Evolutionary Computation Techniques for Intelligent Computing in Commercial Mobile Adhoc Network

##plugins.themes.academic_pro.article.sidebar##

Published Apr 1, 2021
https://doi.org/10.47164/ijngc.v12i2.205
Download
pdf
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 12, Special Issue 2, April 2021

##plugins.themes.academic_pro.article.main##

Dr. Kavita Taneja
DCSA,Panjab University,Chandigarh
Dr. Harmunish Taneja
DAV college,sector 10,Chandigarh
Ramanpreet Kaur
DCSA,Panjab University,Chandigarh

Abstract

Ubiquitous smart devices and applications are constructing pavement for Mobile Adhoc Networks (MANETs) that allow the users to communicate without any physical infrastructure. The immense usage of pervasive computing devices have fuelled virtual environments which have exponentially enhanced the popularity of commercial MANET. In today’s scenario, MANETs are used by each and every individual to perform even routine tasks. This extensive growth in number of users of mobile network has posed a gigantic challenge in catering needs of a huge set of varied users. To deliver Quality of Service (QoS) to users, there is a need to incorporate intelligent computing techniques in commercial MANETs. Emerging intelligent computing trends in commercial MANET are explored in this paper. It further explores the role of evolutionary computation approach in tackling commercial MANET challenges for improving its performance. Comparative analysis of evolutionary computation techniques for commercial MANET is presented in this paper.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Dr. Kavita Taneja, Dr. Harmunish Taneja, & Ramanpreet Kaur. (2021). Evolutionary Computation Techniques for Intelligent Computing in Commercial Mobile Adhoc Network. International Journal of Next-Generation Computing, 12(2), 209–217. https://doi.org/10.47164/ijngc.v12i2.205
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Abielmona R, Falcon R, Zincir-Heywood N, Abbass H. Recent Advances in Computational Intelligence in Defense and Security. Vol 621.; 2016. doi:10.1007/978-3-319-26450-9 .
  2. Bang A. PLR. Manet:History,Challenges and Applicaions. Int J Appl or Innov Eng Manag. 2013;2(9):249-251.
  3. Bento CR da C, Wille ECG. Bio-inspired routing algorithm for MANETs based on fungi networks. Ad Hoc Networks. 2020;107:102248. doi:10.1016/j.adhoc.2020.102248.
  4. Chlamtac I, Conti M, Liu JJN. Mobile ad hoc networking: Imperatives and challenges. Ad Hoc Networks. 2003;1(1):13-64. doi:10.1016/S1570-8705(03)00013-1.
  5. Conti M, Maselli G, Turi G, Giordano S. Cross-Layering in Mobile Ad Hoc Network Design. Computer (Long Beach Calif). 2004;37(2):48-51. doi:10.1109/MC.2004.1266295 .
  6. Dorronsoro B, Ruiz P, Danoy G, Pigne Y, and Bouvry P, ´ Evolutionary Algorithms for Mobile Ad Hoc Networks, NatureInspired Computing Series, John Wiley & Sons, Hoboken, NJ, USA, 2014, Edited by: A. Y. Zomaya, M. Mehrnoosh .
  7. Dote Y, Ovaska SJ. Industrial applications of soft computing: A review. Proc IEEE. 2001;89(9):1243-1264. doi:10.1109/5.949483 .
  8. Elaiwat S, Belal M. An evolutionary creative design approach for optimising the broadcasting trees in MANET. Int J Des Eng. 2010;3(1):97. doi:10.1504/ijde.2010.032824 .
  9. Gutierrez-Reina D, Toral Marín SL, Johnson P, Barrero F. An evolutionary computation approach for designing mobile ad hoc networks. Expert Syst Appl. 2012;39(8):6838-6845. doi:10.1016/j.eswa.2012.01.012 .
  10. Herawan T, Ghazali R, Deris MM. Recent Advances on Soft Computing and Data Mining: Proceedings of the First International Conference on Soft Computing and Data Mining (SCDM-2014) Universiti Tun Hussein Onn Malaysia, Johor, Malaysia June, 16th-18th, 2014. Adv Intell Syst Comput. 2014;287:121-131. doi:10.1007/978-3-319-07692-8 .
  11. Hussein O, Saadawi T. Ant routing algorithm for mobile ad-hoc networks (ARAMA). IEEE Int Performance, Comput Commun Conf Proc. 2003:281-290. doi:10.1109/pccc.2003.1203709 .
  12. Kulkarni, R. V., Forster, A., & Venayagamoorthy GK. Computational Intelligence in Wireless Sensor Networks: A Survey. IEEE Commun Surv Tutorials. 2011;13(1):68-96.
  13. Kumar R, Taneja K, Taneja H. Performance Evaluation of MANET Using Multi-Channel MAC Framework. Procedia Comput Sci. 2018;133:755-762. doi:10.1016/j.procs.2018.07.122 .
  14. Kumar SS, Manimegalai P, Karthik S. An Energy - Competent Routing Protocol for MANETs: A Particle Swarm Optimization Approach. ICSNS 2018 - Proc IEEE Int Conf Soft-Computing Netw Secur. 2018:803-812. doi:10.1109/ICSNS.2018.8573677 .
  15. Kusyk J, Uyar MU, Sahin CS. Survey on evolutionary computation methods for cybersecurity of mobile ad hoc networks. Evol Intell. 2018;10(3-4):95-117. doi:10.1007/s12065-018-0154-4 .
  16. Liu L, Feng G. A novel ant colony based QoS-aware Routing algorithm for MANETs. Lect Notes Comput Sci. 2005;3612(PART III):457-466. doi:10.1007/11539902_56 .
  17. Liu Y, Huang J. A novel fast multi-objective evolutionary algorithm for qos multicast routing in manet. Int J Comput Intell Syst. 2009;2(3):288-297. doi:10.1080/18756891.2009.9727661 .
  18. Mamatha GS, Sharma SC. Analyzing The Manet Variations, Challenges, Capacity And Protocal Issues. Int J Comput Sci Eng Surv. 2010;1(1):14-21. doi:10.5121/ijcses.2010.1102 .
  19. Neeti Maan DRKP. Role of Artificial intelligence in MANET. WorldWideScience. 2012;1(4):102-104.
  20. Raghavendran C V., Satish GN, Varma PS. Intelligent Routing Techniques for Mobile Ad hoc Networks using Swarm Intelligence. Int J Intell Syst Appl. 2012;5(1):81-89. doi:10.5815/ijisa.2013.01.08 .
  21. Ramanathan R, Redi J. A brief overview of ad hoc networks: challenges and directions. IEEE Commun Mag. 2005;40(5):20-22. doi:10.1109/mcom.2002.1006968 .
  22. Reina DG, Ruiz P, Ciobanu R, Toral SL, Dorronsoro B, Dobre C. A Survey on the Application of Evolutionary Algorithms for Mobile Multihop Ad Hoc Network Optimization Problems. Int J Distrib Sens Networks. 2016;2016. doi:10.1155/2016/2082496 .
  23. Reina DG, Toral Marin SL, Bessis N, Barrero F, Asimakopoulou E. An evolutionary computation approach for optimizing connectivity in disaster response scenarios. Appl Soft Comput J. 2013;13(2):833-845. doi:10.1016/j.asoc.2012.10.024 .
  24. Revathi P. Quality of service routing in manet using a hybrid intelligent algorithm inspired by ant colony optimization. Int J Adv Sci Technol. 2020;29(3):4033-4046.
  25. Sapienza TJ. Optimizing quality of service of wireless mobile ad-hoc networks using evolutionary computation. CSIIRW’08 - 4th Annu Cyber Secur Inf Intell Res Work Dev Strateg to Meet Cyber Secur Inf Intell Challenges Ahead. 2008:1-5. doi:10.1145/1413140.1413182.
  26. Sen S. A Survey of Intrusion Detection Systems Using Evolutionary Computation. Elsevier Inc.; 2015. doi:10.1016/B978-0-12-801538-4.00004-5 .
  27. Sethi S, Udgata SK. The Efficient Ant Routing Protocol for MANET. Int J Comput Sci Eng. 2010;2(7):2414-2420.
  28. Sharma S, Agarwal R. Optimizing QoS parameters using computational intelligence in MANETS. Proceeding - IEEE Int Conf Comput Commun Autom ICCCA 2017. 2017;2017-Janua:708-715. doi:10.1109/CCAA.2017.8229893 .
  29. Shi J, Habib M, Yan H. A review paper on different application of genetic algorithm for mobile ad-hoc network (MANET). Int J online Biomed Eng. 2020;16(5):119-139. doi:10.3991/IJOE.V16I05.13325
  30. Siddique N AH. Computational Intelligence Synergies of Fuzzy Logic , 2015.
  31. Spears WM, Jong K, Bäck T, Fogel DB, Garis H. An Overview of Evolutionary Computation Introduction. Chinese J Adv Softw Res. 1996;3(1):1-19.
  32. Taneja K, Patel RB. Mobile Ad hoc Networks: Challenges and Future. Proceedings of National Conference on Challenges & Opportunities in Information Technology (COIT-2007). 2007 Mar 23; 133-135 .
  33. Taneja K, Taneja H, Kumar R. Multi-channel medium access control protocols: review and comparison. J Inf Optim Sci. 2018;39(1):239-247. doi:10.1080/02522667.2017.1372921
  34. Vishnu Balan E, Priyan M K, Gokulnath C, Usha G,Fuzzy Based Intrusion Detection Systems in MANET, Procedia Computer Science, Volume 50, 2015, Pages 109-114, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2015.04.071.
  35. Wedde HF, Farooq M, Pannenbaecker T, Vogel B, Mueller C, Meth J, Jeruschkat R (2005) Beeadhoc: an energy efficient routing algorithm for mobile ad hoc networks inspired by bee behavior. In: GECCO ’05: Proceedings of the 2005 conference on genetic and evolutionary computation, ACM, New York, NY, USA, pp 153–160.