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Gradient-driven parking navigation using a continuous information potential field based on wireless sensor network
School of Computer Science and Engineering, Xi’an University of Technology.
Department of Electrical and Computer Engineering, West Virginia University, Montgomery, WV .
Centre for Distributed and High Performance Computing, School of Information Technologies, The University of Sydney.
National school of Software, Xidian University.
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2017 (English)In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 408, 100-114 p.Article in journal (Refereed) Published
Abstract [en]

Wireless sensor networks can support building and transportation system automation in numerous ways. An emerging application is to guide drivers to promptly locate vacant parking spaces in large parking structures during peak hours. This paper proposes efficient parking navigation via a continuous information potential field and gradient ascent method. Our theoretical analysis proves the convergence of a proposed algorithm and efficient convergence during the first and second steps of the algorithm to effectively prevent parking navigation from a gridlock situation. The empirical study demonstrates that the proposed algorithm performs more efficiently than existing algorithms.

Place, publisher, year, edition, pages
2017. Vol. 408, 100-114 p.
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-63183DOI: 10.1016/j.ins.2017.04.042ISI: 000403126200006Scopus ID: 2-s2.0-85019003216OAI: oai:DiVA.org:ltu-63183DiVA: diva2:1091711
Note

Validerad; 2017; Nivå 2; 2017-05-10 (rokbeg)

Available from: 2017-04-27 Created: 2017-04-27 Last updated: 2017-06-30Bibliographically approved

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