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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Computer Communications
Article . 2020
License: Elsevier TDM
Data sources: Crossref
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Recruitment algorithms for vehicular sensor networks

Authors: Campioni, F; Choudhury, S; Tariq, U; Bashir, AK;

Recruitment algorithms for vehicular sensor networks

Abstract

© 2020 Elsevier B.V. Vehicular crowdsensing allows the rapid, predictable movement of vehicles, as well as their wide variety of sensors, to gather sensing data in crowdsensing applications. Recruitment algorithms are used to select a subset of participants in an area that will provide the most complete coverage. In this paper, we explore two variations of the vehicular recruitment problem. In the first problem, which we refer to as the priority based vehicle recruitment problem, we consider coverage areas in which subsets must be covered. In the multisensor variation, we consider coverage areas which require different types of sensors, in which participating vehicles have one or more sensor types onboard. For each, we implement a mixed integer programming model which returns optimal solutions, as well as a heuristic for obtaining approximate solutions. In the unbudgeted priority vehicular recruitment performance evaluation, our heuristic on average obtains only 0.05% lower utility at 1.78% higher recruitment cost. In the budgeted runs, our heuristic obtains on average only 0.02% lower utility at 0.59% higher recruitment costs. In the unbudgeted multisensor vehicular recruitment performance evaluation, our heuristic obtains only 0.04% lower utility at 1.10% higher recruitment cost, and in the budgeted runs we obtain 11.33% lower utility at 0.27% higher recruitment cost.

Country
United Kingdom
Subjects by Vocabulary

Microsoft Academic Graph classification: Heuristic (computer science) Computer science Variation (game tree) Crowdsensing Integer programming Algorithm Wireless sensor network

Keywords

Computer Networks and Communications

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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