Abstract
In vehicular crowdsensing (VCS) applications, vehicular participants should be carefully selected to meet the limited operation budget while providing sufficient quality of sensing. In this paper, we propose a matching based vehicular participant recruiting (MVP) strategy. The MVP strategy in-centivizes detouring vehicles to the target region, which allows the maximization of the sensing quality under the given VCS operation budget. The preliminary simulation results based on real traces demonstrate that MVP outperforms the existing vehicular crowdsensing strategy.
Original language | English |
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Title of host publication | 2019 IEEE Vehicular Networking Conference, VNC 2019 |
Editors | Danijela Cabric, Onur Altintas, Tim Leinmueller, Hongwei Zhang, Takamasa Higuchi |
Publisher | IEEE Computer Society |
ISBN (Electronic) | 9781728145716 |
DOIs | |
Publication status | Published - 2019 Dec |
Event | 2019 IEEE Vehicular Networking Conference, VNC 2019 - Los Angeles, United States Duration: 2019 Dec 4 → 2019 Dec 6 |
Publication series
Name | IEEE Vehicular Networking Conference, VNC |
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Volume | 2019-December |
ISSN (Print) | 2157-9857 |
ISSN (Electronic) | 2157-9865 |
Conference
Conference | 2019 IEEE Vehicular Networking Conference, VNC 2019 |
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Country/Territory | United States |
City | Los Angeles |
Period | 19/12/4 → 19/12/6 |
Bibliographical note
Funding Information:ACKNOWLEDGMENT This was was supported in part by National Research Foundation (NRF) grant (No. 2017R1E1A1A01073742) and in part by Institute of Information & communications Technology Planning Evaluation (IITP) grant (No.2017-0-00195).
Keywords
- Matching theory
- Recruiting strategy
- Vehicular crowdsensing
ASJC Scopus subject areas
- Computer Networks and Communications
- Automotive Engineering
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Transportation