Energy Consumption Minimization using Data Compression in Mobile Edge Computing

In recent years, many researchers have done a lot of work on mobile edge computing (MEC). However, existing works on energy minimization of MEC systems mainly focus on comparing energy consumption between accomplishing tasks locally and computing tasks on MEC server, failing to explore energy optimal solutions for computing tasks on MEC server. In this paper, we consider a single-user-single-server MEC system, working under latency constraint. In order to reduce the energy consumption of mobile device and meet the latency constraint, we use computation offloading to accomplish large tasks. However, energy consumed by mobile devices (MD) for the transmitting process could be decreased further by compressing data transmitted from MD to mobile-edge server (MEC server). But energy consumed when compressing data on MD increases while the compression rate decreases. Therefore, we develop a solution to figure out the optimal compression rate to minimize the total energy for MD to accomplish one simple task and to meet the latency constraint at the same time. With the optimal compression rate, energy consumption for task-process could be reduced by 38.37% to 60.49% when the latency constraint varies from 0.1s to 0.8s.