Abstract

In a lot of fields, like watching the environment, watching military bases, and developing smart cities, wireless sensor networks (WSNs) are particularly significant. One of the main difficulties with WSNs is how to use energy intelligently so that the network lasts as long as feasible. Using cluster-based topologies is a fantastic way to get the most out of energy utilization. In these topologies, a set of nodes in the network known as Cluster Heads (CHs) are responsible for communicating with the base station and other sensor nodes. It is still hard to choose a CH because WSNs are continually evolving and there are a lot of elements to worry about, like energy, coverage, and node density. Traditional approaches for choosing a CH don’t function well on heterogeneous and dynamic WSNs because they use static algorithms or single-factor optimization. To make the network more stable, energy-efficient, and long-lasting, we need an algorithm that can swiftly adjust to changes in the network and take a number of different things into account at once. The Adaptive Multi-Factor Co-Evolutionary Algorithm with Local Search, or AMCE-LS, is the main focus of this study. The approach has a co-evolutionary framework that uses a variety of adaptive fitness criteria to give nodes a score. The measures include coverage, the distance between nodes in the same cluster, the degree of each node, and the energy that is left behind. Adding a local search refinement step, which makes the CH selection even better, speeds up the convergence and makes the response better. Because it can monitor the network in real time, the adaptive technique can modify the weighting variables on the fly. The recommended AMCE-LS approach works better than earlier algorithms like LEACH, PSO, and DEEC when it comes to testing the durability of a network, its energy efficiency, and its packet delivery ratio. In dense node installations, the adaptive multi-factor technique can help networks persist and stay stable for up to 30% longer.

Authors

Minal Sudarshan Hardas¹, M. Ezhilvendan²
Shivajirao S. Jondhale College of Engineering, India¹, Panimalar Engineering College, India²

Keywords

Wireless Sensor Networks, Cluster Head Selection, Co-Evolutionary Algorithm, Local Search, Energy Efficiency