Joint User Association and Resource Allocation Optimization for MEC-Enabled IoT Networks

Abstract

This paper studies a mobile edge computing (MEC) network to support emerging Internet-of-things applications, where multiple access points (APs), each attached with an MEC server, need to collect data from multiple sensors, process them, and then send computation results to the paired actuators for control. Specifically, we consider a three-phase operation protocol for data uploading, edge computing, and results downloading, where the frequency-division multiple access is implemented to accommodate communications of multiple sensors/actuators. Under this setup, we minimize the end-to-end (E2E) latency of the sensing-communication-computation-actuation loop by properly designing the user association and resource allocation policy, subject to the communication and computation resource constraints. The formulated problem, however, is a mixed-integer non-linear program that is difficult to be optimized. Despite this fact, we first search the optimal solution for user association, and then apply convex optimization for resource allocation given the obtained optimal user association. Finally, numerical results show that the proposed optimal joint design significantly reduces the E2E latency, as compared to conventional designs without such joint designs.