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A Stable and Energy-Efficient Routing Algorithm Based on Learning Automata Theory for MANET

  • Research paper
  • Published:
Journal of Communications and Information Networks

Abstract

The mobile Ad Hoc network (MANET) is a self-organizing and self-configuring wireless network, consisting of a set of mobile nodes. The design of efficient routing protocols for MANET has always been an active area of research. In existing routing algorithms, however, the current work does not scale well enough to ensure route stability when the mobility and distribution of nodes vary with time. In addition, each node in MANET has only limited initial energy, so energy conservation and balance must be taken into account. An efficient routing algorithm should not only be stable but also energy saving and balanced, within the dynamic network environment. To address the above problems, we propose a stable and energy-efficient routing algorithm, based on learning automata (LA) theory for MANET. First, we construct a new node stability measurement model and define an effective energy ratio function. On that basis, we give the node a weighted value, which is used as the iteration parameter for LA. Next, we construct an LA theory-based feedback mechanism for the MANET environment to optimize the selection of available routes and to prove the convergence of our algorithm. The experiments show that our proposed LA-based routing algorithm for MANET achieved the best performance in route survival time, energy consumption, energy balance, and acceptable performance in end-to-end delay and packet delivery ratio.

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References

  1. L. Blazevic, L. Buttyan, S. Capkun, et al. Self-organization in mobile ad-hoc networks: the approach of terminodes [J]. IEEE Communications Magazine, 2001, 39(6): 166–174

    Article  Google Scholar 

  2. R. Bruno, M. Conti, E. Gregori. Mesh networks: commodity multihop Ad Hoc networks [J]. IEEE Press, 2005, 43(3): 123–131

    Google Scholar 

  3. Z. Yang, Y. Liu. Understanding node localizability of wireless Ad Hoc and sensor networks [J]. IEEE Transactions on Mobile Computing, 2012, 11(8): 1249–1260

    Article  Google Scholar 

  4. M. A. Rahman, M. S. Hossain. A location-based mobile crowdsensing framework supporting a massive Ad Hoc social network environment [J]. IEEE Communications Magazine, 2017, 55(3): 76–85

    Article  Google Scholar 

  5. N. T. Dinh, Y. Kim. Information-centric dissemination protocol for safety information in vehicular ad-hoc networks [J]. Wireless Networks, 2017, 23(5): 1359–1371

    Article  Google Scholar 

  6. S. J. Lee, W. Su, M. Gerla. Wireless Ad Hoc multicast routing with mobility prediction [J]. Mobile Networks and Applications, 2001, 6(4): 351–360

    Article  MATH  Google Scholar 

  7. B. An, S. Papavassiliou. MHMR: Mobility-based hybrid multicast routing protocol in mobile Ad Hoc wireless networks [J]. Wireless Communications and Mobile Computing, 2003, 3(2): 255–270

    Article  Google Scholar 

  8. A. Bentaleb, S. Harous, A. Boubetra. A weight based clustering scheme for mobile Ad Hoc networks [C]//The 11th International Conference on Advances in Mobile Computing and Multimedia, Vienna, 2013: 161–166

    Google Scholar 

  9. S. Guo, O. Yang. Maximizing multicast communication lifetime in wireless mobile Ad Hoc networks [J]. IEEE Transactions on Vehicular Technology, 2008, 57(4): 2414–2425

    Article  Google Scholar 

  10. H. B. Thriveni, G. M. Kumar, R. Sharma. Performance evaluation of routing protocols in mobile ad-hoc networks with varying node density and node mobility [C]//International Conference on Communication Systems and Network Technologies, Gwalior, 2013: 252–256

    Google Scholar 

  11. R. Suraj, S. Tapaswi, S. Yousef, et al. Mobility prediction in mobile Ad Hoc networks using a lightweight genetic algorithm [J]. Wireless Networks, 2016, 22(6): 1797–1806

    Article  Google Scholar 

  12. T. Manimegalai, C. Jayakumar, G. Gunasekaran. Using animal communication strategy (ACS) for MANET routing [J]. Journal of the National Science Foundation of Sri Lanka, 2015, 43(3): 199–208

    Article  Google Scholar 

  13. G. Singal, V. Laxmi, M. S. Gaur, et al. Moralism: mobility prediction with link stability based multicast routing protocol in MANETs [J]. Wireless Networks, 2017, 23(3): 663–679

    Article  Google Scholar 

  14. Selvi, F. A. Pitchaimuthu. Ant based multipath backbone routing for load balancing in MANET [J]. IET Communications, 2017, 11(1): 136–141

    Google Scholar 

  15. A. Kout, S. Labed, S. Chikhi, et al. AODVCS, a new bio-inspired routing protocol based on cuckoo search algorithm for mobile Ad Hoc networks [J]. Wireless Networks, 2017(9): 1–11

    Article  Google Scholar 

  16. J. Liu, Y. Xu, X. Jiang. End-to-end delay in two hop relay MANETs with limited buffer [C]//Second International Symposium on Computing and Networking, Shizuoka, 2015: 151–156

    Google Scholar 

  17. S. Chettibi, S. Chikhi. Adaptive maximum-lifetime routing in mobile ad-hoc networks using temporal difference reinforcement learning [J]. Evolving Systems, 2014, 5(2): 89–108

    Article  Google Scholar 

  18. A. Petrowski, F. Aissanou, I. Benyahia, et al. Multicriteria reinforcement learning based on a Russian doll method for network routing [C]//IEEE International Conference Intelligent Systems, London, 2010: 321–326

    Google Scholar 

  19. P. Vijayalakshmi, S. A. J. Francis, J. A. Dinakaran. A robust energy efficient ant colony optimization routing algorithm for multi-hop Ad Hoc networks in MANETs [J]. Wireless Networks, 2016, 22(6): 2081–2100

    Article  Google Scholar 

  20. S. Chettibi, S. Chikhi. Dynamic fuzzy logic and reinforcement learning for adaptive energy efficient routing in mobile ad-hoc networks [J]. Applied Soft Computing, 2016,38: 321–328

    Article  Google Scholar 

  21. P. Srivastava, R. Kumar. A new QoS-aware routing protocol for MANET using artificial neural network [J]. Journal of Computing and Information Technology, 2016, 24(3): 221–235

    Article  Google Scholar 

  22. S. K. Das, S. Tripathi. Intelligent energy-aware efficient routing for MANET [J]. Wireless Networks, 2016(7): 1–21

    Google Scholar 

  23. K. S. Narendra, M. A. L. Thathachar. Learning automata: An introduction [M]. USA: DBLP, 2012

    Google Scholar 

  24. M. A. L. Thathachar, P. S. Sastry. A hierarchical system of learning automata that can learn the globally optimal path [J]. Information Sciences, 1987, 42(2): 143–166

    Article  MathSciNet  MATH  Google Scholar 

  25. H. Beigy, M. R. Meybodi. Utilizing distributed learning automata to solve stochastic shortest path problems [J]. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2006, 14(05): 591–615

    Article  MathSciNet  MATH  Google Scholar 

  26. M. L. Thathachar, P. S. Sastry. Varieties of learning automata: an overview [J]. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, 2002, 32(6): 711–722

    Article  Google Scholar 

  27. A. A. Anasane, R. A. Satao. A survey on various multipath routing protocols in wireless sensor networks [J]. Procedia Computer Science, 2016, 79: 610–615

    Article  Google Scholar 

  28. D. B. West. Introduction to graph theory [M]. 2nd ed. USA: McGraw-Hill Higher Education, 2005: 260

    Google Scholar 

  29. I. Das, D. K. Lobiyal, C. P. Katti. Multipath routing in mobile Ad Hoc network with probabilistic splitting of traffic [J]. Wireless Networks, 2016, 22(7): 2287–2298

    Article  Google Scholar 

  30. H. J. Kushner. Approximation and weak convergence methods for random processes, with applications to stochastic systems theory [M]. USA: MIT Press, 1984

    MATH  Google Scholar 

  31. G. Wahba. Erratum: spline interpolation and smoothing on the sphere [J]. Siam Journal on Scientific & Statistical Computing, 2012, 2(2): 5–16

    MATH  Google Scholar 

  32. J. Chen, W. Li. Convergence behaviour of inexact Newton methods under weak Lipschitz condition [J]. Journal of Computational & Applied Mathematics, 2006, 191(1):143–164

    Article  MathSciNet  MATH  Google Scholar 

  33. G. A. Anastassiou, S. G. Gal. Approximation theory: Moduli of continuity and global smoothness preservation [M]. USA: DBLP, 2000

    Book  MATH  Google Scholar 

  34. G. F. Riley, T. R. Henderson. The ns-3 network simulator [J]. Modeling and Tools for Network Simulation, 2010: 15–34

    Chapter  Google Scholar 

  35. M. S. Khan, Q. K. Jadoon, M. I. Khan. Mobile and wireless technology 2015: A comparative performance analysis of MANET routing protocols under security attacks [M]. Germany: Springer Berlin Heidelberg, 2015, 310: 137–145

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer, Wuhan University, Wuhan, 430079, China

    Sheng Hao, Huyin Zhang & Mengkai Song

Authors
  1. Sheng Hao
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  2. Huyin Zhang
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  3. Mengkai Song
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Corresponding author

Correspondence to Huyin Zhang.

Additional information

This work is supported by the National Natural Science Foundation of China (No. 61772386), Guangdong provincial science and technology project (No. 2015B010131007). The associate editor coordinating the review of this paper and approving it for publication was W. Quan.

Sheng Hao was born in Lanzhou. He received his B.E. and M.S. degrees in computer science and technology from Wuhan University. He is now a Ph.D. candidate of architecture. His research interests include wireless network, communication theory and complex network theory.

Huyin Zhang [corresponding author] was born in Shanghai. He received his Ph.D. degree in computer science and technology from Wuhan university. He is a professor of Wuhan university. His research interests include high performance computing, network quality of service, new generation network architecture.

Mengkai Song was born in Wuhan. He received his undergraduate degree in computer science and technology from Wuhan university. He is now a graduate student of computer network. His research interests include wireless network and differential privacy.

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Hao, S., Zhang, H. & Song, M. A Stable and Energy-Efficient Routing Algorithm Based on Learning Automata Theory for MANET. J. Commun. Inf. Netw. 3, 52–66 (2018). https://doi.org/10.1007/s41650-018-0012-7

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  • DOI: https://doi.org/10.1007/s41650-018-0012-7

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