Smart City Gnosys

Smart city article details

Title	Optimal Transport For Mobile Crowd Sensing Participants
ID_Doc	40549
Authors	Azmy S.B.; Zorba N.; Hassanein H.S.
Year	2019
Published	IEEE Wireless Communications and Networking Conference, WCNC, 2019-April
DOI	http://dx.doi.org/10.1109/WCNC.2019.8886074
Abstract	Smart cities are becoming more complex and greater volumes of data are required for its efficient operation. Mobile Crowdsensing (MCS) is a paradigm that employs smartphones as instruments to collect data, where the recruitment of participants is based on rewards and incentives. However due to the mobile nature of people, sensing may not be available in a specific area of interest, reducing the quality of the MCS inference of that region. In this paper, we propose a method that utilizes optimal transport so that the MCS administrator could direct participants towards areas with poor quality to improve overall quality. An analysis of optimal transport is presented where the method is evaluated using computer simulations, where it is shown to be efficient for moving participants among spatiotemporal cells. © 2019 IEEE.
Author Keywords	coverage quality metric; data collection; internet of things; mobile crowdsensing; optimal transport; sensor networks; source quality

Similar Articles

Id	Similarity	Authors	Title	Published
27660	0.894	Dasari V.S.; Kantarci B.; Pouryazdan M.; Foschini L.; Girolami M.	Game Theory In Mobile Crowdsensing: A Comprehensive Survey	Sensors (Switzerland), 20, 7 (2020)
31074	0.89	Xu, SS; Chen, XL; Pi, XD; Joe-Wong, C; Zhang, P; Noh, HY	Incentivizing Large-Scale Vehicular Crowdsensing System For Smart City Applications	SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2019, 10970 (2019)
48813	0.887	Rontini M.; Bassem C.; Montori F.	Simulating Realistic User Mobility For Mobile Crowdsensing Using Tacsim: A Performance Study	Proceedings - 2025 IEEE International Conference on Smart Computing, SMARTCOMP 2025 (2025)
16704	0.885	Montori F.; Cortesi E.; Bedogni L.; Capponi A.; Fiandrino C.; Bononi L.	Crowdsensim 2.0: A Stateful Simulation Platform For Mobile Crowdsensing In Smart Cities	MSWiM 2019 - Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (2019)
37261	0.884	Chowdhury C.; Roy S.	Mobile Crowd-Sensing For Smart Cities	Smart Cities: Foundations, Principles, and Applications (2017)
41420	0.883	Prochazka J.; Plasilova A.	Passive Mobile Crowdsensing For Determining The Volume Of Passengers In Public Transport	Proceedings of 2023 2nd International Conference on Informatics, ICI 2023 (2023)
16708	0.877	Fiandrino C.; Capponi A.; Kliazovich D.; Bouvry P.	Crowdsensing Architectures For Smart Cities	Nanosensors for Smart Cities (2020)
60990	0.877	Yu T.-Y.; Zhu X.; Maheswaran M.	Vehicular Crowdsensing For Smart Cities	Handbook of Smart Cities: Software Services and Cyber Infrastructure (2018)
52562	0.876	Zhang F.; Yu Z.; Liu Y.; Cui H.; Guo B.	Spatio-Temporal Feature Based Multi-Participant Recruitment In Heterogeneous Crowdsensing	Proceedings - 2022 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022 (2022)
16718	0.876	Plašilová A.; Procházka J.	Crowdsensing Technologies For Optimizing Passenger Flows In Public Transport	1st International Conference in Advanced Innovation on Smart City, ICAISC 2023 - Proceedings (2023)