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, vol. 7 (issue 3), pp. 2421-2431

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