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Cluster Computing

, Volume 22, Supplement 6, pp 13209–13217 | Cite as

Internet of Things network cognition and traffic management system

  • Abida SharifEmail author
  • Jian Ping Li
  • Muhammad Irfan Sharif
Article

Abstract

In a world of ever increasing technological diversity and an advancing ‘Internet of Things’ (IoTs), business landscapes are changing. New conditions alter the way in which competence resources are regarded and how they need to be managed in order for organizations to sustain as successful actors in a knowledge economy. Information technology play an important role in this new setting, including network management systems for handling information concerning competence resources. This research focus on smart IoTs traffic management system, which is advertised by minimal cost, long scalability, great compatibility, easy to promote, to replace conventional traffic management system and the proposed approach can develop public road traffic enormously. The aim of this research proposed to develop an IoT public traffic adaptive detection system and proficient of supposing the travel time associated with each street sector based on the traffic information streamlined every 18 s, which sequentially finds the path with the minimal travel time in the traffic network by using a dynamic procedure.

Keywords

Internet of Things Smart city Sensor system integration Network architecture Intelligent traffic Wireless sensor networks Agent technology 

References

  1. 1.
    Rahman, F., Kubota, H.: Point scoring system to rank traffic calming projects. J. Traffic Transp. Eng. (Engl. Ed.) 3(4), 324–335 (2016)CrossRefGoogle Scholar
  2. 2.
    Silva, V.J., Gomes, C.E.M., Santana, S.S., De Lucena, V.F.: Intelligent system for medication management in residential environments. IFAC-PapersOnLine 49(30), 171–174 (2016)CrossRefGoogle Scholar
  3. 3.
    Haghani, A., Hamedi, M.: Application of Bluetooth technology in traffic detection, surveillance, and traffic management. J. Intell. Transp. Syst. 17(2), 107–109 (2013)CrossRefGoogle Scholar
  4. 4.
    Zheng, S.K., Ma, G.H.: Police traffic management system design based on GIS. Adv. Mater. Res. 791–793, 1618–1621 (2013)CrossRefGoogle Scholar
  5. 5.
    Dayeni, M.K., Soleymani, M.: Dayeni and M. Soleymani, Intelligent energy management of a fuel cell vehicle based on traffic condition recognition. Clean Technol. Environ. Policy 18(6), 1945–1960 (2016)CrossRefGoogle Scholar
  6. 6.
    Anandakumar, H., Umamaheswari, K.: Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Clust. Comput. 20(2), 1505–1515 (2017).  https://doi.org/10.1007/s10586-017-0798-3 CrossRefGoogle Scholar
  7. 7.
    Cui, D.C., Yu, Y.: The optimization layout method of intelligent roadside sensor system in traffic management and control. Adv. Mater. Res. 591–593, 1251–1255 (2012)CrossRefGoogle Scholar
  8. 8.
    Wu, T.-Y., Guizani, N., Hsieh, C.-Y.: An efficient adaptive intelligent routing system for multi-intersections. Wirel. Commun. Mob. Comput. 16(17), 3175–3186 (2016)CrossRefGoogle Scholar
  9. 9.
    Keeler, J., Zimmerman, R.L., Gawron, V., Battiste, V., Strybel, T.Z., Vu, K.-P.L.: Examining the effectiveness of a traffic flow management course for air traffic control students. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 60(1), 99–100 (2016)CrossRefGoogle Scholar
  10. 10.
    Fang, J., Jin, J.: Intelligent algorithms for reducing short-term traffic state prediction error in active traffic management. J. Intell. Transp. Syst. 19(3), 304–315 (2014)CrossRefGoogle Scholar
  11. 11.
    Costantino, F., Di Gravio, G., Patriarca, R.: Resilience engineering to assess risks for the air traffic management system: a new systemic method. Int. J. Reliab. Saf. 10(4), 323 (2016)CrossRefGoogle Scholar
  12. 12.
    Anandakumar, H., Umamaheswari, K.: An efficient optimized handover in cognitive radio networks using cooperative spectrum sensing. Intell. Autom. Soft Comput. (2017).  https://doi.org/10.1080/10798587.2017.1364931
  13. 13.
    Sawaguchi, T., Ikeda, D., Sugawa, M., Sawaguchi, A., Kawahara, K., Sato, J., Sato, K.: Analysis of emergency survival rate after traffic accidents in Japan. Eur. J. Public Health (2016). https://academic.oup.com/eurpub/article-abstract/26/suppl_1/ckw175.063/2449521
  14. 14.
    Sandhu, S.S., Jain, N., Gaurav, A., Iyengar, N.C.S.N.: Agent based intelligent traffic management system for smart cities. Int. J. Smart Home 9(12), 307–316 (2015)CrossRefGoogle Scholar
  15. 15.
    Jyothi, R.J., Prasad, V.R., Anuradha, N.A.N.: Automatic accident detection and ambulance rescue with intelligent traffic light system. Int. J. Sci. Res. 3(7), 177–179 (2012)Google Scholar
  16. 16.
    Padmanaban, R.P.S., Divakar, K., Vanajakshi, L., Subramanian, S.C.: Development of a real-time bus arrival prediction system for Indian traffic conditions. IET Intell. Transp. Syst. 4(3), 189 (2010)CrossRefGoogle Scholar
  17. 17.
    Anandakumar, H., Umamaheswari, K.: A bio-inspired swarm intelligence technique for social aware cognitive radio handovers. Comput. Electr. Eng. (2017).  https://doi.org/10.1016/j.compeleceng.2017.09.016 CrossRefGoogle Scholar
  18. 18.
    Reztsov, A.: How micro simulation models can be used to assess intelligent transport system strategies: use of real traffic data. SSRN Electron. J. (2015). https://ssrn.com/abstract=2680487
  19. 19.
    Kergaye, C., Stevanovic, A., Martin, P.: Comparative evaluation of adaptive traffic control system assessments through field and microsimulation. J. Intell. Transp. Syst. 14(2), 109–124 (2010)CrossRefGoogle Scholar
  20. 20.
    Chattaraj, A., Bansal, S., Chandra, A.: An intelligent traffic control system using RFID. IEEE Potentials 28(3), 40–43 (2009)CrossRefGoogle Scholar
  21. 21.
    Cukurtepe, H., Akgun, I.: Towards space traffic management system. Acta Astronaut. 65(5–6), 870–878 (2009)CrossRefGoogle Scholar
  22. 22.
    Spyropoulou, I., Karlaftis, M.G.: Incorporating intelligent speed adaptation systems into microscopic traffic models. IET Intell. Transp. Syst. 2(4), 331 (2008)CrossRefGoogle Scholar
  23. 23.
    Arulmurugan, R., Sabarmathi, K.R., Anandakumar, H.: Classification of sentence level sentiment analysis using cloud machine learning techniques. Clust. Comput. (2017).  https://doi.org/10.1007/s10586-017-1200-1 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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