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An Adaptive WiFi/WiMAX Networking Platform for Cognitive Vehicular Networks

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Cognitive Radio Mobile Ad Hoc Networks

Abstract

This chapter presents an adaptive networking platform using WiFi/WiMAX technologies for cognitive vehicle-to-roadside communications, which can be used to transfer safety messages and provide Internet access for mobile users inside vehicles. The proposed platform is based on a heterogeneous multihop cluster-based vehicular network, where a vehicular node can choose to play the role of a gateway or a client. The gateway nodes communicate directly with a roadside base station through a WiMAX link. The client nodes connect to the gateways through WiFi links. Traffic from client nodes are relayed by the gateways to a roadside base station. The vehicular nodes are the self-interest (i.e., rational) and have capability to learn and adapt decision to achieve their objectives independently. A decision-making framework is proposed for this WiFi/WiMAX platform. This distributed decision-making framework, which enables the vehicular nodes with cognitive capability, is modeled and analyzed using game theory. Also, a Q-learning algorithm is used in vehicular nodes to provide the cognitive capability to learn and adapt their decision. Dynamics of Q-learning algorithm can be modeled as an evolutionary game.

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References

  1. Susan R. Dickey, C.-L. Huang, and X. Guan, “Field Measurements of Vehicle to Roadside Communication performance,” in Proceedings of IEEE Vehicular Technology Conference (VTC) Fall, pp. 2179–2183, September-October 2007.

    Google Scholar 

  2. H. Cai and Y. Lin, “Design of a Roadside Seamless Wireless Communication System for Intelligent Highway,” in Proceeding of IEEE Networking, Sensing and Control, pp. 342–347, March 2005.

    Google Scholar 

  3. K. Yang, S. Ou, H.-H. Chen, and J. He, “A Multihop Peer-Communication Protocol with Fairness Guarantee for IEEE 802.16-Based Vehicular Networks,” IEEE Transactions on Vehicular Technology, vol. 56, no. 6, Part 1, pp. 3358–3370, November 2007.

    Article  Google Scholar 

  4. L. Wischhof, A. Ebner, H. Rohling, M. Lott, and R. Halfmann, “Adaptive Broadcast for Travel and Traffic Information Distribution Based on Inter-Vehicle Communication,” in Proceedings of IEEE Intelligent Vehicles Symposium, pp. 6–11, June 2003.

    Google Scholar 

  5. O. Maeshima, S. Cai, T. Honda, and H. Urayama, “A Roadside-to-Vehicle Communication System for Vehicle Safety using Dual Frequency Channels,” in Proceedings of IEEE Intelligent Transportation Systems Conference (ITSC) 2007, pp. 349–354, September–October 2007.

    Google Scholar 

  6. H. Su and X. Zhang, “Clustering-Based Multichannel MAC Protocols for QoS Provisionings Over Vehicular Ad Hoc Networks,” IEEE Transaction Vehicular Technology, vol. 56, no. 6, pp. 3309–3323, November 2007.

    Article  Google Scholar 

  7. B. Sikdar, “Design and Analysis of a MAC Protocol for Vehicle to Roadside Networks,” in Proceedings of IEEE Wireless Communications and Networking Conference (WCNC), pp. 1691–1696, March–April 2008.

    Google Scholar 

  8. C.-J. Chang, R.-G. Cheng, H.-T. Shih, and Y.-S. Chen, “Maximum Freedom Last Scheduling Algorithm for Downlinks of DSRC Networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 2, pp. 223–232, June 2007.

    Article  Google Scholar 

  9. X. Y. Wang and P.-H. Ho, “A Novel Sensing Coordination Framework for CR-VANETs,” IEEE Transactions on Vehicular Technology, vol. 59, no. 4, pp. 1936–1948, May 2010.

    Article  Google Scholar 

  10. H. Li and D. K. Irick, “Collaborative Spectrum Sensing in Cognitive Radio Vehicular Ad hoc Networks: Belief Propagation on Highway,” in Proceedings of IEEE Vehicular Technology Conference (VTC)-Spring, pp. 1–5, May 2010.

    Google Scholar 

  11. Z. Ahmed, H. Jamal, S. Khan, R. Mehboob, and A. Ashraf, “Cognitive Communication Device for Vehicular Networking,” IEEE Transactions on Consumer Electronics, vol. 55, no. 2, pp. 371–375, May 2009.

    Article  Google Scholar 

  12. S. Chung, J. Yoo, and C. Kim, “A Cognitive MAC for VANET Based on the WAVE Systems,” in Proceedings of International Conference on Advanced Communication Technology (ICACT), vol. 1, pp. 41–46, February 2009.

    Google Scholar 

  13. K. Tsukamoto, Y. Omori, O. Altintas, M. Tsuru, and Y. Oie, “On Spatially-Aware Channel Selection in Dynamic Spectrum Access Multi-hop Inter-Vehicle Communications,” in Proceedings of IEEE Vehicular Technology Conference (VTC)-Fall, pp. 1–7, September 2009.

    Google Scholar 

  14. D. Niyato, E. Hossain, and P. Wang, “Optimal Channel Access Management with QoS Support for Cognitive Vehicular Networks,” IEEE Transactions on Mobile Computing, vol. 10, no. 4, pp. 573–591, February 2011.

    Article  Google Scholar 

  15. R. Rangnekar, F. Ge, A. Young, M. D. Silvius, A. Fayez, and C. W. Bostian, “A Remote Control and Service Access Scheme for a Vehicular Public Safety Cognitive Radio,” in Proceedings of IEEE Vehicular Technology Conference Fall (VTC)-Fall, pp. 1–5, September 2009.

    Google Scholar 

  16. R. Muraleedharan and L. A. Osadciw, “Cognitive Security Protocol for Sensor Based VANET Using Swarm Intelligence,” in Proceedings of Asilomar Conference on Signals, Systems and Computers, pp. 288–290, November 2009.

    Google Scholar 

  17. K. Tuyls, A. Nowe, T. Lenaerts, and B. Manderick, “An Evolutionary Game Theoretic Perspective on Learning in Multi-agent Systems,” in Synthese Knowledge, Rationality & Action, vol. 139, no. 2, pp. 297–330, March 2004.

    MathSciNet  MATH  Google Scholar 

  18. D. Niyato and E. Hossain, “Dynamics of Network Selection in Heterogeneous Wireless Networks: An Evolutionary Game Approach,” IEEE Transactions on Vehicular Technology, vol. 58, no. 4, pp. 2008–2017, May 2009.

    Article  Google Scholar 

  19. E. Altman, R. ElAzouzi, Y. Hayel, and H. Tembine, “An Evolutionary Game Approach for the Design of Congestion Control Protocols in Wireless Networks,” in Proceedings of International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks and Workshops (WiOPT), pp. 547–552, April 2008.

    Google Scholar 

  20. M. P. Anastasopoulos, P.-D. M. Arapoglou, R. Kannan, and P. G. Cottis, “Adaptive Routing Strategies in IEEE 802.16 Multi-hop Wireless Backhaul Networks Based on Evolutionary Game Theory,” IEEE Journal on Selected Areas in Communications, vol. 26, no. 7, pp. 1218–1225, September 2008.

    Article  Google Scholar 

  21. J. Hu, B. Wang, J. Wei, and S. Huang, “Evolutionary Game for Distributed Power Allocation over Cooperative Relay Networks,” in Proceedings of International Conference on Wireless Communications, Networking and Mobile Computing (WiCom), September 2009.

    Google Scholar 

  22. B. Wang, K. J. Ray Liu, and T. C. Clancy, “Evolutionary Cooperative Spectrum Sensing Game: How to Collaborate?,” IEEE Transactions on Communications, vol. 58, no. 3, pp. 890–900, March 2010.

    Article  Google Scholar 

  23. C. Sun, E. Stevens-Navarro, and V. W. S. Wong, “A Constrained MDP-based Vertical Handoff Decision Algorithm for 4G Wireless Networks,” in Proceedings of IEEE International Conference on Communications (ICC), pp. 2169–2174, May 2008.

    Google Scholar 

  24. D. Niyato, E. Hossain, and S. Camorlinga, “Remote Patient Monitoring Service Using Heterogeneous Wireless Access Networks: Architecture and Optimization,” IEEE Journal on Selected Areas in Communications, vol. 27, no. 4, pp. 412–423, May 2009.

    Article  Google Scholar 

  25. A. Galindo-Serrano and L. Giupponi, “Distributed Q-learning for Aggregated Interference Control in Cognitive Radio Networks,” IEEE Transactions on Vehicular Technology, vol. 59, no. 4, pp. 1823–1834, May 2010.

    Article  Google Scholar 

  26. J. Gummeson, D. Ganesan, M. D. Corner, and P. Shenoy, “An Adaptive Link Layer for Range Diversity in Multi-radio Mobile Sensor Networks,” in Proceedings of IEEE INFOCOM, pp. 154–162, April 2009.

    Google Scholar 

  27. B. Xia, M. H. Wahab, Y. Yang, Z. Fan, and M. Sooriyabandara, “Reinforcement Learning Based Spectrum-aware Routing in Multi-hop Cognitive Radio Networks,” in Proceedings of International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), June 2009.

    Google Scholar 

  28. M. Li, Y. Xu, and J. Hu, “A Q-Learning Based Sensing Task Selection Scheme for Cognitive Radio Networks,” in Proceedings of International Conference on Wireless Communications & Signal Processing (WCSP), November 2009.

    Google Scholar 

  29. W. H. Sandholm, Population Games and Evolutionary Dynamics. The MIT Press, January 2011.

    Google Scholar 

  30. M. Lindstrom and P. Lungaro, “Resource Delegation and Rewards to Stimulate Forwarding in Multihop Cellular Networks,” in Proceedings of IEEE Vehicular Technology Conference (VTC) Spring, vol. 4, pp. 2152–2156, May–June 2005.

    Google Scholar 

  31. K. Chen, Z. Yang, C. Wagener, and K. Nahrstedt, “Market Models and Pricing Mechanisms in a Multihop Wireless Hotspot Network,” in Proceedings of International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous). pp. 73–82, July 2005.

    Google Scholar 

  32. D. Niyato and E. Hossain, “Integration of WiMAX and WiFi: Optimal Pricing for Bandwidth Sharing,” IEEE Communications, vol. 45, no. 5, pp. 140–146, May 2007.

    Article  Google Scholar 

  33. T. L. Vincent and J. S. Brown, Evolutionary Game theory, Natural selection, and Darwinian Dynamics. Cambridge University Press, 2005.

    Google Scholar 

  34. M. J. Osborne, An Introduction to Game Theory. Oxford University Press, 2004.

    Google Scholar 

  35. M. J. Lighthill and G. B. Whitham, “On Kinematic Waves. II. A Theory of Traffic Flow on Long Crowded Roads,” in Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, vol. 229, no. 1178, pp. 317–345, May 1955.

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

This work was supported by the AUTO21 NCE research grant for the project F303-FVT.

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Correspondence to Dusit Niyato .

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Niyato, D., Hossain, E., Issariyakul, T. (2011). An Adaptive WiFi/WiMAX Networking Platform for Cognitive Vehicular Networks. In: Yu, F. (eds) Cognitive Radio Mobile Ad Hoc Networks. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6172-3_12

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  • DOI: https://doi.org/10.1007/978-1-4419-6172-3_12

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