Deakin University

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COMEC: computation offloading for video-based heart rate detection APP in mobile edge computing

conference contribution
posted on 2019-03-20, 00:00 authored by X Li, R Ding, Xiao LiuXiao Liu, W Yan, J Xu, H Gao, X Zheng
Medical data is invaluable in medical diagnosis. With the recent advance in Internet of Things (IoT) and non-invasive sensing, massive medical data can be gathered in real-time but how to process them efficiently becomes a big challenge for e-Health applications. In this demo paper, we investigate the idea of employing Mobile Edge Computing (MEC) which can provide computing resources close to the end devices with a variety of computation offloading models to facilitate real-time medical data processing. To evaluate this idea and find out which computation offloading model can achieve the best performance, we implement COMEC where a video-based heart rate detection APP is running in a MEC environment based on Open Air Interface (OAI). Experimental results have shown that the best performance is achieved under multi-layer offloading model where both the edge server and the cloud server are employed.



IEEE Computer Society. Conference (2018 : Melbourne, Vic.)


IEEE Computer Society Conference


1038 - 1039


Institute of Electrical and Electronics Engineers


Melbourne, Vic.

Place of publication

Piscataway, N.J.

Start date


End date






Publication classification

E1 Full written paper - refereed

Copyright notice

2018, IEEE


J Chen, L Yang

Title of proceedings

ISPA/IUCC/BDCloud/SocialCom/SustainCom : Proceedings of the 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications 2018