Intelligent Resource Allocation and Task Offloading Model for IoT Applications in Fog Networks: A Game-Theoretic Approach

Fog computing is an emerging paradigm that allows IoT devices and applications to process their data at the network's edge. However, fog servers have limited computational resources compared to the cloud system; therefore, they cannot accommodate the ever-increasing computational demand from IoT devices. In such a challenging environment, IoT users' decision to offload their tasks to the fog node or the central cloud server is affected by the environment's current dynamics as other IoT users are also competing to maximize their resource utilization. In this paper, we propose a computational model that considers energy consumption and transmission latency as decision parameters for task offloading of IoT applications. Second, we model the competition as a game where IoT devices' decision for the optimal distribution of tasks is captured in a joint optimization problem for energy consumption and latency. Third, we propose a decentralized task distribution algorithm where the players learn to update their strategy based on other players' actions. We also prove that the solution of the proposed algorithm converges to Nash equilibrium (NE). Finally, we carry out extensive evaluations and compare our computational model and results with existing studies.