Smart City Gnosys

Smart city article details

Title	In-Network Collaborative Mobile Crowdsensing
ID_Doc	31040
Authors	Du Y.; Issarny V.; Sailhan F.
Year	2020
Published	2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020
DOI	http://dx.doi.org/10.1109/PerComWorkshops48775.2020.9156268
Abstract	Our work aims to make opportunistic crowdsensing a reliable means of detecting urban phenomena, as a component of smart city development. We believe that the optimal method for achieving this is by enforcing the cost-effective collection of high quality data. We then investigate a supporting middleware solution that reduces both the network traffic and computation at the cloud. To this end, our research focuses on defining a set of protocols that together implement 'context-aware in-network collaborative mobile crowdsensing' by combining: (i) The inference of the crowdsensors' physical context so as to characterize the gathered data; (ii) The context-aware grouping of crowdsensors to share the workload and filter out low quality data; and (iii) Data aggregation at the edge to enhance the knowledge transferred to the cloud. © 2020 IEEE.
Author Keywords	Context Inference; Crowdsensing; D2D Collaboration; Edge Computing; Environment Sensing

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