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A method of routing optimization using CHNN in MANET

  • Hua YangEmail author
  • Zhimei Li
  • Zhiyong Liu
Original Research

Abstract

The performance of routing protocols determines the performance of MANET, and the development and improvement of routing protocols have been the hotspot of MANET research. DSR, as one of the IETF standardized routing protocol, which can be applied to large-scale scenario of fast-moving nods, is of high value of improvement. Based on the DSR, the stability of the link is estimated by using Continuous Hopfield Neural Network to find the route with the highest stability from the source node to the destination node to improve the performance of the DSR and improve the performance of the MANET. The simulation results show that compared with DSR and CHNN-DSR, CHNN-DSR has better performance in packet delivery performance, such as packet delivery ratio, average end-to-end delay and so on.

Keywords

CHNN DSR Routing metric MANET 

Notes

Acknowledgements

Supported by the Opening Project of Guangxi Colleges and Universities Key Laboratory of robot and welding. The project of Guangxi Education Department (KY2016YB531, 2017KY0868).

References

  1. Aghbashlo M, Hosseinpour S, Mujumdar AS (2015) Application of artificial neural networks (ANNs) in drying technology: a comprehensive review. Drying Technol 33(12):1397–1462CrossRefGoogle Scholar
  2. Agrawal VM, Chauhan H (2015) An Overview of security issues in Mobile Ad hoc Networks. International Journal of Computer Engineering Sciences 1(1):9–17CrossRefGoogle Scholar
  3. Ali AKS, Kulkarni U (2015) Characteristics, applications and challenges in mobile Ad-Hoc networks (MANET): overview. Wireless Netw 3(12)Google Scholar
  4. Carlini N, Wagner D (2017) Towards evaluating the robustness of neural networks. In: IEEE Symposium on Security and Privacy (SP). IEEE, pp 39–57Google Scholar
  5. Chang JM, Tsou PC, Woungang I, Chao HC, Lai CF (2015) Defending against collaborative attacks by malicious nodes in MANETs: A cooperative bait detection approach. IEEE systems journal 9(1):65–75CrossRefGoogle Scholar
  6. Chatterjee S, Das S (2015) Ant colony optimization based enhanced dynamic source routing algorithm for mobile Ad-hoc network. Inf Sci 295:67–90MathSciNetCrossRefGoogle Scholar
  7. Chojaczyk AA, Teixeira AP, Neves LC, Cardoso JB, Soares CG (2015) Review and application of artificial neural networks models in reliability analysis of steel structures. Struct Saf 52:78–89CrossRefGoogle Scholar
  8. Clausen T, Jacquet P (2003) Optimized link state routing protocol (OLSR) (No. RFC 3626)Google Scholar
  9. Clausen T, Dearlove C, Jacquet P, Herberg U (2014) The optimized link state routing protocol version 2 (No. RFC 7181)Google Scholar
  10. Ganesan T, Vasant P, Elamvazuthi I (2014) Hopfield neural networks approach for design optimization of hybrid power systems with multiple renewable energy sources in a fuzzy environment. Journal of Intelligent Fuzzy Systems 26(5):2143–2154MathSciNetGoogle Scholar
  11. Hoebeke J, Moerman I, Dhoedt B, Demeester P (2004) An overview of mobile ad hoc networks: applications and challenges. Journal-Communications Network 3(3):60–66Google Scholar
  12. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79(8):2554–2558MathSciNetCrossRefzbMATHGoogle Scholar
  13. Hopfield JJ (1988) Artificial neural networks. IEEE Circuits Devices Mag 4(5):3–10CrossRefGoogle Scholar
  14. Jeong M, Ahn S, Oh H (2016) A network coding aware routing with considering traffic load balancing for the multi-hop wireless networks. In Information Networking (ICOIN), 2016 International Conference on (pp. 382–384). IEEEGoogle Scholar
  15. Jha RK, Kharga P (2015) A comparative performance analysis of routing protocols in MANET using NS3 simulator. Int J Comput Netw Inf Secur 7(4):62Google Scholar
  16. Jiang ZY, Ma JF, Jing X (2015) Enhancing traffic capacity of scale-free networks by employing hybrid routing strategy. Physica A 422:181–186CrossRefGoogle Scholar
  17. Johnson D, Hu YC, Maltz D (2007) The dynamic source routing protocol (DSR) for mobile ad hoc networks for IPv4 (No. RFC 4728)Google Scholar
  18. Joya G, Atencia MA, Sandoval F (2002) Hopfield neural networks for optimization: study of the different dynamics. Neurocomputing 43(1):219–237CrossRefzbMATHGoogle Scholar
  19. Kubat M (2015) Artificial neural networks. In: An introduction to machine learning. Springer International Publishing, pp 91–111Google Scholar
  20. Kumar R, Routray SK (2016) Ant Colony based dynamic source routing for VANET. In Applied and Theoretical Computing and Communication Technology (iCATccT), 2016 2nd International Conference on (pp. 279–282). IEEEGoogle Scholar
  21. Loo J, Mauri JL, Ortiz JH (2016) Mobile ad hoc networks: current status and future trends. CRC PressGoogle Scholar
  22. Maarouf M, Sosa A, Galván B, Greiner D, Winter G, Mendez M, Aguasca R (2015) The role of artificial neural networks in evolutionary optimisation: a review. Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences. Springer International Publishing, pp 59–76Google Scholar
  23. Macker J (1999) Mobile ad hoc networking (MANET): routing protocol performance issues and evaluation considerationsGoogle Scholar
  24. Mai Y, Bai Y, Wang N (2017) Performance comparison and evaluation of the routing protocols for MANETs using NS3Google Scholar
  25. Manoufali M, Kong PY, Alshaer H, Jimaa S (2015) An overview of maritime wireless mesh communication technologies and protocols. Mobile computing and wireless networks: concepts, methodologies, tools, and applications, p 171Google Scholar
  26. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133MathSciNetCrossRefzbMATHGoogle Scholar
  27. Perkins C, Belding-Royer E, Das S (2003) Ad hoc on-demand distance vector (AODV) routing (No. RFC 3561)Google Scholar
  28. Rajawat S, Kuri M, Chaudhary A, Choudhary SS (2016) Effective congestion less dynamic source routing for data transmission in MANETs. In: Proceedings of the international congress on information and communication technology. Springer, Singapore, pp 499–511Google Scholar
  29. Safdar M, Khan IA, Ullah F, Khan F, Jan SR (2016) Comparative study of routing protocols in mobile adhoc networks. Int J Comput Sci Trends Technol (ISSN 2347–8578) Google Scholar
  30. Sainath TN, Kingsbury B, Saon G, Soltau H, Mohamed AR, Dahl G, Ramabhadran B (2015) Deep convolutional neural networks for large-scale speech tasks. Neural Netw 64:39–48CrossRefGoogle Scholar
  31. Scardapane S, Wang D (2017) Randomness in neural networks: an overview. Wiley Interdiscip Rev Data Min Knowl Discov 7(2):1–18CrossRefGoogle Scholar
  32. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural networks 61:85–117CrossRefGoogle Scholar
  33. Shen J, Wang C, Wang A, Sun X, Moh S, Hung PC (2017) Organized topology based routing protocol in incompletely predictable ad-hoc networks. Comput Commun 99:107–118CrossRefGoogle Scholar
  34. Turkson RF, Yan F, Ali M K A, Hu J (2016) Artificial neural network applications in the calibration of spark-ignition engines: an overview. Eng Sci Technol Int J 19(3):1346–1359CrossRefGoogle Scholar
  35. Wang H, Yu Y, Wen G, Zhang S, Yu J (2015) Global stability analysis of fractional-order Hopfield neural networks with time delay. Neurocomputing 154:15–23CrossRefGoogle Scholar
  36. Zhang H, Wang Z, Liu D (2014) A comprehensive review of stability analysis of continuous-time recurrent neural networks. IEEE Trans Neural Netw Learn Syst 25(7):1229–1262CrossRefGoogle Scholar
  37. Zhang S, Yu Y, Wang Q (2016) Stability analysis of fractional-order Hopfield neural networks with discontinuous activation functions. Neurocomputing 171:1075–1084CrossRefGoogle Scholar
  38. Zhong C, Luo C, Chu Z, Gan W (2017) A continuous hopfield neural network based on dynamic step for the traveling salesman problem. Neural Networks (IJCNN), 2017 International Joint Conference (pp 3318–3323). IEEEGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Guangxi Colleges and Universities Key Laboratory of Robot and WeldingGuilin University of Aerospace TechnologyGuilinChina
  2. 2.Guilin University of Aerospace TechnologyGuilinChina

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