SenseNow: A Time-Dependent Incentive Approach for Vehicular Crowdsensing
This paper presents an incentive mechanism for vehicular crowdsensing (VCS). Here, a platform selects a set of spots or Places of sensing Interest (PsI) and outsources the collection of data from these places. In particular, the platform is interested in collecting data from most of the PsIs (spatia...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10552144/ |
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| author | Luis G. Jaimes Harish Chintakunta Paniz Abedin |
| author_facet | Luis G. Jaimes Harish Chintakunta Paniz Abedin |
| author_sort | Luis G. Jaimes |
| collection | DOAJ |
| description | This paper presents an incentive mechanism for vehicular crowdsensing (VCS). Here, a platform selects a set of spots or Places of sensing Interest (PsI) and outsources the collection of data from these places. In particular, the platform is interested in collecting data from most of the PsIs (spatial coverage) at regular and well-spread time intervals (temporal coverage). Although spatial coverage is a natural by-product of this approach, our main focus is to reach temporal coverage. To this goal, we model the interaction between participants (vehicles) as a non-cooperative game in which vehicles are the players, and the time to sample at a given PsI is the players’ strategy. Here, vehicles are rewarded for deviating from their pre-planned paths and visiting a set of PsIs. The rewarding formula is designed such that selfish vehicles trying to maximize their reward will collect high temporal coverage data. In particular, this paper analyses the effects of increasing the number of vehicle deviations on the utilities of both vehicles and the platform. |
| format | Article |
| id | doaj-art-bcc93a5a862a48e583b3c5402d7ff805 |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-bcc93a5a862a48e583b3c5402d7ff8052025-01-24T00:02:50ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-01530732110.1109/OJITS.2024.341152510552144SenseNow: A Time-Dependent Incentive Approach for Vehicular CrowdsensingLuis G. Jaimes0https://orcid.org/0000-0003-4914-6740Harish Chintakunta1Paniz Abedin2Department of Computer Science, Florida Polytechnic University, Lakeland, FL, USADepartment of Engineering, MathWorks, Santa Clara, CA, USADepartment of Computer Science, Florida Polytechnic University, Lakeland, FL, USAThis paper presents an incentive mechanism for vehicular crowdsensing (VCS). Here, a platform selects a set of spots or Places of sensing Interest (PsI) and outsources the collection of data from these places. In particular, the platform is interested in collecting data from most of the PsIs (spatial coverage) at regular and well-spread time intervals (temporal coverage). Although spatial coverage is a natural by-product of this approach, our main focus is to reach temporal coverage. To this goal, we model the interaction between participants (vehicles) as a non-cooperative game in which vehicles are the players, and the time to sample at a given PsI is the players’ strategy. Here, vehicles are rewarded for deviating from their pre-planned paths and visiting a set of PsIs. The rewarding formula is designed such that selfish vehicles trying to maximize their reward will collect high temporal coverage data. In particular, this paper analyses the effects of increasing the number of vehicle deviations on the utilities of both vehicles and the platform.https://ieeexplore.ieee.org/document/10552144/Vehicular CrowdsensingSmart MobilityAlgorithmic Game TheoryTransportation Systems |
| spellingShingle | Luis G. Jaimes Harish Chintakunta Paniz Abedin SenseNow: A Time-Dependent Incentive Approach for Vehicular Crowdsensing IEEE Open Journal of Intelligent Transportation Systems Vehicular Crowdsensing Smart Mobility Algorithmic Game Theory Transportation Systems |
| title | SenseNow: A Time-Dependent Incentive Approach for Vehicular Crowdsensing |
| title_full | SenseNow: A Time-Dependent Incentive Approach for Vehicular Crowdsensing |
| title_fullStr | SenseNow: A Time-Dependent Incentive Approach for Vehicular Crowdsensing |
| title_full_unstemmed | SenseNow: A Time-Dependent Incentive Approach for Vehicular Crowdsensing |
| title_short | SenseNow: A Time-Dependent Incentive Approach for Vehicular Crowdsensing |
| title_sort | sensenow a time dependent incentive approach for vehicular crowdsensing |
| topic | Vehicular Crowdsensing Smart Mobility Algorithmic Game Theory Transportation Systems |
| url | https://ieeexplore.ieee.org/document/10552144/ |
| work_keys_str_mv | AT luisgjaimes sensenowatimedependentincentiveapproachforvehicularcrowdsensing AT harishchintakunta sensenowatimedependentincentiveapproachforvehicularcrowdsensing AT panizabedin sensenowatimedependentincentiveapproachforvehicularcrowdsensing |