Quality, Reliability, Security and Robustness in Heterogeneous Systems. 13th International Conference, QShine 2017, Dalian, China, December 16 -17, 2017, Proceedings

Research Article

A Situation-Aware Road Emergency Navigation Mechanism Based on GPS and WSNs

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  • @INPROCEEDINGS{10.1007/978-3-319-78078-8_4,
        author={Ruixin Ma and Qirui Li and Tie Qiu and Chen Chen and Arun Sangaiah},
        title={A Situation-Aware Road Emergency Navigation Mechanism Based on GPS and WSNs},
        proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 13th International Conference, QShine 2017, Dalian, China, December 16 -17, 2017, Proceedings},
        proceedings_a={QSHINE},
        year={2018},
        month={4},
        keywords={Road congestion Emergency navigation Situation-aware},
        doi={10.1007/978-3-319-78078-8_4}
    }
    
  • Ruixin Ma
    Qirui Li
    Tie Qiu
    Chen Chen
    Arun Sangaiah
    Year: 2018
    A Situation-Aware Road Emergency Navigation Mechanism Based on GPS and WSNs
    QSHINE
    Springer
    DOI: 10.1007/978-3-319-78078-8_4
Ruixin Ma1,*, Qirui Li1,*, Tie Qiu2,*, Chen Chen3,*, Arun Sangaiah4,*
  • 1: Dalian University of Technology
  • 2: Tianjin University
  • 3: Xidian University
  • 4: VIT University
*Contact email: dlutwindows@163.com, lqrrey@163.com, qiutie@ieee.org, cc2000@mail.xidian.edu.cn, arunkumarsangaiah@gmail.com

Abstract

Traffic congestion happens when emergencies occur. Traditional congestion algorithms evaluate traffic congestion only according to real-time vehicle speed, instead of comprehensive aspects. To address this shortcoming, we provide a new algorithm for congestion evaluation based on WSNs and GPS, which provide many sensor nodes to monitor and transmit traffic message in time. This paper takes more aspects for traffic into consideration, including congestion situation, danger condition and sudden road peak flow, and turns them into weights, which help to measure congestion intensity. According to congestion intensity, congestion field is established to navigate for the vehicles. Furthermore, we propose future prediction mechanism for vehicles. Finally, we do simulation with Matlab to evaluate the performance of the prediction mechanism, and results show that the performance of prediction mechanism is better than greedy algorithm. Moreover, a route will be recommended after a comprehensive evaluation about the distance, time, congestion and traffic lights number. In a word, the prediction mechanism for traffic can not only ensure the effectiveness of the navigation, but also protect drivers from the sudden peak flow, which brings convenience and comfortableness to drivers.