Energy-Aware Mobile Edge Computation Offloading for IoT Over Heterogenous Networks

The rapid development of the Internet of Things gives rise to the emergence of delay-sensitive and computation-intensive applications. Due to the inherent long delay of cloud computing and the limited resources at end devices, mobile edge computing is considered a promising approach to meet the stringent delay requirement of such demanding applications. To handle the massive connection of the Internet of Things, the 5G network is shifting toward heterogenous architecture, where each end device can access more than one edge server (e.g., base stations and access points). In the presence of multiple edge servers, this paper investigates the interesting problem of how to exploit the heterogenous computation resources at the network edge to achieve the best energy efficiency among multiple end devices while satisfying their delay requirements. We study a computation offloading management problem by jointly considering the heterogeneous computation resources, latency requirements, power consumption at end devices, and channel states. The formulated energy minimization problem falls into the category of mixed-integer and nonlinear program. To solve it efficiently, we decompose the original problem into two subproblems and propose an iterative solution framework to solve for transmission power allocation strategy and computation offloading scheme. Through simulation results, we show that the proposed solution is competitive when compared with the optimal solution. Moreover, we leverage the optimal solutions to analyze the impact of computation resource distribution on energy consumption and computation offloading decision.