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

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.