P. Calyam, and
In IEEE International Conference on Future Internet of Things and Cloud (FiCloud),
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 facial 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 facial 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.
author = "H. Trinh and D. Chemodanov and S. Yao and Q. Lei and B. Zhang and F. Gao and P. Calyam and K. Palaniappan",
title = "Energy-aware mobile edge computing for low-latency visual data processing",
year = 2017,
journal = "In IEEE International Conference on Future Internet of Things and Cloud (FiCloud)",
month = "Jan",
keywords = "iot-based applications, cloud computing, energy awareness, visual data processing, mobile edge cloud",
url = "https://par.nsf.gov/servlets/purl/10048928"
H. Trinh, D. Chemodanov, S. Yao, Q. Lei, B. Zhang, F. Gao, P. Calyam, and K. Palaniappan. Energy-aware mobile edge computing for low-latency visual data processing. In IEEE International Conference on Future Internet of Things and Cloud (FiCloud), January 2017.