Energy-Efficient and Reliable MEC Offloading for Heterogeneous Industrial IoT Networks

Abstract

The ultra-reliable and low latency communication (URLLC) and massive machine type communication (mMTC) in 5G are envisioned to support intelligent automation in the Factories-of-the-Future (FoF) environment. Mobile-edge computing (MEC) is thought of as a promising system for realization. However, the computation work cannot be done without reliable transmission. In this work, rather than simply investigating task offloading problem, the reliability of radio transmission is jointly considered under heterogeneous industrial IoT networks. A 2-tier MEC-cloud framework is provided, wherein the IoT mobile devices (MDs) are able to partition tasks and offload them to the MEC and the cloud server through the reliable transmission. A two-step algorithm named opportunity-cost-based offloading algorithm (OCBOA) is proposed to jointly optimize the allocation of communication and computation resources for task offloading with the minimum energy consumption and offloading failure probability. The experiments show that our low-complexity algorithm outperforms the other heuristic algorithms on resource allocation while satisfying the QoS requirements of the MDs.