Smart City Gnosys

Smart city article details

Title Towards Uniform Urban Map Coverage In Vehicular Crowd-Sensing: A Decentralized Incentivization Solution
ID_Doc 58452
Authors Di Martino S.; Starace L.L.L.
Year 2022
Published IEEE Open Journal of Intelligent Transportation Systems, 3
DOI http://dx.doi.org/10.1109/OJITS.2022.3211540
Abstract Vehicular Crowd-Sensing (VCS) is a well-known data collection approach leveraging sensors of connected vehicles to efficiently gather contextual information in urban environments. High-mileage vehicles such as taxis are often regarded as effective VCS platforms, due to their pervasiveness in modern cities, even though the road network coverage achievable by these vehicles is still an open issue. Indeed, their drivers generally follow the most-efficient route to destination, leading to major roads being frequently visited, while others are often neglected. To address this issue, many centralized incentivization solutions have been proposed to recruit/reward drivers accepting minor detours towards roads with higher sensing demand. However, these works mostly focus on assigning specific sensing tasks to drivers, rather than achieving an overall better-balanced urban sensing coverage, which is nonetheless required for many use cases, such as air quality monitoring. To fill this gap, we present ROUTR, an incentivization budget-aware routing solution designed to achieve more uniform coverage in VCS without requiring central coordination, thus significantly reducing back-end infrastructure costs. We empirically evaluated the proposal using taxi traces collected in the City of San Francisco. Results highlighted that, even with small incentivization budgets, our proposal leads to significantly more uniform urban road network coverage. © 2020 IEEE.
Author Keywords intelligent transportation systems; Internet of Vehicles; routing; smart cities; vehicular crowd-sensing


Similar Articles


Id Similarity Authors Title Published
31074 View0.9Xu, SS; Chen, XL; Pi, XD; Joe-Wong, C; Zhang, P; Noh, HYIncentivizing Large-Scale Vehicular Crowdsensing System For Smart City ApplicationsSENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2019, 10970 (2019)
40944 View0.887Meng D.; Han K.Optimizing Vehicle-Passenger Matching For Online Ride-Hailing With Vehicular Crowd-SensingIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC (2023)
10605 View0.885Chen X.; Xu S.; Fu H.; Joe-Wong C.; Zhang L.; Noh H.Y.; Zhang P.Asc: Actuation System For City-Wide Crowdsensing With Ride-Sharing Vehicular PlatformSCOPE 2019 - Proceedings of the 2019 International Science of Smart City Operations and Platforms Engineering (2019)
60991 View0.871Olariu, SVehicular Crowdsourcing For Congestion Support In Smart CitiesSMART CITIES, 4, 2 (2021)
37900 View0.868Suleymanoglu B.; Toth C.; Masiero A.; Ladai A.Monitoring The Environment In Smart Cities: The Importance Of Geospatial Location ReferencingInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 48, M-1-2023 (2023)
6772 View0.867Bian J.; Xiong H.; Wang Z.; Zhou J.; Ji S.; Chen H.; Zhang D.; Dou D.Afcs: Aggregation-Free Spatial-Temporal Mobile Community SensingIEEE Transactions on Mobile Computing, 22, 9 (2023)
60990 View0.867Yu T.-Y.; Zhu X.; Maheswaran M.Vehicular Crowdsensing For Smart CitiesHandbook of Smart Cities: Software Services and Cyber Infrastructure (2018)
31075 View0.866Alsaqabi Y.; Krishnamachari B.Incentivizing Private Data Sharing In Vehicular Networks: A Game-Theoretic ApproachIEEE Vehicular Technology Conference (2023)
60988 View0.864Elbery A.; Hassanein H.S.; Zorba N.Vehicular Crowd Management: An Iot-Based Departure Control And Navigation SystemIEEE International Conference on Communications, 2020-June (2020)
16150 View0.864Guastella D.A.; Pournaras E.Cooperative Multi-Agent Traffic Monitoring Can Reduce Camera SurveillanceIEEE Access, 11 (2023)