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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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.

MCS is an emerging paradigm that leverages the pervasiveness of mobile, wearable, and vehicle-mounted devices to collect data from urban environments for ubiquitous service provisioning. In order to manage MCS application data streams efficiently, a scalable computing infrastructure hosting heterogeneous and distributed resources is critical. FC, as a geodistributed computing paradigm, is a key enabler for this requirement as it bridges cloud servers and smart mobile devices. Research on the integration of MCS with FC has recently started to be explored recognizing the requirements of MCS and their coexistence with the cyberphysical systems. In this article, we analyze the state of the art of FC solutions in MCS systems. After brief overview of MCS we emphasize the link between MCS and the FC. We then investigate the existing fog-based MCS architectures in detail by focusing on their building blocks, as well as the challenges that yet remain un-addressed. Our detailed review on the subject results in a taxonomy of FC solutions in MCS systems. In particular, we highlight the node structures, the information exchanged, the resource and service management, and the type of solutions adopted concerning privacy and security. Moreover, we provide a thorough discussion on the open issues and challenges by reporting useful insights for the researchers in MCS and FC.

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