Version 1
: Received: 2 September 2024 / Approved: 3 September 2024 / Online: 10 September 2024 (12:37:49 CEST)
How to cite:
Jaimes, L. G.; White, C.; Abedin, P. Bridging the Gap: An Algorithmic Framework for Vehicular Crowdsensing. Preprints2024, 2024090701. https://doi.org/10.20944/preprints202409.0701.v1
Jaimes, L. G.; White, C.; Abedin, P. Bridging the Gap: An Algorithmic Framework for Vehicular Crowdsensing. Preprints 2024, 2024090701. https://doi.org/10.20944/preprints202409.0701.v1
Jaimes, L. G.; White, C.; Abedin, P. Bridging the Gap: An Algorithmic Framework for Vehicular Crowdsensing. Preprints2024, 2024090701. https://doi.org/10.20944/preprints202409.0701.v1
APA Style
Jaimes, L. G., White, C., & Abedin, P. (2024). Bridging the Gap: An Algorithmic Framework for Vehicular Crowdsensing. Preprints. https://doi.org/10.20944/preprints202409.0701.v1
Chicago/Turabian Style
Jaimes, L. G., Craig White and Paniz Abedin. 2024 "Bridging the Gap: An Algorithmic Framework for Vehicular Crowdsensing" Preprints. https://doi.org/10.20944/preprints202409.0701.v1
Abstract
In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing.
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
