Unsupervised Machine Learning based Energy Efficient Routing for Mobile Ad-Hoc Networks

Abstract

Mobile Ad-hoc Networks (MANETs) are temporary networks formed by a group of mobile hosts without the need for centralized administration or specific support services. Energy consumption is a critical issue in MANETs due to their reliance on limited battery resources. Reducing energy consumption is crucial for increasing network lifespan and throughput. Existing energy-saving techniques often fall short in their effectiveness. This research proposes a novel approach that combines a proactive MANET routing protocol with an energy-efficient strategy to address these limitations. The proposed solution considers both node mobility and energy levels in the routing process. Traditional AODV routing relies on flooding, which broadcasts RREQ packets to all nodes within the sender's range. This often leads to unnecessary retransmissions of RREQ and RREP packets, resulting in collisions and network congestion. To overcome this issue, we propose an optimized route discovery mechanism for AODV. The key idea is to leverage the K-means clustering algorithm to select the optimal cluster of nodes to forward RREQ packets instead of relying on broadcasting. This approach aims to alleviate network congestion and reduce end-to-end delay by minimizing unnecessary control packet transmissions.