Improved Recruitment Algorithms for Vehicular Crowdsensing Networks

Vehicular crowdsensing aims to utilize the plethora of onboard sensors and resources on smart vehicles to gather sensing data in a large coverage area. Recruitment algorithms aim to select participants within a crowdsensing network such that the most sensing data is obtained for the lowest possible cost. In this paper, the authors consider two such existing recruitment problems for vehicular crowdsensing and propose several heuristics. The authors also show that existing algorithms to solve these problems can be arbitrarily bad in the worst case. The authors also compare their algorithms with both optimal solutions (returned by mixed integer programs) as well as existing heuristics. Performance evaluations on the authors' algorithms show that their algorithms outperform existing algorithms and obtain near optimal solutions.

• Record URL:
  • https://doi.org/10.1109/TVT.2018.2885403
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2019, IEEE.
• Authors:
  • Campioni, Fabio
  • Choudhury, Salimur
  • Salomaa, Kai
  • Akl, Selim G
• Publication Date: 2019-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1198-1207
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 68
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Algorithms; Monitoring; Recruiting; Sensors; Trajectory; Wireless communication systems
• Uncontrolled Terms: Crowdsensing
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01699411
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 22 2019 4:15PM