A Social-Driven Edge Computing Architecture for Mobile Crowd Sensing Management

The multi-access edge computing (MEC) architectural model has fostered the development of new human-driven edge computing (HEC) frameworks that extend the coverage of traditional MEC solutions leveraging people roaming around with their devices. HEC is a well-suited architecture for human-centered technologies such as mobile crowdsensing (MCS) as it allows conveying and distributing sensing tasks at the edges of the network, also enabling (local) sensing data collection from devices. This article, through the joint use of HEC and MCS paradigms, introduces a new social-driven edge computing architecture based on incentives and centrality measures. The core idea is to add social MEC (SMEC) nodes to complement the traditional edge nodes (i.e., the main actors of the middle layer of the standard MEC architecture), acting as bridges between other devices and the cloud. The principle that underlies the SMEC selection is based on the attitude of the users in performing tasks and on their measures of centrality. In addition, we report extensive experimental results based on co-location traces and cooperativeness scores extracted from the ParticipAct living lab, a well-known MCS dataset based on data collected between 2013 and 2015 from 170 students of the University of Bologna, that show how the selection based on centrality measurements returns greater benefits than simple selection based on cooperativeness scores.