Enhancing internet of things network efficiency with clustering and random forest fusion techniques

Abstract

The internet of things (IoT) is a key element of the future internet, enabling the acquisition and transfer of data to improve efficiency. One challenge in IoT networks is managing the energy consumption of nodes. IoT innovation constantly evolves dynamically, contributing significantly to sustainable cities and economies. Clustering techniques can help conserve energy and extend the operational lifespan of network nodes. Cluster heads (CH) manage all cluster member (CM) nodes within their group, establishing intra-cluster and inter-cluster connections. Enhancing the CH selection process can further prolong the network lifespan. Various algorithms aim to extend the active duration of IoT nodes and the overall network lifespan. A comparison of the five algorithms shows that one algorithm is better than the others in some cases. This paper discusses how fusion techniques using the random forest (RF) algorithm can enhance energy efficiency in IoT networks. Five algorithms are compared using RF, a robust machine-learning algorithm renowned for its ensemble learning capabilities. It selects the best one based on active nodes per round, residual energy for each round, and the average end-to-end delay.