Improving Network Efficiency Through an Energy-Aware Geographic Routing Protocol

Although wireless sensor networks (WSNs) are crucial parts of modern communication systems, their energy consumption and network longevity are severely constrained. Enhancements to the geographic and energy-efficient routing protocol (GF-EERP) are suggested in this study. This we do by use of precise node location info which in turn improves routing decisions.

The protocol will also incorporate reinforcement learning algorithms. To make adaptive routing techniques possible, which improve network efficiency and accomplish balanced traffic distribution. On a number of performance metrics, including latency, packet delivery ratio, network lifetime, and energy consumption, the suggested approach will be carefully contrasted and examined with alternative routing methods. The simulation results will demonstrate that, in a variety of network circumstances, the enhanced GF-EERP will offer superior scalability and reliability compared to the current protocols. In order to enhance energy-efficient communications in WSNs and, consequently, guarantee dependable and sustainable network deployment, this study will highlight the significance of combining geographic routing with machine learning techniques.

: Energy Efficiency, GF-EERP, Geographic Routing Protocol, Network Lifetime, Node Localization, Reinforcement Learning

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