A Hybrid Genetic-Chaos Algorithm for Energy-Efficient Cluster Head Selection and Reduced Energy Consumption in Wireless Sensor Networks
DOI:
https://doi.org/10.29304/jqcsm.2025.17.42545Keywords:
Wireless Sensor Networks (WSN), Supervised Machine Learning, Energy-Efficient Routing, Energy Consumption Minimization, Network Lifetime Extension, Smart CitiesAbstract
WSNs are an essential part of the current data-intensive technology, including environmental monitoring and industrial automation. The operational lifetime of a WSN and energy usage is a key difficulty to maximize throughput or minimize energy consumption. In this study, we explore one such novel way of using supervised learning machine algorithms in dealing with this problem. The research opens the capability of designing and executing supervised machine learning algorithms in the WSNs in order to maximize energy usage. These intelligent routing algorithms allow the decision to be made at the node level reducing state on the network using historical data and network parameters. Using the supervised learning, the ability to change the operating parameters and routing strategies of the nodes dynamically to save energy is satisfied. The research proves the effectiveness of supervised machine learning by minimizing energy consumption in WSNs based on extensive simulations and real world experiments. Results indicate a considerable increase in network lifetime, and more efficient data transferring. The use of such algorithms not only helps in minimizing the amount of energy wasted but also lengthens the lifetime of the network thus it becomes long lasting and more viable to use over a long period of deployment. The research elevates work towards WSNs by becoming more energy- efficient, resilient, and adaptable. These findings are important to several spheres including smart cities or precision agriculture, since it allows developing reliable and sustainable wireless sensors networks that are capable of surviving in the data-rich realms of the future.
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