Document - Impact of evolutionary community detection algorithms for edge selection strategies

2020

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Impact of evolutionary community detection algorithms for edge selection strategies

Barsocchi P., Belli D., Chessa S., Foschini L., Girolami M.

Multi-access edge computing Mobile Edge Community Detection Mobile edge Community detection CrowdSensing Multi-access Edge Computing

The combination of the edge computing paradigm with Mobile CrowdSensing (MCS) is a promising approach. However, the selection of the proper edge nodes is a crucial aspect that greatly affects the performance of the extended architecture. This work studies the performance of an edge-based MCS architecture with ParticipAct, a real-word experimental dataset. We present a community-based edge selection strategy and we measure two key metrics, namely latency and the number of requests satisfied. We show how they vary by adopting three evolutionary community detection algorithms, TILES, Infomap and iLCD configured by changing several configuration settings. We also study the two metrics, by varying the number of edge nodes selected so that to show its benefit.

Source: GLOBECOM 2020 - IEEE Global Communications Conference, Taipei, Taiwan, December 07-11, 2020

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@inproceedings{oai:it.cnr:prodotti:438906,
	title = {Impact of evolutionary community detection algorithms for edge selection strategies},
	author = {Barsocchi P. and Belli D. and Chessa S. and Foschini L. and Girolami M.},
	doi = {10.1109/globecom42002.2020.9348085},
	booktitle = {GLOBECOM 2020 - IEEE Global Communications Conference, Taipei, Taiwan, December 07-11, 2020},
	year = {2020}
}