Energy-Aware Mobile Edge Computing for Low-Latency Visual Data Processing

Abstract

New opportunities exist for applications such as disaster incident response that can benefit from the convergence of Internet of Things (IoT) and cloud computing technologies. Particularly, new paradigms such as Mobile Edge Computing (MEC) are becoming feasible to handle the data deluge occurring in the network edge to gain insights that assist in real-time decision making. In this paper, we study the potential of MEC to address application issues related to energy management on constrained IoT devices with limited power sources, while also providing low-latency processing of visual data being generated at high resolutions. Using a face recognition application that is important in disaster incident response scenarios, we analyze the tradeoffs in computing policies that offload visual data processing (i.e., to an edge cloud or a core cloud) at low-to-high workloads, and their impact on energy consumption under different visual data consumption requirements (i.e., users with thick clients or thin clients). From our empirical results obtained from experiments with our face recognition application on a realistic edge and core cloud testbed, we show how MEC can provide flexibility to users who desire energy conservation over low-latency or vice versa in the visual data processing.