Game-Theoretic Optimization Of Intelligent Iot Networks For Enhanced Resource Management In Precision Agriculture

Abstract

The burgeoning application of Internet of Things (IoT) technologies in agriculture has revolutionized precision farming practices. Intelligent IoT networks equipped with sensors, actuators, and edge computing capabilities offer real-time monitoring and intelligent control over crucial agricultural parameters. However, optimizing resource allocation and network performance in these dynamic environments remains a complex challenge due to competing interests among network devices and potential interference between neighboring farms. This paper proposes a novel approach for optimizing intelligent IoT networks in precision agriculture using game theory. We first model the network as a non-cooperative game where individual devices act as rational players aiming to maximize their own utilities, represented by factors like data transmission success, energy efficiency, and resource utilization. We then employ Nash equilibrium and its refinements to determine stable and efficient network configurations. To address the potential for strategic manipulation and ensure collective benefit, we further introduce cooperative game mechanisms, such as coalition formation and resource sharing protocols, to incentivize collaborative behavior among devices. The efficacy of the proposed approach is evaluated through extensive simulations with realistic agricultural scenarios. Results demonstrate significant improvements in network performance metrics, including higher data throughput, reduced energy consumption, and improved resource utilization compared to traditional non-game-theoretic approaches. We conclude by discussing the real-world implementation challenges and future research directions in game-theoretic optimization of intelligent IoT networks for sustainable and efficient precision agriculture.