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Increasing Route Availability in Internet of Vehicles Using Ant Colony Optimization

  • Nitika ChowdharyEmail author
  • Pankaj Deep Kaur
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 712)

Abstract

Smart City, where a large number of self-configurable and intelligent devices communicate with each other, provides a platform for collaborative decision making processes affecting virtually every other device present in the ecosystem. Internet of Vehicles (IoV) forms a major part of thus ecosystem that comprises of mobile vehicles capable of generating, storing and moreover processing the data flowing through the system. The vehicles continuously communicate with each other and with the external environment to collect and process real-time information. This collaboration provides a means to build up optimized routing decisions that may lead to the improvement in overall congestion suffered by the network. In this paper, we apply two optimization algorithms Any Colony and Firefly Optimization, on the real-time data collected from various vehicular sources to provide them optimized and congestion-free routes. Various road parameters have been considered that may affect the selection of a particular route toward the destination. The results of experimental setup, conducted using two open source simulators NS2 and SUMO, have shown a predominant enhancement by reducing the average travelling time of the vehicles in the complete system taken into consideration.

Keywords

Route optimization Internet of vehicles Ant colony optimization 

References

  1. 1.
    Medagliani, P., Leguay, J., Duda, A., Rousseau, F., Duquennoy, S., Raza, S., Ferrari, G., Gonizzi, P., Cirani, S., Veltri, L., Monton, M.: Internet of Things Applications-From Research and Innovation to Market Deployment, pp. 287–313. The River Publishers, Amsterdam (2014)Google Scholar
  2. 2.
    Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)CrossRefGoogle Scholar
  3. 3.
    Li, J.: Vehicle routing problem with time windows for reducing fuel consumption. J. Comput. 7(12), 3020–3027 (2012)Google Scholar
  4. 4.
    Choi, W.-K., Kim, S.-J., Kang, T.-G., Jeon, H.-T.: Study on method of route choice problem based on user preference. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007. LNCS, vol. 4694, pp. 645–652. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-74829-8_79 CrossRefGoogle Scholar
  5. 5.
    Pazooky, S., Rahmatollahi Namin, S., Soleymani, A., Samadzadegan, F.: An evaluation of potentials of genetic algorithm in shortest path problem. In: EGU General Assembly Conference Abstracts, vol. 11, pp. 80–98 (2009)Google Scholar
  6. 6.
    Kanoh, H.: Dynamic route planning for car navigation systems using virus genetic algorithms. Int. J. Knowl. Based Intell. Eng. Syst. 11(1), 65–78 (2007)CrossRefGoogle Scholar
  7. 7.
    Dethloff, J.: Relation between vehicle routing problems: an insertion heuristic for the vehicle routing problem with simultaneous delivery and pick-up applied to the vehicle routing problem with backhauls. J. Oper. Res. Soc. 53(1), 115–118 (2002)CrossRefzbMATHGoogle Scholar
  8. 8.
    Ai, T.J., Kachitvichyanukul, V.: A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Comput. Oper. Res. 36(5), 1693–1702 (2009)CrossRefzbMATHGoogle Scholar
  9. 9.
    Gajpal, Y., Abad, P.: An ant colony system (ACS) for vehicle routing problem with simultaneous delivery and pickup. Comput. Oper. Res. 36(12), 3215–3223 (2009)CrossRefzbMATHGoogle Scholar
  10. 10.
    Montané, F., Galvão, R.D.: A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service. Comput. Oper. Res. 33(3), 595–619 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Sahoo, A., Swain, S.K., Pattanayak, B.K., Mohanty, M.N.: An optimized cluster based routing technique in VANET for next generation network. In: Satapathy, S., Mandal, J., Udgata, S., Bhateja, V. (eds.) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol. 433, pp. 667–675. Springer, New Delhi (2016). doi: 10.1007/978-81-322-2755-7_69 CrossRefGoogle Scholar
  12. 12.
    AbdAllah, A.M.F., Essam, D.L., Sarker, R.A.: On solving periodic re-optimization dynamic vehicle routing problems. Appl. Soft Comput. 55, 1–12 (2017)CrossRefGoogle Scholar
  13. 13.
    Oranj, A.M., Alguliev, R.M., Yusifov, F., Jamali, S.: Routing algorithm for vehicular ad hoc network based on dynamic ant colony optimization. Int. J. Electron. Electr. Eng. 4(1), 79–83 (2016)Google Scholar
  14. 14.
    Pan, J.S., Popa, I.S., Borcea, C.: Divert: a distributed vehicular traffic re-routing system for congestion avoidance. IEEE Trans. Mob. Comput. 16(1), 58–72 (2017)CrossRefGoogle Scholar
  15. 15.
    Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)CrossRefGoogle Scholar
  16. 16.
    Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-04944-6_14 CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringLyallpur Khalsa College of EngineeringJalandharIndia
  2. 2.Department of Computer Science and EngineeringGNDU RC JalandharJalandharIndia

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