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Modeling and improving the throughput of vehicular networks using cache enabled RSUs

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Abstract

Newly emerged applications in vehicular networks demand high throughput to transfer large amount of data through both Vehicle-to-Vehicle and Vehicle-to-Infrastructure links. One solution which recently draws researchers attention to itself for improving the throughput in this type of network is to deploy some Road-Side-Units (RSUs) in the streets with storage capability and store the data closer to the end users. Consequently, vehicles are able to download their inquired contents from these local RSUs instead of the base station. This will decrease the network traffic of the base station and also the average delay each vehicle has to wait to receive his requested files. The main issue to implement this distributed approach in this type of environment compared to other types of networks is that the fast moving vehicles make the topology of the network highly dynamic. Also due to limited storage capacity of the caches in the RSUs, we should decide on how to distribute the contents in the RSUs to maximize the number of locally satisfied vehicles. In this paper, we address the cache content placement problem in vehicular networks and model it using a game theoretic approach. We show that the proposed game model is a special case of generalized covering games. Considering the hit ratio of the caches as the performance metric in our model, we propose a method to distributively optimize this metric using the RSU’s local information. In addition, we propose a combinatorial approach to find efficient file placements in the RSUs using Markov approximation. Empirical evaluations on realistic trace-based simulations show an improvement of 7.5% in the average hit ratio of the proposed method compared to other well-known cache content placement approaches. Newly emerged applications in vehicular networks demand high throughput to transfer large amount of data through both Vehicle-to-Vehicle and Vehicle-to-Infrastructure links. To improve the network throughput, we deploy some Road-Side-Units (RSUs) in the streets with storage capability and store the data closer to the end users. Consequently, vehicles are able to download their inquired contents from these local RSUs instead of the base station. The main issue to implement this distributed approach is that the fast moving vehicles make the topology of the network highly dynamic. Also due to limited storage capacity of the caches in the RSUs, we should decide on how to distribute the contents in the RSUs to maximize the number of locally satisfied vehicles. In this paper, we address the cache content placement problem in vehicular networks and model it using game theoretic approach and Combinatorial approach. We show that the proposed game model is a special case of generalized covering games. Considering the hit ratio of the caches as the performance metric in our model, we propose a method to distributively optimize this metric using the RSU’s local information. In addition, we propose a Combinatorial approach to find efficient file placements in the RSUs using Markov approximation. Empirical evaluations on realistic trace-based simulations show an improvement of 7.5% in the average hit ratio of the proposed method compared to other well-known cache content placement approaches.

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References

  1. Gerla, M., & Kleinrock, L. (2011). Vehicular networks and the future of the mobile internet. Computer Networks, 55(2), 457–469.

    Article  Google Scholar 

  2. Stein, G., Dagan, E., Mano, O & Shashua, A. (2017). Collision warning system, uS Patent 9,656,607.

  3. Kaufmann, S. & Marten, A. (2017). Method for vehicle communication by means of a vehicle-implemented vehicle diagnostic system, vehicle diagnostic interface, interace module, user communication terminal, data connection system, and diagnostic and control network for a plurality of vehicles, uS Patent 9,538,374.

  4. Jung, E. T. (2017). Vehicle location tracking device and method, uS Patent 9,702,704.

  5. Zheng, Z., Lu, Z., Sinha, P., & Kumar, S. (2015). Ensuring predictable contact opportunity for scalable vehicular internet access on the go. IEEE/ACM Transactions on Networking (TON), 23(3), 768–781.

    Article  Google Scholar 

  6. Campolo, C., Molinaro, A., Vinel, A., & Zhang, Y. (2016). Modeling and enhancing infotainment service access in vehicular networks with dual-radio devices. Vehicular Communications, 6, 7–16.

    Article  Google Scholar 

  7. Sciancalepore, V., Giustiniano, D., Banchs, A., & Hossmann-Picu, A. (2016). Offloading cellular traffic through opportunistic communications: Analysis and optimization. IEEE Journal on Selected Areas in Communications, 34(1), 122–137.

    Article  Google Scholar 

  8. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast, Online Available: http://www.cisco.com/c/en/us/soluHrBtions/collateral/service-provider/visual-networking-index-vni/whHrBite paper c11-520862.html. Accessed 20 Sept 2014.

  9. Kim, D., Velasco, Y., Yang, Z., Wang, W., Hussain, R & Uma, R. (2016). Cost effective mobile and static road side unit deployment for vehicular adhoc networks, In: 2016 International conference on computing, networking and communications (ICNC) (pp. 1–5) IEEE.

  10. Xiong, W., He, S & Qiu, T.Z. (2017). Research on connected vehicle architecture based on dsrc technology. In 2017 4th international conference on transportation information and safety (ICTIS) (pp. 530–534). IEEE.

  11. Guchhait, A., Kandar, D., & Maji, B. (2017). Design of a hybrid technology by converging wimax and dsrc for intelligent transportation system. International Journal of Sensors Wireless Communications and Control, 7(1), 33–38.

    Article  Google Scholar 

  12. Yu, B., Bai, F & Etp. (2011). Encounter transfer protocol for opportunistic vehicle communication. In INFOCOM, 2011 proceedings IEEE (pp. 2201–2209). IEEE.

  13. Goemans, M. X., Li, L., Mirrokni, V. S., & Thottan, M. (2006). Market sharing games applied to content distribution in ad hoc networks. IEEE Journal on Selected areas in Communications, 24(5), 1020–1033.

    Article  Google Scholar 

  14. Chun, B.-G., Chaudhuri, K., Wee, H., Barreno, M., Papadimitriou, C.H & Kubiatowicz, J. (2004) Selfish caching in distributed systems: a game-theoretic analysis. In: Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing (pp. 21–30). ACM.

  15. Sagratella, S. (2017). Algorithms for generalized potential games with mixed-integer variables. Computational Optimization and Applications, 68(3), 689–717.

    Article  Google Scholar 

  16. Chen, M., Liew, S. C., Shao, Z., & Kai, C. (2013). Markov approximation for combinatorial network optimization. IEEE Transactions on Information Theory, 59(10), 6301–6327.

    Article  Google Scholar 

  17. Sourlas, V., Paschos, G. S., Flegkas, P & Tassiulas, L. (2010). Mobility support through caching in content-based publish/subscribe networks. In 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing (CCGrid). (pp. 715–720) IEEE.

  18. Gaddah, A., & Kunz, T. (2010). Extending mobility to publish/subscribe systems using a pro-active caching approach. Mobile Information Systems, 6(4), 293–324.

    Article  Google Scholar 

  19. Vasilakos, X., Siris, V.A., Polyzos, G.C & Pomonis, M. (2012). Proactive selective neighbor caching for enhancing mobility support in information-centric networks. In Proceedings of the second edition of the icn workshop on information-centric networking (pp. 61–66). ACM.

  20. Golrezaei, N., Shanmugam, K., Dimakis, A. G., Molisch, A. F & Caire, G. (2012). Femtocaching: Wireless video content delivery through distributed caching helpers. In INFOCOM, 2012 Proceedings IEEE (pp. 1107–1115). IEEE.

  21. Lee, U., Park, J.-S., Yeh, J., Pau, G & Gerla, M. (2006) Code torrent: content distribution using network coding in vanet. In Proceedings of the 1st international workshop on decentralized resource sharing in mobile computing and networking. ACM.

  22. Ahmed, S & Kanhere, S.S. (2006). Vanetcode: network coding to enhance cooperative downloading in vehicular ad-hoc networks. In Proceedings of the 2006 international conference on Wireless communications and mobile computing (pp. 527–532). ACM.

  23. Li, M., Yang, Z., & Lou, W. (2011). Codeon: Cooperative popular content distribution for vehicular networks using symbol level network coding. IEEE Journal on Selected Areas in Communications, 29(1), 223–235.

    Article  Google Scholar 

  24. Shevade, U., Chen, Y.-C., Qiu, L., Zhang, Y., Chandar, V., Han, M. K., Song, H. H & Seung, Y. (2010). Enabling high-bandwidth vehicular content distribution. In Proceedings of the 6th international conference (p 23). ACM.

  25. Luan, T. H., Cai, L. X., Chen, J., Shen, X., & Bai, F. (2014). Engineering a distributed infrastructure for large-scale cost-effective content dissemination over urban vehicular networks. IEEE Transactions on Vehicular Technology, 63(3), 1419–1435.

    Article  Google Scholar 

  26. Acer, U. G., Giaccone, P., Hay, D., Neglia, G., & Tarapiah, S. (2012). Timely data delivery in a realistic bus network. IEEE Transactions on Vehicular Technology, 61(3), 1251–1265.

    Article  Google Scholar 

  27. Hadaller, D., Keshav, S., Brecht, T & Agarwal, S. (2007). Vehicular opportunistic communication under the microscope. In Proceedings of the 5th international conference on mobile systems, applications and services (pp. 206–219). ACM.

  28. Shariff, A. A. M., Katuk, N., & Zakaria, N. H. (2017). An overview to pre-fetching techniques for content caching of mobile applications. Journal of Telecommunication Electronic and Computer Engineering (JTEC), 9(2–12), 37–43.

    Google Scholar 

  29. Zhang, X.-Y., Zhang, J., Gong, Y.-J., Zhan, Z.-H., Chen, W.-N., & Li, Y. (2016). Kuhn-munkres parallel genetic algorithm for the set cover problem and its application to large-scale wireless sensor networks. IEEE Transactions on Evolutionary Computation, 20(5), 695–710.

    Article  Google Scholar 

  30. Mirrokni, V. S., & Vetta, A. (2004). Convergence issues in competitive games. In K. Jansen, S. Khanna, J. D. P. Rolim, & D. Ron (Eds.), Approximation, randomization, and combinatorial optimization. Algorithms and Techniques. RANDOM 2004, APPROX 2004. Lecture notes in Computer Science (Vol. 3122, pp. 183–194). Berlin, Heidelberg: Springer.

  31. Gairing, M. (2009). Covering games: Approximation through non-cooperation. In: Internet and network economics (pp. 184–195). Springer.

  32. Christodoulou, G., & Gairing, M. (2016). Price of stability in polynomial congestion games. ACM Transactions on Economics and Computation, 4(2), 10.

    Article  Google Scholar 

  33. Ji, M., Caire, G., & Molisch, A. F. (2016). Wireless device-to-device caching networks: Basic principles and system performance. IEEE Journal on Selected Areas in Communications, 34(1), 176–189.

    Article  Google Scholar 

  34. Shao, Z., Zhang, H., Chen, M & Ramchandran, K. (2012) Reverse-engineering bittorrent: A markov approximation perspective. In INFOCOM, 2012 Proceedings IEEE (pp. 2996–3000).

  35. Jahn, J. (2017). Karush–kuhn–tucker conditions in set optimization. Journal of Optimization Theory and Applications, 172(3), 707–725.

    Article  Google Scholar 

  36. Chen, M., Liew, S.C., Shao, Z., Kai, C. (2010) Markov approximation for combinatorial network optimization, In INFOCOM, 2010 Proceedings IEEE (pp. 1–9).

  37. Codeca, L., Frank, R., Faye, S., & Engel, T. (2017). Luxembourg sumo traffic (lust) scenario: Traffic demand evaluation. IEEE Intelligent Transportation Systems Magazine, 9(2), 52–63.

    Article  Google Scholar 

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Acknowledgements

We want to thank the researchers of Game Theory Lab at University of Colorado Boulder and researchers of NETLAB at University of Tehran.

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Correspondence to Saeid Akhavan Bitaghsir.

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Akhavan Bitaghsir, S., Khonsari, A. Modeling and improving the throughput of vehicular networks using cache enabled RSUs. Telecommun Syst 70, 391–404 (2019). https://doi.org/10.1007/s11235-018-0495-4

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