An Energy-efficient Task Offloading Solution for MEC-based IoT in Ultra-dense Networks

By pushing computation to the mobile network edge, Multi-access Edge Computing (MEC) has been an enabler for the stringent latency and energy requirements of the new Internet of Things (IoT) services. On the other hand, ultra-dense heterogeneous networks with wireless backhaul have been proposed as a low-cost solution, allowing Network Operators (NOs) to extend the network capability, by deploying densified close-proximity small-cells and hence supporting a large number of low-latency low-energy IoT devices. In this paper, we study the problem of IoT task offloading in a MEC-enabled heterogeneous network, which to the best of our knowledge, is the first attempt to thoroughly explore the task offloading problem in a heterogeneous network with MEC support and wireless backhaul. We jointly optimize the offloading decision, transmission power, and the allocation of radio and computational resources, with the objective of minimizing the devices energy consumption, while respecting their latency deadline. We mathematically formulate our problem as a non-convex mixed-integer program, and due to its complexity, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) method for providing an approximate solution on the original problem. Through numerical analysis, we perform simulations based on multiple scenarios, and find out how NOs can respond to the requested load and help in minimizing the total devices energy consumption.