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Achieving Passive Localization with Traffic Light Schedules in Urban Road Sensor Networks

Sensors (Basel, Switzerland), 2016-10, Vol.16 (10), p.1662-1662 [Peer Reviewed Journal]

Copyright MDPI AG 2016 ;2016 by the authors; licensee MDPI, Basel, Switzerland. 2016 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s16101662 ;PMID: 27735871

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  • Title:
    Achieving Passive Localization with Traffic Light Schedules in Urban Road Sensor Networks
  • Author: Niu, Qiang ; Yang, Xu ; Gao, Shouwan ; Chen, Pengpeng ; Chan, Shibing
  • Subjects: Algorithms ; Collection ; Localization ; Position (location) ; Roads ; Sensors ; time stamps ; traffic lights ; Traffic signals ; Vehicles ; wireless sensor network
  • Is Part Of: Sensors (Basel, Switzerland), 2016-10, Vol.16 (10), p.1662-1662
  • Description: Localization is crucial for the monitoring applications of cities, such as road monitoring, environment surveillance, vehicle tracking, etc. In urban road sensor networks, sensors are often sparely deployed due to the hardware cost. Under this sparse deployment, sensors cannot communicate with each other via ranging hardware or one-hop connectivity, rendering the existing localization solutions ineffective. To address this issue, this paper proposes a novel Traffic Lights Schedule-based localization algorithm (TLS), which is built on the fact that vehicles move through the intersection with a known traffic light schedule. We can first obtain the law by binary vehicle detection time stamps and describe the law as a matrix, called a detection matrix. At the same time, we can also use the known traffic light information to construct the matrices, which can be formed as a collection called a known matrix collection. The detection matrix is then matched in the known matrix collection for identifying where sensors are located on urban roads. We evaluate our algorithm by extensive simulation. The results show that the localization accuracy of intersection sensors can reach more than 90%. In addition, we compare it with a state-of-the-art algorithm and prove that it has a wider operational region.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s16101662
    PMID: 27735871
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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