A Novel Energy Efficient Routing Protocol Using K-Means Clustering for Mobile Ad-Hoc Networks

Abstract

The advancement of mobile ad-hoc networks (MANETs) has sparked an increasing interest in high-efficiency communication protocols. These networks are exposed to various challenges due to their dynamic topologies, sporadic connections, and dense nodes. Consequently, energy efficiency is a key concern in the design of routing protocols for MANETs. Machine learning techniques provide optimal solutions for such routing protocols as they enable the determination of appropriate routing schemes and efficient energy consumption, without relying on any prior knowledge of the environment. This paper presents an energy efficient routing protocol that uses machine learning techniques to analyze the network environment to identify optimal paths. By leveraging the power of machine learning algorithms, the proposed solution is capable of providing the optimal energy aware route for data transmission. This protocol is able to precisely identify the appropriate route to ensure energy-efficient wireless communication while accommodating the dynamic and ad-hoc nature of MANETs. Comparative simulations are conducted to show the effectiveness of the proposed protocol with respect to various benchmark solutions. The proposed energy efficient routing protocol proves to be a viable solution for reducing the power consumption of the nodes in MANETs, thereby improving overall network performance.