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

  • Sheng Hao
  • Huyin Zhang
  • Mengkai Song
Research paper


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.


MANET routing stability measurement model effective energy ratio function learning automata theory feedback mechanism optimization 


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  1. [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): 166–174CrossRefGoogle Scholar
  2. [2]
    R. Bruno, M. Conti, E. Gregori. Mesh networks: commodity multihop Ad Hoc networks [J]. IEEE Press, 2005, 43(3): 123–131Google Scholar
  3. [3]
    Z. Yang, Y. Liu. Understanding node localizability of wireless Ad Hoc and sensor networks [J]. IEEE Transactions on Mobile Computing, 2012, 11(8): 1249–1260CrossRefGoogle Scholar
  4. [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): 76–85CrossRefGoogle Scholar
  5. [5]
    N. T. Dinh, Y. Kim. Information-centric dissemination protocol for safety information in vehicular ad-hoc networks [J]. Wireless Networks, 2017, 23(5): 1359–1371CrossRefGoogle Scholar
  6. [6]
    S. J. Lee, W. Su, M. Gerla. Wireless Ad Hoc multicast routing with mobility prediction [J]. Mobile Networks and Applications, 2001, 6(4): 351–360CrossRefzbMATHGoogle Scholar
  7. [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): 255–270CrossRefGoogle Scholar
  8. [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: 161–166Google Scholar
  9. [9]
    S. Guo, O. Yang. Maximizing multicast communication lifetime in wireless mobile Ad Hoc networks [J]. IEEE Transactions on Vehicular Technology, 2008, 57(4): 2414–2425CrossRefGoogle Scholar
  10. [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: 252–256Google Scholar
  11. [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): 1797–1806CrossRefGoogle Scholar
  12. [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): 199–208CrossRefGoogle Scholar
  13. [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): 663–679CrossRefGoogle Scholar
  14. [14]
    Selvi, F. A. Pitchaimuthu. Ant based multipath backbone routing for load balancing in MANET [J]. IET Communications, 2017, 11(1): 136–141Google Scholar
  15. [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): 1–11CrossRefGoogle Scholar
  16. [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: 151–156Google Scholar
  17. [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): 89–108CrossRefGoogle Scholar
  18. [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: 321–326Google Scholar
  19. [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): 2081–2100CrossRefGoogle Scholar
  20. [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: 321–328CrossRefGoogle Scholar
  21. [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): 221–235CrossRefGoogle Scholar
  22. [22]
    S. K. Das, S. Tripathi. Intelligent energy-aware efficient routing for MANET [J]. Wireless Networks, 2016(7): 1–21Google Scholar
  23. [23]
    K. S. Narendra, M. A. L. Thathachar. Learning automata: An introduction [M]. USA: DBLP, 2012Google Scholar
  24. [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): 143–166MathSciNetCrossRefzbMATHGoogle Scholar
  25. [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): 591–615MathSciNetCrossRefzbMATHGoogle Scholar
  26. [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): 711–722CrossRefGoogle Scholar
  27. [27]
    A. A. Anasane, R. A. Satao. A survey on various multipath routing protocols in wireless sensor networks [J]. Procedia Computer Science, 2016, 79: 610–615CrossRefGoogle Scholar
  28. [28]
    D. B. West. Introduction to graph theory [M]. 2nd ed. USA: McGraw-Hill Higher Education, 2005: 260Google Scholar
  29. [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): 2287–2298CrossRefGoogle Scholar
  30. [30]
    H. J. Kushner. Approximation and weak convergence methods for random processes, with applications to stochastic systems theory [M]. USA: MIT Press, 1984zbMATHGoogle Scholar
  31. [31]
    G. Wahba. Erratum: spline interpolation and smoothing on the sphere [J]. Siam Journal on Scientific & Statistical Computing, 2012, 2(2): 5–16zbMATHGoogle Scholar
  32. [32]
    J. Chen, W. Li. Convergence behaviour of inexact Newton methods under weak Lipschitz condition [J]. Journal of Computational & Applied Mathematics, 2006, 191(1):143–164MathSciNetCrossRefzbMATHGoogle Scholar
  33. [33]
    G. A. Anastassiou, S. G. Gal. Approximation theory: Moduli of continuity and global smoothness preservation [M]. USA: DBLP, 2000CrossRefzbMATHGoogle Scholar
  34. [34]
    G. F. Riley, T. R. Henderson. The ns-3 network simulator [J]. Modeling and Tools for Network Simulation, 2010: 15–34CrossRefGoogle Scholar
  35. [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: 137–145CrossRefGoogle Scholar

Copyright information

© Posts & Telecom Press and Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of ComputerWuhan UniversityWuhanChina

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