Document - A Probabilistic Model for the Deployment of Human-enabled Edge Computing in Massive Sensing Scenarios

2020

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A Probabilistic Model for the Deployment of Human-enabled Edge Computing in Massive Sensing Scenarios

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

Cloud computing Mobile crowdsensing social mobility Computational modeling multiaccess edge computing (MEC) Information Systems Probabilistic logic Hardware and Architecture Computer Networks and Communications Multi-access Edge Computing Social mobility Edge computing Human-enabled Edge Computing Signal Processing Internet of things Human-enabled edge computing (HEC) Computer Science Applications Computer architecture Sensors mobile crowdsensing (MCS)

Human-enabled Edge Computing (HEC) is a recent smart city technology designed to combine the advantages of massive Mobile CrowdSensing (MCS) techniques with the potential of Multi-access Edge Computing (MEC). In this context, the architectural hierarchy of the network shifts the management of sensing information close to terminal nodes through the use of intermediate entities (edges) bridging the direct Cloud-Device communication channel. Recent proposals suggest the implementation of those edges, not only employing fixed MEC nodes, but also opportunistically using as edge nodes mobile devices selected among the terminal ones. However, inappropriate selection techniques may lead to an overestimation or an underestimation of the number of nodes to be used in such a layer. In this work, we propose a probabilistic model for the estimation of the number of mobile nodes to be selected as substitutes of fixed ones. The effectiveness of our model is verified with tests performed on real-world mobility traces.

Source: IEEE Internet of Things Journal 7 (2020): 2421–2431. doi:10.1109/JIOT.2019.2957835

Publisher: Institute of Electrical and Electronics Engineers, New York, NY, Stati Uniti d'America

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@article{oai:it.cnr:prodotti:415559,
	title = {A Probabilistic Model for the Deployment of Human-enabled Edge Computing in Massive Sensing Scenarios},
	author = {Belli D. and Chessa S. and Foschini L. and Girolami M.},
	publisher = {Institute of Electrical and Electronics Engineers, New York, NY, Stati Uniti d'America},
	doi = {10.1109/jiot.2019.2957835},
	journal = {IEEE Internet of Things Journal},
	volume = {7},
	pages = {2421–2431},
	year = {2020}
}