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Utilizing Genetic-Based Heuristic Approach to Optimize QOS in Networks

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Applications of Soft Computing for the Web

Abstract

Routing is regarded as the process that transfers packets from a given source to a given destination with minimum cost, so the routing algorithm has the most specific role of acquiring, organizing and distributing information regarding various network states. So, routing is considered as one of the most important issue for various wireless infrastructures-less networks as most of the Quality of Service (QOS) parameters are related to how efficiently and effectively routes are managed. The traditional approach like Dijkstra’s shortest path algorithm was not able to optimize the QOS issues along with finding the shortest/fittest path. So this chapter utilizes heuristic-based genetic approach to solve and optimize routing-related issues. The proposed and simulated algorithm finds the fittest path from source to destination and optimizes various QOS parameters like hop count, delay, throughput, etc. The proposed heuristic-based approach is compared with traditional Dijkstra’s approach through MATLAB simulator for further validation and affirmation of presented methodology.

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References

  1. Abdullah J et al (2008) GA based QOS route selection algorithm for mobile ad-hoc networks. In: Proceedings of IEEE conference on telecommunication technologies

    Google Scholar 

  2. Begumhan TD, Turgut R, Than VL (2003) Optimizing clustering algorithm in mobile adhoc networks using simulated annealing. IEEE

    Google Scholar 

  3. Bellavista P, Corradi A, Gianneli C (2011) A unifying perspective on context-aware evaluation and management of heterogeneous wireless connectivity. IEEE Commun Surv Tut 13:337–357

    Article  Google Scholar 

  4. Cizmar A, Papaj J, Dobos L (2012) Security and QOS integration model for MANET. Comput Inform 31:1025–1044

    Google Scholar 

  5. Cheng H (2010) Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad-hoc networks. Eng Appl Artif Intell, 806–819

    Google Scholar 

  6. Defrawy KE, Tsudik G (2011) ALARM: anonymous location-aided routing in suspicious MANET’s. IEEE Trans Mob Comput 10(9):1345–1358

    Article  Google Scholar 

  7. Fessi BA, BenAbdullah S, Hamdi M, Boudriga N (2009) A new genetic algorithm approach for intrusion response system in computer networks. IEEE Symp Comp Commun, 342–347

    Google Scholar 

  8. De Rango F, Socievole A (2011) Meta-heuristics techniques and swarm intelligence in mobile ad-hoc networks. In: Book on mobile ad-hoc network and applications, vol 11

    Google Scholar 

  9. Gabrielle A (2011) Simulation of a secure ad-hoc network. Norwegian University of Science and Technology, Department of Telematics

    Google Scholar 

  10. Ghazal MA, Sayed A, Kelash H (2007) Routing optimization using genetic algorithm in ad-hoc networks. In: IEEE international symposium on signal processing and information technology

    Google Scholar 

  11. Gunasekaran R et al (2009) An improved parallel genetic algorithm for path bandwidth calculation in TDMA based mobile ad-hoc networks. In: IEEE conference on advances in computing, control and telecommunications technologies

    Google Scholar 

  12. Jagadeesan AT, Duraiswamy K (2010) Cryptographic key generation from multiple biometric modalities: Fusing minutiae with Iris feature. Int J Comput Appl 2(6)

    Google Scholar 

  13. Jyotika K, Baregar AJ (2013) Security using image processing. Int J Manag Info Technol 5(2)

    Google Scholar 

  14. Krishna BA, Radha S, Reddy KCK (2007) Data security in ad-hoc networks using randomization of cryptographic algorithms. J Appl Sci, 4007–4012

    Google Scholar 

  15. Nandi B, Barman S, Paul S (2010) Genetic algorithm based optimization of clustering in ad-hoc networks. Int J Comput Sci Inf Secur 7(1)

    Google Scholar 

  16. Sanderson S, Erbetta J (2000) Authentication for secure environments based on Iris scanning technology. IEEE Colloq Vis Biomet

    Google Scholar 

  17. Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor, MI

    Google Scholar 

  18. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, MA

    MATH  Google Scholar 

  19. Engelbrecht AP (2002) Computational intelligence an introduction. Wiley, New York

    Google Scholar 

  20. Sanzgiri K, Laflamme D, Dahill B, Levine BN (2005) Authenticated routing for ad-hoc network. IEEE J Sel Areas Commun

    Google Scholar 

  21. Shannon RE (1989) Introduction to the art and science of simulation. In: Proceedings of 30th conference on winter simulation

    Google Scholar 

  22. Zafar S, Soni MK, Beg MMS (2015) An optimized genetic stowed approach to potent QOS in MANET. Proc Comput Sci 62:410–418. https://doi.org/10.1016/j.proc.08.434. ISSN: 1877-0509

  23. Zafar S, Soni MK (2014) Sustaining security in MANET: biometric stationed authentication protocol (BSAP) inculcating meta-heuristic genetic algorithm. Int J Mod Educ Comput Sci 9:28–35. Published Online September in MECS. http://www.mecs-press.org/,, https://doi.org/10.5815/ijmecs.2014.09.05. ISSN: 2075-0161(Print), ISSN: 2075-017X (Online) Impact Factor 0.669 (2015)

  24. Zafar S, Soni MK, Beg MMS (2014) Sustaining security: encircling wavelet quartered extrication algorithm for crypt-biometric perception. In: International conference on data mining and intelligent computing (ICDMIC). IEEE Conference Publications, pp 1–6. https://doi.org/10.1109/ICDMIC.2014.6954263

  25. Zafar S, Soni MK (2014) Trust based QOS protocol (TBQP) using meta-heuristic genetic algorithm for optimizing and securing MANET. IEEE Explore, pp 173–177. https://doi.org/10.1109/ICROIT.2014.6798315

  26. Prügel-Bennett A, Shapiro JL (1994) An analysis of genetic algorithms using statistical mechanics. Phys Rev Lett 72(9):1305–1309

    Article  Google Scholar 

  27. Zafar S, Soni MK (2014) A novel crypt-biometric perception algorithm to protract security in MANET. Int J Comput Netw Inf Secur 6(12):64–71. https://doi.org/10.5815/ijcnis.12.08. ISSN: 2074-9090 (Print). ISSN: 2074-9104 (Online), https://doi.org/10.5815/ijcnis. Published by: MECS Publisher, November 2014, Impact factor 0.726 (2015)

  28. Umadevi V, Chezhian R, Khan ZU (2012) Security requirements in mobile ad-hoc networks. Int J Adv Res Comput Commun 1(2)

    Google Scholar 

  29. Yen YS et al (2008) A genetic algorithm for energy-efficient based multicast routing on MANET. J Comput Commun, 2632–2641

    Google Scholar 

  30. Zarza L, Pegueroles J, Soriano M (2007) Interpretation of binary strings as security protocols for their evolution by means of genetic algorithms

    Google Scholar 

  31. Sherin Z, Soni MK (2014) Secure routing in MANET through crypt-biometric technique. In: Proceedings of the 3rd international conference on frontiers of intelligent computing: theory and applications (FICTA), pp 713–720

    Google Scholar 

  32. Zafar S, Soni MK, Beg MMS (2015) QOS optimization in networks through meta-heuristic quartered genetic approach. ICSCTI, IEEE

    Book  Google Scholar 

  33. Zafar S, Soni MK, Beg MMS (2016) Iris signature methodology for securing MANET. MR Int J Eng Technol 8(1)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, School of Engineering Sciences & Technology, Jamia Hamdard (Deemed to be University), New Delhi, India

    Sherin Zafar

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  1. Sherin Zafar
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Correspondence to Sherin Zafar .

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Editors and Affiliations

  1. Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India

    Rashid Ali

  2. Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India

    MM Sufyan Beg

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Zafar, S. (2017). Utilizing Genetic-Based Heuristic Approach to Optimize QOS in Networks. In: Ali, R., Beg, M. (eds) Applications of Soft Computing for the Web. Springer, Singapore. https://doi.org/10.1007/978-981-10-7098-3_14

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  • DOI: https://doi.org/10.1007/978-981-10-7098-3_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7097-6

  • Online ISBN: 978-981-10-7098-3

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