Energy Efficient Routing using Game Theory for Wireless Sensor Networks

Abstract

Over time, wireless sensor networks with limited energy resources have grappled with energy conservation challenges. These energy-constrained sensor nodes are expected to operate over extended periods, especially for critical data-sensing applications. Low-energy devices often struggle to transmit data packets effectively, leading to issues such as packet dissipation and compromised accuracy. As a result, the persistent energy challenges in WSNs underscore the importance of developing energy-efficient routing protocols. In response, we propose an evolutionary game theory-based path selection model to enhance energy efficiency in WSNs. In this approach, nodes are evaluated based on their residual energy and their distance to the next hop. These evaluations are integrated into a payoff matrix. Distances to the next hop are computed for both optimal and opportunistic routing scenarios, with these distances influencing the determination of the most efficient transmission route within the payoff matrix. The choice of path for each node primarily hinges on the nature of the data, distinguishing between critical and non-critical data. It's imperative to transmit critical data efficiently, and for this purpose, our game theory model prioritizes shorter distances for data destined for the subsequent hop, particularly for nodes in processing stages. The proposed GTRS model demonstrated the best energy distribution compared to existing GEEC and ECGD models.