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

  • Ruixin Ma
  • Qirui Li
  • Tie Qiu
  • Chen Chen
  • Arun Kumar Sangaiah
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 234)


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.


Road congestion Emergency navigation Situation-aware 


  1. 1.
    Aouami, R., Ouzzif, M., Rifi, M.: A new architecture for traffic congestion using smartphone and wireless sensor networks. In: 2016 3rd International Conference on Systems of Collaboration, pp. 1–5 (2016)Google Scholar
  2. 2.
    Bhuiyan, M.Z.A., Wang, G., Cao, J., Wu, J.: Sensor placement with multiple objectives for structural health monitoring. ACM Trans. Sens. Netw. (TOSN) 10, 68 (2014)Google Scholar
  3. 3.
    Bondorf, S., Schmitt, J.B.: Boosting sensor network calculus by thoroughly bounding cross-traffic. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 235–243 (2015)Google Scholar
  4. 4.
    Buragohain, C.: Distributed navigation algorithms for sensor networks. In: 25th IEEE International Conference on Computer Communications (2006).
  5. 5.
    Cheng, S., Cai, Z., Li, J., Gao, H.: Extracting kernel dataset from big sensory data in wireless sensor networks. IEEE Trans. Knowl. Data Eng. 29, 813–827 (2017)CrossRefGoogle Scholar
  6. 6.
    Giachetti, A., Campani, M., Torre, V.: The use of optical flow for road navigation. IEEE Trans. Robot. Autom. 14, 34–48 (1998)CrossRefGoogle Scholar
  7. 7.
    He, L., Yang, Z., Pan, J., Cai, L., Xu, J., Gu, Y.: Evaluating service disciplines foron-demand mobile data collectionin sensor networks. IEEE Trans. Mobile Comput. 13, 797–810 (2014)CrossRefGoogle Scholar
  8. 8.
    He, Z., Cai, Z., Yu, J., Wang, X., Sun, Y., Li, Y.: Cost-efficient strategies for restraining rumor spreading in mobile social networks. IEEE Trans. Veh. Technol. 66, 2789–2800 (2017)CrossRefGoogle Scholar
  9. 9.
    Gao, H., Zhang, X., Lifeng, A., Yuchao, L., Deyi, L.: Relay navigation strategy study on intelligent drive on urban roads. J. China Univ. Posts Telecommun. 23, 79–90 (2016)CrossRefGoogle Scholar
  10. 10.
    Hussein, A., Marín-Plaza, P., Martín, D., de la Escalera, A., Armingol, J.M.: Autonomous off-road navigation using stereo-vision and laser-rangefinder fusion for outdoor obstacles detection. In: 2016 IEEE Intelligent Vehicles Symposium (IV), pp. 104–109 (2016)Google Scholar
  11. 11.
    Li, M., Yang, Z., Liu, Y.: Sea depth measurement with restricted floating sensors. ACM Trans. Embed. Comput. Syst. (TECS) 13, 1 (2013)CrossRefGoogle Scholar
  12. 12.
    Lin, S., Zhou, G., Al-Hami, M., Whitehouse, K., Wu, Y., Stankovic, J.A., He, T., Wu, X., Liu, H.: Toward stable network performance in wireless sensor networks: a multilevel perspective. ACM Trans. Sens. Netw. (TOSN) 11, 42 (2015)Google Scholar
  13. 13.
    Liu, Y., Lu, Y., Shi, Q., Ding, J.: Optical flow based urban road vehicle tracking. In: 2013 9th International Conference on Computational Intelligence and Security (CIS), pp. 391–395 (2013)Google Scholar
  14. 14.
    Liu, Y., Mao, X., He, Y., Liu, K., Gong, W., Wang, J.: Citysee: not only a wireless sensor network. IEEE Network 27, 42–47 (2013)CrossRefGoogle Scholar
  15. 15.
    Qiu, T., Zhang, Y., Qiao, D., Zhang, X., Wymore, M.L., Sangaiah, A.K.: A robust time synchronization scheme for industrial internet of things. IEEE Trans. Industr. Inf. (2017).
  16. 16.
    Qiu, T., Zhao, A., Xia, F., Si, W., Wu, D.O.: ROSE: robustness strategy for scale-free wireless sensor networks. IEEE/ACM Trans. Netw. 25(5), 2944–2959 (2017)CrossRefGoogle Scholar
  17. 17.
    Qiu, T., Zheng, K., Han, M., Chen, C.L.P., Xu, M.: A data-emergency-aware scheduling scheme for internet of things in smart cities. IEEE Trans. Industr. Inf. (2017).
  18. 18.
    Wang, C., Lin, H., Jiang, H.: Cans: towards congestion-adaptive and small stretch emergency navigation with wireless sensor networks. IEEE Trans. Mobile Comput. 15, 1077–1089 (2016)CrossRefGoogle Scholar
  19. 19.
    Wang, C., Lin, H., Zhang, R., Jiang, H.: Send: a situation-aware emergency navigation algorithm with sensor networks. IEEE Trans. Mobile Comput. 16, 1149–1162 (2017)CrossRefGoogle Scholar
  20. 20.
    Wang, L., He, Y., Liu, W., Jing, N.: On oscillation-free emergency navigation via wireless sensor networks. IEEE Trans. Mobile Comput. 14, 2086–2100 (2015)CrossRefGoogle Scholar
  21. 21.
    Wang, Y., Dong, Q.: Using optical flow with principal divection screen strategy for road navigation. In: 2016 9th International Symposium on Computational Intelligence and Design, vol. 2, pp. 52–55 (2016)Google Scholar
  22. 22.
    Yang, Z., Jian, L., Wu, C., Liu, Y.: Beyond triangle inequality: sifting noisy and outlier distance measurements for localization. ACM Trans. Sens. Netw. (TOSN) 9, 26 (2013)Google Scholar
  23. 23.
    Zeng, K., Shu, Y., Liu, S.: A practical GPS location spoofing attack in road navigation scenario. In: Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications, pp. 85–90 (2017)Google Scholar
  24. 24.
    Li, Z., Qian, C., Zun, G., Choi, Y.: A low latency, energy efficient MAC protocol for wireless sensor networks. Int. J. Distrib. Sens. Netw. (2015).
  25. 25.
    Zheng, X., Cai, Z., Li, J., Gao, H.: A study on application-aware scheduling in wireless networks. IEEE Trans. Mobile Comput. 16, 1787–1801 (2017)CrossRefGoogle Scholar
  26. 26.
    Xiao, F., Wang, Z., Ye, N., Wang, R., Li, X.: One more tag enables fingrained RFID localization and tracking. IEEE/ACM Trans. Netw. 26, 161–174 (2017). Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Ruixin Ma
    • 1
  • Qirui Li
    • 1
  • Tie Qiu
    • 4
  • Chen Chen
    • 2
  • Arun Kumar Sangaiah
    • 3
  1. 1.School of SoftwareDalian University of TechnologyDalianChina
  2. 2.State Key Laboratory of Integrated Services NetworksXidian UniversityXi’anChina
  3. 3.School of Computing Science and EngineeringVIT UniversityVelloreIndia
  4. 4.School of Computer Science and TechnologyTianjin UniversityTianjinChina

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