Energy-Efficient Joint Optimization of Sensing and Computation in MEC-Assisted IoT Using Mean-Field Game

Intergrating multi-access edge computing (MEC) with the Internet of Things (IoT) is able to provide IoT sufficient computational resources in addition to its capabilities of sensing and communication. In this paper, given the limited computational and energy resources, IoT devices (IDs) are allowed to offload computational tasks to MEC severs for execution. However, as the number of IDs increases dramatically, jointly optimizing the usage of sensing, communication and computational resources becomes challenging due to the exponential growth in interactions among the IDs. In this paper, we address the energy-efficient joint optimization problem for sensing and computation in the MEC-assisted IoT system, aiming to ensure the freshness of the status update and minimize the energy consumption of IDs. To reduce the computation complexity, we introduce the concept of the general mean field N-player Markov game (GMFG), and reformulate it as a mean-field game (MFG) with teams, leveraging the network structure of states. Considering the advantages of reinforcement learning (RL) for solving dynamic problems, we propose a MFG based actor-critic algorithm (MFGAC) to minimize the long-term average system cost. Through extensive simulations, we demonstrate that the proposed method is effective and can outperform other schemes under different scenarios.