and balancing factor. Additionally, the model uses a routing strategy based on Improved Salp Swarm Optimization (ISSO). The ISSO technique finds the best route by using a network-based fitness function. Evaluation of the suggested method using Python software shows that it is successful in improving security and reducing energy consumption in IoT networks.