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A learning automata and clustering-based routing protocol for named data networking

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Abstract

Named data networking (NDN) is a new information-centric networking architecture in which data or content is identified by a unique name and saved pieces of the content are used in the cache of routers. Certainly, routing is one of the major challenges in these networks. In NDN, to achieve the required data for users, interest messages containing the names of data are sent. Because the source and destination addresses are not included in this package, routers forward them using the names that carried in packages. This forward will continue until the interest package is served. In this paper, we propose a routing algorithm for NDN. The purpose of this protocol is to choose a path with the minimum cost in order to enhance the quality of internet services. This is done using learning automata with multi-level clustering and the cache is placed in each cluster head. Since the purpose of this paper is to provide a routing protocol and one of the main rules of routing protocol in NDN is that alternative paths should be found in each path request, so, we use multicast trees to observe this rule. One way of making multicast trees is by using algorithms of the Steiner tree construction in the graph. According to the proposed algorithm, the content requester and content owners are the Steiner tree root and terminal nodes, respectively. Dijkstra’s algorithm is one of the proper algorithms in routing which is used for automata convergence. The proposed algorithm has been simulated in NS2 environment and proved by mathematical rules. Experimental results show the excellence of the proposed method over the one of the most common routing protocols in terms of the throughput, control message overhead, packet delivery ratio and end-to-end delay.

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Notes

  1. Network simulator.

References

  1. Named data networking. http://www.named-data.net/.

  2. Project CCNx. http://www.ccnx.org.

  3. Drira, W., & Filali, F. (2014). A Pub/Sub extension to NDN for efficient data collection and dissemination in V2X networks. A World of Wireless, Mobile and Multimedia Networks (WoWMoM). In IEEE 15th International Symposium, pp. 1–7.

  4. Amadeo, M., Campolo, C., & Molinaro, A. (2015). Forwarding strategies in named data wireless ad hoc networks: Design and evaluation. Journal of Network and Computer Applications, 50, 148–158.

    Article  Google Scholar 

  5. Mauri, G., Verticale, G. (2013). Distributing Key Revocation Status in Named data networking. Advances in Communication Networking, Springer, pp. 310–313.

  6. Conti, M., Gasti, P., & Teoli, M. (2013). A lightweight mechanism for detection of cache pollution attacks in named data networking. Computer Networks, 57, 3178–3191.

    Article  Google Scholar 

  7. Wählisch, M., Schmidt, T. C., & Vahlenkamp, M. (2013). Backscatter from the data plane-threats to stability and security in information-centric network infrastructure. Computer Networks, 57, 3192–3206.

    Article  Google Scholar 

  8. NDN Project. NDN Platform. (2013). http://named-data.net/codebase/platform/.

  9. Arianfar, S., Sarolahti, P., Ott, J. (2013). Deadline-based resource management for information-centric networks. In Proceedings of the 3rd ACM SIGCOMM Workshop on Information-Centric Networking, pp. 49–54.

  10. Hoque, A., Amin, S. O., Alyyan, A., Zhang, B., Zhang, L., Wang, L. (2013). Nisr: named-data link state routing protocol. In Proceedings of the 3rd ACM SIGCOMM Workshop on Information-Centric Networking, pp. 15–20.

  11. Carzaniga, A., Rutherford, M. J., Wolf, A. L. (2004). A routing scheme for content-based networking. INFOCOM 2004. In Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, pp. 918–928.

  12. Bari, M., Chowdhury, S., Ahmed, R., Boutaba, R., & Mathieu, B. (2012). A survey of naming and routing in information-centric networks. IEEE Communications Magazine, 50, 44–53.

    Article  Google Scholar 

  13. Torres, J., Ferraz, L., & Duarte, O. (2012). Controller-based routing scheme for Named Data Network. Electrical Engineering Program, COPPE/UFRJ, Tech: Rep.

  14. Wang, L., Hoque, A., Yi, C., Alyyan, A., & Zhang, B. (2012). OSPFN: An OSPF based routing protocol for named data networking. University of Memphis and University of Arizona, Tech Rep.

  15. Dai, H., Lu, J., Wang, Y., Liu, B. (2012). A two-layer intra-domain routing scheme for Named data networking. IEEE Global Communications Conference (GLOBECOM), 2012, pp. 2815–2820.

  16. Carzaniga, A., Rutherford, M. J., Wolf, A. L. (2004). A routing scheme for content-based networking. In Proc. of IEEE INFOCOM.

  17. DiBenedetto, S., Gasti, P., Tsudik, G., Uzun, E. (2011). ANDaNA: Anonymous named data networking application. arXiv:1112.2205.

  18. Yi, C., Afanasyev, A., Wang, L., Zhang, B., & Zhang, L. (2012). Adaptive forwarding in Named data networking. ACM SIGCOMM Computer Communication Review, 42, 62–67.

    Article  Google Scholar 

  19. Nguyen, A. D., Sénac, P., Ramiro, V., Diaz, M. (2011). Pervasive intelligent routing in content centric delay tolerant networks. In IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing (DASC), pp. 178–185.

  20. Kim, Y., An, J., Lee, Y. -H. (2012). CCNFRR: Fast one-hop Re-Route in CCN. In IEEE International Conference on Communications (ICC), 2012, pp. 5799–5803.

  21. Kim, J.-J., Ryu, M.- W., Cha, S.- H., Cho, K. -H. (2013). A cluster based multi-path routing protocol for support load-balancing in content-centric network. In International Conference on Information Science and Applications (ICISA), 2013, 1–2.

  22. Eymann, J., Timm-Giel, A. (2013). Multipath transmission in content centric networking using a probabilistic ant-routing mechanism. Mobile Networks and Management, ed: Springer, pp. 45–56.

  23. Benoit, A., Brenner, L., Fernandes, P., & Plateau, B. (2004). Aggregation of stochastic automata networks with replicas. Linear Algebra and its Applications, 386, 111–136.

    Article  Google Scholar 

  24. Galata, A., Johnson, N., & Hogg, D. (2001). Learning variable-length Markov models of behavior. Computer Vision and Image Understanding, 81, 398–413.

    Article  Google Scholar 

  25. Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2001). On clustering validation techniques. Journal of Intelligent Information Systems, 17, 107–145.

    Article  Google Scholar 

  26. Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys, 31, 264–323.

    Article  Google Scholar 

  27. Anderson, E., Patterson, D. A. (1997). Extensible, scalable monitoring for clusters of computers. LISA, pp. 9–16.

  28. Li, C., Liu, W., Wang, L., Li, M., & Okamura, K. (2015). Energy-efficient quality of service aware forwarding scheme for Content-Centric Networking. Journal of Network and Computer Applications, 58, 241–254.

    Article  Google Scholar 

  29. Tin-Yu, W., Lee, W.-T., Duan, C.-Y., & Yu-Wei, W. (2014). Data lifetime enhancement for improving QoS in NDN. Procedia Computer Science, 32, 69–76.

    Article  Google Scholar 

  30. Bradai, A., Ahmed, T., Boutaba, R., & Ahmed, R. (2014). Efficient content delivery scheme for layered video streaming in large-scale networks. Journal of Network and Computer Applications, 45, 1–14.

    Article  Google Scholar 

  31. Yuemei, X., Li, Y., Lin, T., Wang, Z., Niu, W., Tang, H., et al. (2014). A novel cache size optimization scheme based on manifold learning in content centric networking. Journal of Network and Computer Applications, 37, 273–281.

    Article  Google Scholar 

  32. Eum, S., Nakauchi, K., Murata, M., Shoji, Y., & Nishinaga, N. (2013). Potential based routing as a secondary best-effort routing for Information-centric networkinging (ICN). Computer Networks, 57(16), 3154–3164.

    Article  Google Scholar 

  33. Akbari Torkestani, J., & Meybodi, M. R. (2010). Mobility-based multicast routing algorithm for wireless mobile ad-hoc networks: A learning automata approach. Computer Communications, 33, 721–735.

    Article  Google Scholar 

  34. Torkestani, J. A., & Meybodi, M. R. (2012). A learning automata-based heuristic algorithm for solving the minimum spanning tree problem in stochastic graphs. The Journal of Supercomputing, 59, 1035–1054.

    Article  Google Scholar 

  35. Akbari Torkestani, J., & Meybodi, M. R. (2011). Learning automata-based algorithms for solving stochastic minimum spanning tree problem. Applied Soft Computing, 11, 4064–4077.

    Article  Google Scholar 

  36. Akbari Torkestani, J., & Meybodi, M. R. (2010). Learning automata-based algorithms for finding minimum weakly connected dominating set in stochastic graphs. International Journal of Uncertainty Fuzziness and Knowledge-based Systems, 18, 721–758.

    Article  Google Scholar 

  37. Rodolakis, G., Laouiti, A., Jacquet, P., & Meraihi Naimi, A. (2008). Multicast overlay spanning trees in ad hoc networks: Capacity bounds, protocol design and performance evaluation. Computer Communications, 31, 1400–1412.

    Article  Google Scholar 

  38. Akbari Torkestani, J., & Meybodi, M. R. (2011). A link stability-based multicast routing protocol for wireless mobile ad hoc networks. Journal of Network and Computer Applications, 34, 1429–1440.

    Article  Google Scholar 

  39. Akbari Torkestani, J., & Meybodi, M. R. (2011). A cellular learning automata-based algorithm for solving the vertex coloring problem. Expert Systems with Applications, 38, 9237–9247.

    Article  Google Scholar 

  40. Hutson, K. R., & Shier, D. R. (2006). Minimum spanning trees in networks with varying edge weights. Annals of Operations Research, 146, 3–18.

    Article  Google Scholar 

  41. Rezaei, Z., & Torkestani, J. A. (2012). An energy-efficient MCDS-based routing algorithm for wireless sensor networks: Learning automata approach. Przeglad Elektrotechniczny, 11, 147–151.

    Google Scholar 

  42. Misra, S., Krishna, P. V., Saritha, V., Agarwal, H., & Ahuja, A. (2014). Learning automata-based multi-constrained fault-tolerance approach for effective energy management in smart grid communication network. Journal of Network and Computer Applications, 44, 212–219.

    Article  Google Scholar 

  43. Rezvanian, A., Rahmati, M., & Meybodi, M. R. (2014). Sampling from complex networks using distributed learning automata. Physica A, 396, 224–234.

    Article  Google Scholar 

  44. Kumar, N., Chilamkurti, N., & Rodrigues, J. J. (2014). Learning automata-based opportunistic data aggregation and forwarding scheme for alert generation in vehicular ad hoc networks. Computer Communications, 39, 22–32.

    Article  Google Scholar 

  45. Kumar, N., Tyagi, S., & Deng, D. J. (2014). LA-EEHSC: Learning automata based energy efficient heterogeneous selective clustering for wireless sensor networks. Journal of Network and Computer Applications, 46, 264–279.

    Article  Google Scholar 

  46. Beigy, H., & Meybodi, M. R. (2006). Utilizing distributed learning automata to solve stochastic shortest path problems. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 14, 591–615.

    Article  Google Scholar 

  47. Lai, W. K., Tsai, C.-D., & Shieh, C.-S. (2008). Dynamic appointment of ABR for the OSPF routing protocol. Computer Communications, 31(14), 3098–3102.

    Article  Google Scholar 

  48. Lian, J., Agnew, G. B., Naik, S. (2003). A variable degree based clustering algorithm for networks. In Proceedings of the 12th International Conference on Computer Communications and Networks, ICCCN 2003, pp. 465–470.

  49. Zhang, H., Liu, H., Jiang, C., & Chu, X. (2015). A practical semi-dynamic clustering scheme using affinity propagation in cooperative picocells. IEEE Transactions on Vehicular Technology, 64(9), 4372–4377.

  50. Sourlas, V., Psaras, I., Saino, L., & Pavlou, G. (2016). Efficient hash-routing and domain clustering techniques for information-centric networks. Computer Networks, 103, 67–83.

    Article  Google Scholar 

  51. Fortz, B., Thorup, M. (2000). Internet traffic engineering by optimizing OSPF weights. In IEEE INFOCOM, pp. 519–528.

  52. Giroire, F., Perennes, S., & Tahiri, I. (2013). On the hardness of equal shortest path routing. Electronic Notes in Discrete Mathematics, 41, 439–446.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Zeinab Shariat & Mehdi Hoseinzadeh

  2. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Ali Movaghar

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  1. Zeinab Shariat
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Correspondence to Zeinab Shariat.

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Shariat, Z., Movaghar, A. & Hoseinzadeh, M. A learning automata and clustering-based routing protocol for named data networking. Telecommun Syst 65, 9–29 (2017). https://doi.org/10.1007/s11235-016-0209-8

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  • DOI: https://doi.org/10.1007/s11235-016-0209-8

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