AoI-Aware Wireless Resource Allocation of Energy-Harvesting-Powered MEC Systems

Mobile-edge computing (MEC) has been deployed to enhance the data analysis performance of Internet of Things (IoT) and alleviate the shortage of computing resources for IoT devices. At the same time, energy harvesting (EH) is considered as a potential technology to prolong the network lifetime of the energy-limited IoT devices. In this article, we investigate the data analysis scenario in IoT architecture, where data are generated by wireless devices (WDs) and uploaded to the MEC server for centralized data processing. WDs, powered by the EH technology, upload the collected data in a frequency-division multiple access (FDMA) manner. Our goal is to maximize the long-term average system utility under the constraint of the Age of Information (AoI) and the limited system resources, by jointly optimizing the communication resource allocation, the data generating and discarding strategies, and the computing resource allocation. In order to solve the time-related AoI constraint, a series of virtual queues are applied to rewrite the optimization problem. To deal with the time-varying channel and the randomly data arrival, we propose a Lyapunov-based algorithm, decoupling the optimizing problem into several subproblems which can be fixed by traditional optimization algorithms and successive convex approximation (SCA) algorithm. With rigorous analysis, the proposed algorithm is proved to be an asymptotically optimal. Numerical results verify the theoretical analysis and reveal the effectiveness of the Lyapunov-based algorithm.