Optimizing Information Freshness in MEC-Assisted Status Update Systems with Heterogeneous Energy Harvesting Devices

Abstract

The ever-growing number of Internet of Things (IoT) devices makes multi-access edge computing (MEC) assisted status update system more and more attractive, which can be deployed to enable remote data acquisition and analysis from urban space. The ambient computing resource at edge automatically extracts valuable status update information from the data collected by IoT devices, which supports the real-time remote monitoring applications. In this paper, we employ the concept of Age of Information (AoI) to quantify the freshness of status updates. To combat the limited battery capacity at IoT devices, energy harvesting (EH) is leveraged to capture the green energy from ambient environment. Specifically, we investigate an age minimization problem by considering the randomness in energy arrivals, heterogeneity in harvesting mode, and the stochasticity in transmission and computing process. The formulated problem is a long-term stochastic optimization problem. Then, we transform the original problem into a series of per-time slot deterministic optimization problem. An online scheduling policy is proposed to obtain the energy management decisions at devices, and the transmission and computing scheduling decisions among multiple devices without any prior knowledge on the network dynamics, which is facilitated to be implemented. Simulation results show that the performance of our proposed algorithm is competitive when compared with other existing schemes.