Advertisement

Wireless Networks

, Volume 25, Issue 4, pp 1711–1729 | Cite as

Infrastructure-aided hybrid routing in CR-VANETs using a Bayesian Model

  • Huma Ghafoor
  • Insoo KooEmail author
Article

Abstract

With long delays due to sporadic routing links in cognitive vehicular communications systems, relay node selection is one of the key design factors, as it significantly improves end-to-end delay, thereby improving overall network performance. To this end, we propose infrastructure-aided hybrid routing that uses a roadside unit (RSU) to help vehicular nodes to select idle channels and relay nodes. Channel selection is done with a belief propagation algorithm, which aggregates individual beliefs with the help of vehicles and RSUs to make a final belief, providing high accuracy in hypotheses about spectrum availability. The selection of a relay node is determined by calculating the message delivery time—the source/relay node selects the one that has the minimum message delivery time from among all the neighboring nodes. This is a hybrid (vehicle-to-vehicle and vehicle-to-RSU) communications scheme where two nodes can communicate only when they have consensus about a common idle channel. The idea is to combine cognitive capabilities with a routing technique in order to simultaneously overcome spectrum scarcity and network connectivity issues. Therefore, both dense and sparse network conditions are considered in this routing protocol for both highway and city scenarios. To enhance the stability of cognitive routing links, different functions for vehicles and RSUs are considered. We prove better performance in delay, delivery ratio, and overhead by comparing the proposed technique with two existing techniques (one dealing with, and another without, RSUs).

Keywords

Cognitive routing Roadside unit Spectrum sensing Vehicle-to-infrastructure Vehicle-to-vehicle 

Notes

Acknowledgements

This work was supported by the National Research Foundation (NRF) of Korea funded by the MEST (NRF-2016K2A9A1A01950711).

References

  1. 1.
    Al-Sultan, S., Al-Doori, M. M., Al-Bayatti, A. H., & Zedan, H. (2014). A comprehensive survey on vehicular Ad Hoc network. Journal of Network and Computer Applications, 37, 380–392.CrossRefGoogle Scholar
  2. 2.
    Lee, K. C., Lee, U., & Gerla, M. (2009). Survey of routing protocols in vehicular ad hoc networks. In Advances in vehicular ad-hoc networks: Developments and challenges (Chap. 8, pp. 149–170). IGI Global.  https://doi.org/10.4018/978-1-61520-913-2.ch008.
  3. 3.
    Lin, Y.-W., Chen, Y.-S., & Lee, S.-L. (2010). Routing protocols in vehicular ad hoc networks: A survey and future perspectives. The Journal of Information Science and Engineering, 26(3), 913–932.Google Scholar
  4. 4.
    Di Felice, M., Doost-Mohammady, R., Chowdhury, K. R., & Bononi, L. (2012). Smart radios for smart vehicles: Cognitive vehicular networks. IEEE Vehicular Technology Magazine, 7(2), 26–33.CrossRefGoogle Scholar
  5. 5.
    Jiang, D., Delgrossi, L. (2008). IEEE 802.11p: Towards an international standard for wireless access in vehicular environments. In Proceedings IEEE vehicular technology Conference (VTC ’08-Spring) (pp. 2036–2040).Google Scholar
  6. 6.
    Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.CrossRefGoogle Scholar
  7. 7.
    Singh, K. D., Rawat, P., & Bonnin, J.-M. (2014). Cognitive radio for vehicular ad hoc networks (CR-VANETs): Approaches and challenges. EURASIP Journal on Wireless Communications and Networking, 2014(49), 1–22.Google Scholar
  8. 8.
    Bukhari, S. H. R., Rehmani, M. H., & Siraj, S. (2016). A survey of channel bonding for wireless networks and guidelines of channel bonding for futuristic cognitive radio sensor networks. IEEE Communications Surveys & Tutorials, 18(2), 924.CrossRefGoogle Scholar
  9. 9.
    Zhang, N., & Mark, J. W. (2014). Cooperative cognitive radio networking. In Security-aware cooperation in cognitive radio networks (Chap. 2, pp. 15–22). SpringerBriefs in Computer Science.  https://doi.org/10.1007/978-1-4939-0413-6_2.
  10. 10.
    Akyildiz, I. F., Lo, B. F., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Elsevier Personal Communication, 4, 40–62.Google Scholar
  11. 11.
    Huang, X.-L., Wu, J., Li, W., Zhang, Z., Zhu, F., & Wu, M. (2016). Historical spectrum sensing data mining for cognitive radio enabled vehicular ad-hoc networks. IEEE Transactions on Dependable and Secure Computing, 13(1), 59–70.CrossRefGoogle Scholar
  12. 12.
    Youssef, M., Ibrahim, M., Abdelatif, M., Chen, L., & Vasilakos, A. V. (2014). Routing metrics of cognitive radio networks: A survey. IEEE Communications Surveys & Tutorials, 16(1), 92–109.CrossRefGoogle Scholar
  13. 13.
    Zhang, Z., Long, K., & Wang, J. (2013). Self-organization paradigms and optimization approaches for cognitive radio technologies: A survey. IEEE Wireless Communication, 20(2), 36–42.CrossRefGoogle Scholar
  14. 14.
    Kim, W., Oh, S. Y., Gerla, M., & Lee, K. C. (2011). CoRoute: A new cognitive anypath vehicular routing protocol. Wiley Journal of Wireless Communications and Mobile Computing, 11(12), 1588–1602.CrossRefGoogle Scholar
  15. 15.
    Liu, J., Ren, P., Xue, S., & Chen, H. ( 2012). Expected path duration maximized routing algorithm in CR-VANETs. InProceeding 1st IEEE international conference communication China (pp. 659–663).Google Scholar
  16. 16.
    Kim, J., & Krunz, M. (2013). Spectrum-aware beaconless geographical routing protocol for cognitive radio enabled vehicular networks. Mobile Networks and Applications, 18(6), 854–866.CrossRefGoogle Scholar
  17. 17.
    Ghafoor, H., & Koo, I. (2016). Spectrum-aware geographic routing in cognitive vehicular ad hoc network using a Kalman filter. Journal of Sensors, vol. 2016, Article ID 8572601.Google Scholar
  18. 18.
    Yedidia, J. S., Freeman, W. T., & Weiss, Y. (2003). Understanding belief propagation and its generalizations. In G. Lakemeyer & B. Nebel (Eds.), Exploring artificial intelligence in the new millennium (Vol. 8, pp. 2282–2312). San Francisco, CA: Morgan Kaufmann.Google Scholar
  19. 19.
    Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems (2nd ed.). San Francisco, CA: Morgan Kaufmann.zbMATHGoogle Scholar
  20. 20.
    Karp, B., & Kung, H. T. (2000). GPSR: Greedy perimeter stateless routing for wireless networks. In Proceedings MobiCom 2000 (pp. 243–254), Boston, MA.Google Scholar
  21. 21.
    Ding, Y., Wang, C., & Xiao, L. (2007). A static-node assisted adaptive routing protocol in vehicular networks. In Proceedings 4th ACM international workshop VANET (pp. 59–68), Montreal, QC.Google Scholar
  22. 22.
    Cheng, P.-C., Lee, K. C., Gerla, M., & Harri, J. (2010). GeoDTN+Nav: Geographic DTN routing with navigator prediction for urban vehicular environments. Mobile Networks and Applications, 15(1), 61–82.CrossRefGoogle Scholar
  23. 23.
    Ghafoor, H., Koo, I., & Gohar, N. D. (2014). Neighboring and connectivity-aware routing in VANETs. The Scientific World Journal vol. 2014, Article ID 789247Google Scholar
  24. 24.
    Mershad, K., Artail, H., & Gerla, M. (2012). ROAMER: Roadside units as message routers in VANETs. Ad Hoc Networks, 10(3), 479–496.CrossRefGoogle Scholar
  25. 25.
    Ngo, C. T., & Oh, H. (2015). A roadside unit based hybrid routing protocol for vehicular ad hoc networks. IEEE Transactions on Communications, 98(12), 2400.CrossRefGoogle Scholar
  26. 26.
    Amjad, Z., Song, W. -C., Ahn, K. -J. (2016). Two-level hierarchical routing based on road connectivity in VANETs. International Conference on industrial engineering, management science and applications (ICIMSA) Google Scholar
  27. 27.
    Abbassi, S. H., Qureshi, I. M., Abbasi, H., & Alyaie, B. R. (2015). History-based spectrum sensing in CR-VANETs. EURASIP Journal on Wireless Communications and Networking, 2015, 163.CrossRefGoogle Scholar
  28. 28.
    Baraka, K., Safatly, L., Artail, H., Ghandour, A., & Hajj, A . E. (2015). An infrastructure-aided cooperative spectrum sensing scheme for vehicular ad hoc networks. Ad Hoc Networks, 25, 197–212.CrossRefGoogle Scholar
  29. 29.
    Li, P., Huang, C., & Liu, Q. (2015). Delay bounded roadside unit placement in vehicular ad hoc networks. International Journal of Distributed Sensor Networks, vol. 2015, Article ID 937673.Google Scholar
  30. 30.
    Sun, Y., & Chowdhury, K. R. (2014). Enabling emergency communication through a cognitive radio vehicular network. IEEE Communications Magazine, 52(10), 68–75.CrossRefGoogle Scholar
  31. 31.
    Lee, W.-Y., & Akyildiz, I. F. (2008). Optimal spectrum sensing framework for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(10), 3845–3857.CrossRefGoogle Scholar
  32. 32.
    Ghafoor, H., Noh, Y., & Koo, I. (2016). Belief propagation-based cognitive routing in maritime ad hoc networks. International Journal of Distributed Sensor Networks, vol. 2016, Article ID 7635206.Google Scholar
  33. 33.
    Kasemann, M., Fubler, H., Hartenstein, H., & Mauve, M. (2002). A reactive location service for mobile ad hoc networks. Department of Computer Science, University of Mannheim, Technical Report TR-02-014.Google Scholar
  34. 34.
    Bukhari, S. H. R., Siraj, S., & Rehmani, M. H. (2016). NS-2 based simulation framework for cognitive radio sensor networks. Wireless Networks.  https://doi.org/10.1007/s11276-016-1418-5.
  35. 35.
    Rasheed, H., & Rajatheva, N. (2011). Spectrum sensing for cognitive vehicular networks over composite fading. International Journal of Automotive Technology, vol. 2011, Article ID 630467, 9 pages.Google Scholar
  36. 36.
    Abbas, T., Sjoberg, K., Karedal, J., & Tufvesson, F. (2015). A measurement based shadow fading model for vehicle-to-vehicle network simulations. Hindawi International Journal of Antennas and Propagation, vol. 2015, Article ID 190607, 12 pages.Google Scholar
  37. 37.
    Zang, Y., Stibor, L., Orfanos, G., Guo, S., Reumerman, H. -J. (2005). An error model for inter-vehicle communications in highway scenarios at 5.9 GHz. In Proceedings of the 2nd ACM international workshop on performance evaluation of wireless ad hoc, sensor, and ubiquitous networks (PE-WASUN ’05) (pp. 49–56) ACM, Montreal.Google Scholar
  38. 38.
    Felice, M. D., Chowdhury, K. R., Bononi, L. (2010). Analyzing the potential of cooperative cognitive radio technology on inter-vehicle communication. In Proceedings of IFIP Wireless Days (pp. 1–6), Venice, Italy.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Electrical EngineeringUniversity of Ulsan (UOU)UlsanRepublic of Korea

Personalised recommendations