Human-Enabled Edge Computing: Exploiting the Crowd as a Dynamic Extension of Mobile Edge Computing

Bellavista P., Chessa S., Foschini L., Gioia L., Girolami M.

cyber-physical systems  Electrical and Electronic Engineering  Computer Networks and Communication  Computer Science Applications  Computer Science Applications1707 Computer Vision and Pattern Recognition  Computer Networks and Communications  cloud computing  Internet of Things 

e interaction with mobile nodes via local control decisions and actuation. MEC has already been proposed as an enabler for several Internet of Things and cyber-physical systems application sce- narios, and also mutual benefits due to the inte- gration of MEC and mobile crowdsensing (MCS). The article originally proposes human-driven edge computing (HEC) as a new model to ease the provisioning and to extend the coverage of tra- ditional MEC solutions. From a methodological perspective, we show how it is possible to exploit MCS i) to support the effective deployment of fixed MEC (FMEC) proxies and ii) to further extend their coverage through the introduction of impromptu and human-enabled mobile MEC (M2EC) proxies. In addition, we describe how we have implemented these novel concepts in the MCS ParticipAct platform through the integration of the MEC Elijah platform in the ParticipAct liv- ing lab, an ongoing MCS real-world experiment that involved about 170 students at the University of Bologna for more than two years. Reported experimental results quantitatively show the effec- tiveness of the proposed techniques in elastically scaling the load at edge nodes according to run- time provisioning needs.

Source: IEEE communications magazine (Print) 56 (2018): 145–155. doi:10.1109/MCOM.2017.1700385

Publisher: Communications Society of Institute of Electrical and Electronics Engineers], [New York,, Stati Uniti d'America

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