A multi QoS genetic-based adaptive routing in wireless mesh networks with pareto solutions

Автор: Ibraheem Kasim Ibraheem, Alyaa Abdul-Hussain Al-Hussainy

Журнал: International Journal of Computer Network and Information Security @ijcnis

Статья в выпуске: 9 vol.10, 2018 года.

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Wireless Mesh Networks(WMN) is an active research topic for wireless networks designers and researchers. Routing has been studied in the last two decades in the field of optimization due to various applications in WMN. In this paper, Adaptive Genetic Algorithm (AGA) for identifying the shortest path in WMN satisfying multi- QoS measure is introduced. The proposed algorithm is adaptive in the sense that it uses various selection methods during the reproduction process and the one with the best multi- QoS measure is adopted in that generation. The multi-objective QoS measure defined as the combination of the minimum number of hops, minimum delay, and maximum bandwidth. The multi-objective optimization has been formulated and solved using weighted sum approach with Pareto optimal solution techniques. The simulation experiments have been carried out in MATLAB environment with a wireless network modeled as weighted graph of fifty nodes and node coverage equals to 200 meter, and the outcomes demonstrated that the proposed AGA performs well and finds the shortest route of the WMN proficiently, rapidly, and adapts to the dynamic nature of the wireless network and satisfying all of the constraints and objective measures imposed on the networks.

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Quality-of-Service (QoS), wireless routing, end-to-end delay, network bandwidth, wireless mesh networks, number of hops

Короткий адрес: https://sciup.org/15015629

IDR: 15015629   |   DOI: 10.5815/ijcnis.2018.09.01

Список литературы A multi QoS genetic-based adaptive routing in wireless mesh networks with pareto solutions

  • H. Yetgin, K. T. K. Cheung, and L. Hanzo, “Multi-objective routing optimization using evolutionary algorithms,” Wireless Communications and Networking Conference (WCNC), IEEE, Shanghai, China, pp. 3030 - 3034, April 2012.
  • P. K. G. T. Subarno Banerjee, Rajarshi Poddar, “A real-time framework of multiobjective genetic algorithm for Routing in Mobile Networks Subarno,” ACEEE Int. J. Netw. Secur., vol. 4, no. 1, pp. 11–15, 2013.
  • Y. Donoso, R. Fabregat, and J. L. Marzo, “A multi-objective optimization scheme for multicast routing: A multitree approach,” Telecommun. Syst., vol. 27, no. 2–4, pp. 229–251, 2004.
  • E. G. N. Chase, M. Rademacher, “A Benchmark Study of Multi-Objective Optimization Methods,” BMK-3021, Rev 6, pp. 1-24, 2009.
  • J. H. Ryu, S. Kim, and H. Wan, “Pareto front approximation with adaptive weighted sum method in multiobjective simulation optimization,” Proceedings - Winter Simulation Conference (WSC), Austin, TX, USA, pp. 623–633, Dec. 2009.
  • M. Camelo, C. Omaña, and H. Castro, “QoS routing algorithms based on multi-objective optimization for mesh networks,” IEEE Lat. Am. Trans., vol. 9, no. 5, pp. 875–881, 2011.
  • S. Rezaei and A. M. A. Hemmatyar, “General study of jitter mechanisms for metric-based wireless routing protocols,” AEU - International Journal of Electronics and Communications, vol. 79, pp. 132–140, 2017.
  • M. Sepulcre, J. Gozalvez, and B. Coll-Perales, “Multipath QoS-driven routing protocol for industrial wireless networks,” J. Netw. Comput. Appl., vol. 74, pp. 121–132, 2016.
  • M. Boushaba, A. Hafid, and M. Gendreau, “Node stability-based routing in Wireless Mesh Networks,” J. Netw. Comput. Appl., vol. 93, pp. 1–12, 2017.
  • J. Wang, Y. Miao, P. Zhou, M. S. Hossain, and S. M. M. Rahman, “A software defined network routing in wireless multihop network,” J. Netw. Comput. Appl., vol. 85, pp. 76–83, 2017.
  • T. S. Yuan Chai, Wenxiao Shi, “Load-aware cooperative hybrid routing protocol in hybrid wireless mesh networks,” AEU - Int. J. Electron. Commun., vol. 74, pp. 135-144, 2017.
  • Y. Rao and R. Wang, “Performance of QoS routing using genetic algorithm for Polar-orbit LEO satellite networks,” AEU - Int. J. Electron. Commun., vol. 65, no. 6, pp. 530–538, 2011.
  • A. Barolli, E. Spaho, L. Barolli, F. Xhafa, and M. Takizawa, “QoS routing in ad-hoc networks using GA and multi-objective optimization,” in Mobile Information Systems, 2011, vol. 7, no. 3, pp. 169–188.
  • G. Najafi and S. J. Gudakahriz, “A Stable Routing Protocol based on DSR Protocol for Mobile Ad Hoc Networks,” I.J. Wirel. Microw. Technol., vol. 3, pp. 14–22, 2018.
  • P. M. Saha, Himadri N., “Intelligent Energy Aware Fidelity Based On- Demand Secure Routing Protocol for MANET,” I. J. Comput. Netw. Inf. Secur., vol. 4, pp. 48–64.
  • S. K. Bandyopadhyay, Soham, “Improving the Performance of Fuzzy Minimum Spanning Tree based Routing Process through P- Node Fuzzy Multicasting Approach in MANET,” I. J. Comput. Netw. Inf. Secur., vol. 6, pp. 16–26, 2018.
  • S. K. Shelja Sharma, “Simulation Analysis of OLSR and Its Variant with Cooperative MPR Selection on NS-2.35 in Mobile Ad-Hoc Networks,” I. J. Comput. Netw. Inf. Secur. 2018, vol. 7, pp. 44–51, 2018.
  • K. P. V. S.R.M., Krishna, Seeta Ramanath M.N., “Optimal Reliable Routing Path Selection in MANET through Novel Approach in GA,” I.J. Intell. Syst. Appl., vol. 2, pp. 35–41, 2017.
  • M. B. and R. E. kouch Mohamed Ababou, “Energy Efficient Routing Protocol for Delay Tolerant Network Based on Fuzzy Logic and Ant Colony,” I.J. Intell. Syst. Appl., vol. 1, pp. 69–77, 2018.
  • P. K. M. Chandan, Radha Raman, Bindeshwar Singh Kushwaha, “Performance Evaluation of AODV, DSDV, OLSR Routing Protocols using NS-3 Simulator,” I. J. Comput. Netw. Inf. Secur., vol. 7, pp. 59–65, 2018.
  • S. M. S. Alwan Nuha A S, Ibraheem K. Ibraheem, “Fast Computation of the Shortest Path Problem through Simultaneous Forward and Backward Systolic Dynamic Programming,” Int. J. Comput. Appl., vol. 54, no. 1, pp. 21–25, 2012.
  • Shukr Sabreen M., N. A. S. Alwan, and Ibraheem Kasim Ibraheem, “The Multi-Constrained Dynamic Programming Problem in View of Routing Strategies in Wireless Mesh Networks,” Int. J. Inf. Commun. Technol. Res., vol. 2, no. 6, pp. 471–476, 2012.
  • Sabreen Mahmood Shukr, Nuha Abdul Sahib Alwan, Ibraheem Kasim Ibraheem, “A Comparative Study of Single-Constraint Routing in Wireless Mesh Networks Using Different Dynamic Programming Algorithms,” J. Eng., vol. 20, no. 2, pp. 49–60, 2014.
  • Ibraheem Kasim Ibraheem, Alyaa Abdul-Hussain Al-Hussainy, “Application of an Evolutionary Optimization Technique to Routing in Mobile Wireless Networks Application of an Evolutionary Optimization Technique to Routing in Mobile Wireless Networks,” Int. J. Comput. Appl., vol. 99, no. 7, pp. 24–31, 2014.
  • Z. Michalewicz, "Genetic Algorithms + Data Structures = Evolution Programs," Springer, Berlin, Heidelberg 1996.
  • M. Mitchell, “An introduction to genetic algorithms,” MIT press, 1998.
  • Ibraheem Kasim Ibraheem and Alyaa Abdul-Hussain Al-Hussainy, “Design of a Double-objective QoS Routing in Dynamic Wireless Networks using Evolutionary Adaptive Genetic Algorithm,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 4, no. 9, pp. 156–165, 2015.
  • T. Lu and J. Zhu, “Genetic algorithm for energy-efficient QoS multicast routing,” IEEE Commun. Lett., vol. 17, no. 1, pp. 31–34, 2013.
  • C. W. Ahn and R. S. Ramakrishna, “A genetic algorithm for shortest path routing problem and the sizing of populations,” IEEE Trans. Evol. Comput., vol. 6, no. 6, pp. 566–579, 2002.
  • A. Konak, D. W. Coit, and A. E. Smith, “Multi-objective optimization using genetic algorithms: A tutorial,” Reliab. Eng. Syst. Saf., vol. 91, no. 9, pp. 992–1007, 2006.
  • J. D. Schaffer, “Multiple objective optimization with vector evaluated genetic algorithms,” 1st Int. Conf. Genet. Algorithms, Pittsburgh, PA, USA, pp. 93–100, July 1985.
  • D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
  • Paul Rani A, M. Gomathy Nayagam, “Multiobjective Qos Optimazation Based On Multiple Workflow Scheduling In Cloud Environment,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 1, no. 2, 2013.
  • N. Srinivas and K. Deb, “Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms,” Evol. Comput., vol. 2, no. 3, pp. 221–248, 1994.
  • E. K. Burke and K. Graham, "Search methodologies: Introductory tutorials in optimization and decision support techniques", 2nd ed., Springer; 2014.
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