Can I read Green Crowdsensing by Learning Inter-Auction Mappings and Non-local Graph Constraints on EtoBox?

Green Crowdsensing by Learning Inter-Auction Mappings and Non-local Graph Constraints by Gendy, Maggie E. ;Al-Kabbany, Ahmad ;Badran, Ehab F. is a scholarly article available to read on EtoBox.

What is Green Crowdsensing by Learning Inter-Auction Mappings and Non-local Graph Constraints about?

In this short paper, we envisage a framework for green crowdsensing that exploits inter-auction correlations, and that incorporates label propagation on graphs using non-local constraints. Mobile crowdsensing (MCS) is a promising paradigm that relies on the pervasiveness of smart, sensor-rich devices for mapping phenomena of interest. Energy management during sensing campaigns is an open problem in building efficient MCS systems. From the perspective of the participants in sensing campaigns, it is convenient to have a platform (which orchestrates the task-participant matching process) that is energy aware, so that battery life and energy demands of various sensors are taken into consideration during task assignment. From the perspective of service demanders, energy-aware platforms are essential towards harnessing the power of MCS without compromising cost and eco-consciousness. Rather than dealing with auctions as individual and separate events in time and space, the framework envisaged in this research formulates green crowdsensing as a random field on irregular graphs, with neighborhood defined in time (consecutive auctions) and in space (auctions in different geographic regions)

Author

Gendy, Maggie E. ;Al-Kabbany, Ahmad ;Badran, Ehab F.

Publisher

IEEE

Published

2020

Language

EN