Optimization of Wireless Sensor Networks by the Use of Machine Learning-Based Energy-Efficient Cluster Head Selection Algorithms

Abstract

Modern, state-of-the-art developments in communications infrastructure often lead to load optimization and energy savings when paired with the architectural resources of WSNs and multi-objective optimization. Separating issues with WSN design, routing, an energy-efficient deployment strategy, and multi-objective optimization is essential. By looking at the building method and cluster gateways with different goals in mind, we can demonstrate the load calculation procedure. Our clustering, Gateway discovery management, load calculation, and load relocation design technique is based on the input variables, anticipated output, objectives, and limitations of wireless sensor networks. Next, we'll put the cluster gateway into action and examine the choices made for traffic optimization and distribution that followed. Optimal load management in wireless sensor networks is problematic due to several constraints. Wireless sensor network multi-objective optimization might use a cluster-based load distribution approach to accommodate for heterogeneous networks, for example, by spreading an ongoing gateways transmission throughout cluster nodes, Collaborative wireless sensor network protocol built on the LEACH architecture