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

Abstract

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 Crowd Sensing (MCS) as it allows to convey and distribute sensing tasks at the edges of the network, enabling also (local) sensing data collection from devices. This paper, 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 wellknown 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 a simple selection based on cooperativeness scores. Keywords-mobile crowdsensing; multi-access edge computing; human-driven edge computing; social mobilityI. INTRODUCTION The widespread diffusion of mobile devices started at the end of the last century has experienced an exponential growth in recent years and we can assert that nowadays in developed countries, any person owns at least one mobile device. Such ubiquitous devices are typically equipped with sensors and both long-range and short-range communication interfaces. Sensors as accelerometers, gyroscopes, GPS, cameras, microphones, and so on allow to collect data in

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