User Association and Resource Allocation for MEC-Enabled IoT Networks

This paper studies a mobile edge computing (MEC) network to support emerging applications for Internet-of-things, where multiple access points, 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 result 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 solved. Despite this fact, we obtain the optimal solution via using the brute-force search for user association, and convex optimization for resource allocation under given user associations. Next, to reduce the computation complexity from the brute-force search, we propose an alternative algorithm, where the user association and resource allocation are optimized iteratively in an alternating manner, via concave-convex procedure and convex optimization, respectively. Finally, numerical results show that the proposed designs significantly reduce the E2E latency, compared with conventional separate designs.