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APSO-MVS: an adaptive particle swarm optimization incorporating multiple velocity strategies for optimal leader selection in hybrid MANETs

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Abstract

In this paper, we propose a hierarchical topological-based auto-configuration scheme for MANETs providing global internet connectivity among leader and member nodes to reduce the control overhead. The proposed scheme has performed the duplication address detection (DAD) operation through selecting a pre-configured node called coordinator node by a new joining cluster node. Hence, the overhead is reduced by the elimination of DAD messages broadcasting in the whole network. Also, the clustering problem in MANETs is solved by introducing a new adaptive particle swarm optimization with multiple velocity strategy (APSO-MVS) algorithm for a new leader selection with the frequent departure and failure of a leader node. However, to enhance the robustness and global searching ability of classical PSO, the three new velocity updating strategies are used in a newly developed APSO-MVS algorithm. This proposed APSO-MVS algorithm has considered multiple node metrics (node distance from the cluster group centre, node speed and node density) for the selection of an optimal leader node. Simulation results have proved the efficacy of proposed protocol in overhead reduction compared to other existing auto-configuration protocols and in terms of 15 benchmark test functions.

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References

  • Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin, pp 1–165

    Book  Google Scholar 

  • Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795

    Article  Google Scholar 

  • Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435

    Article  Google Scholar 

  • Abualigah LM, Khader AT, Hanandeh ES (2018a) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071

    Article  Google Scholar 

  • Abualigah LM, Khader AT, Hanandeh ES (2018b) A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125

    Article  Google Scholar 

  • Al-Mistarihi MF, Al-Shurman M, Qudaimat A (2011) Tree based dynamic address autoconfiguration in mobile ad hoc networks. Comput Netw 55(8):1894–1908

    Article  Google Scholar 

  • Arumugam MS, Rao MVC (2018) On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems. Appl Soft Comput 8(1):324–336

    Article  Google Scholar 

  • Bernardos C, Calderón M, Moustafa H (2005) Survey of IP address autoconfiguration mechanisms for MANETs. IETF, draft-bernardosmanetautoconf-survey-05. txt (work-in-progress). June, 2010

  • Broch J, Maltz DA, Johnson DB, Hu Y-C, Jetcheva J (1998) A performance comparison of multi-hop wireless ad hoc network routing protocols. In: Proceedings of the 4th annual ACM/IEEE international conference on mobile computing and networking, pp 85–97

  • Chakeres ID, Belding-Royer EM (2002) The utility of hello messages for determining link connectivity. In: The 5th international symposium on wireless personal multimedia communications, vol 2. IEEE, pp 504–508

  • Cheng R, Bai Y, Zhao Y, Tan X, Xu T (2019) Improved fireworks algorithm with information exchange for function optimization. Knowl-Based Syst 163:82–90

    Article  Google Scholar 

  • Fernandes NC, Moreira MDD, Duarte OCMB (2013) An efficient and robust addressing protocol for node autoconfiguration in ad hoc networks. IEEE/ACM Trans Netw 21(3):845–856

    Article  Google Scholar 

  • Grajzer M, Żernicki T, Głabowski M (2013) ND ++-an extended IPv6 Neighbor Discovery protocol for enhanced stateless address autoconfiguration in MANETs. Int J Commun Syst. https://doi.org/10.1002/dac.2472

    Article  Google Scholar 

  • He S, Wu QH, Wen JY, Saunders JR, Paton RC (2004) A particle swarm optimizer with passive congregation. Biosystems 78(1–3):135–147

    Article  Google Scholar 

  • Hu X, Eberhart R (2002) Multiobjective optimization using dynamic neighborhood particle swarm optimization. In: Proceedings of the 2002 congress on evolutionary computation. CEC’02 (Cat. No. 02TH8600), vol 2. IEEE, pp 1677–1681

  • Hussain SR, Saha S, Rahman A (2011) SAAMAN: scalable address autoconfiguration in mobile ad hoc networks. J Netw Syst Manage 19(3):394–426

    Article  Google Scholar 

  • Jobin J, Krishnamurthy SV, Tripathi SK (2004) A scheme for the assignment of unique addresses to support self-organization in wireless sensor networks. In IEEE 60th vehicular technology conference, 2004. VTC2004-Fall. 2004, vol 6. IEEE, pp 4578–4582

  • Johnson DB (2000) The dynamic source routing protocol for mobile ad hoc networks. IETF Internet Draft. http://www.ietf.org/internetdrafts/draft-ietf-manet-dsr04.txt. Accessed 18 Apr 2020

  • Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol 3. IEEE, pp 1931–1938

  • Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the 2002 congress on evolutionary computation. CEC’02 (Cat. No. 02TH8600), vol 2. IEEE, pp 1671–1676

  • Kim SH, Ha M, Kim D (2018) A multi-hop pointer forwarding scheme for efficient location update in low-rate wireless mesh networks. J Parallel Distrib Comput 122:109–121

    Article  Google Scholar 

  • Liang Q (2003) Clusterhead election for mobile ad hoc wireless network. In: 14th IEEE proceedings on personal, indoor and mobile radio communications, 2003. PIMRC 2003, vol 2. IEEE, pp 1623–1628

  • Makasarwala HA, Hazari P (2016) Using genetic algorithm for load balancing in cloud computing. In: 2016 8th international conference on electronics, computers and artificial intelligence (ECAI). IEEE, pp 1–6

  • Narten T (1999) Neighbor discovery and stateless autoconfiguration in IPv6. IEEE Internet Comput 3(4):54–62

    Article  Google Scholar 

  • Narten T, Nordmark E, Simpson W, Soliman H (1998) Neighbor discovery for IP version 6 (IPv6), pp 769–773

  • Niu B, Zhu Y, Hu K, Li S, He X (2006) A novel particle swarm optimizer using optimal foraging theory. In: International conference on intelligent computing. Springer, Berlin, pp 61–71

  • Perkins CE (2001) Ad hoc networking, vol 1. Addison-Wesley, Reading

    Google Scholar 

  • Perkins CE, Royer EM, Das SR, Marina MK (2001) Performance comparison of two on-demand routing protocols for ad hoc networks. IEEE Pers Commun 8(1):16–28

    Article  Google Scholar 

  • Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255

    Article  Google Scholar 

  • Samuel RA, Punithavathani DS (2019) Designing a new scalable autoconfiguration protocol with optimal header selection for large scale MANETs. J Circuits Syst Comput 2050068

  • Sivavakeesar S, Pavlou G, Liotta A (2004) Stable clustering through mobility prediction for large-scale multihop intelligent ad hoc networks. In: 2004 IEEE wireless communications and networking conference (IEEE Cat. No. 04TH8733), vol 3. IEEE, pp 1488–1493

  • Sundararaj V (2016) An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst 9(3):117–126

    Google Scholar 

  • Sundararaj V (2019a) Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wirel Pers Commun 104(1):173–197

    Article  Google Scholar 

  • Sundararaj V (2019b) Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. Int J Biomed Eng Technol 31(4):325

    Article  Google Scholar 

  • Umlauft M, Reichl P (2007) Experiences with the ns-2 network simulator-explicitly setting seeds considered harmful. In: 2007 wireless telecommunications symposium. IEEE, pp 1–5

  • Villalba G, Javier L, Matesanz JG, Orozco ALS, Díaz JDM (2011) Distributed dynamic host configuration protocol (D2HCP). Sensors 11(4):4438–4461

    Article  Google Scholar 

  • Villalba LJG, Orozco ALS, Matesanz JG, Kim T-H (2014) E-D2HCP: enhanced distributed dynamic host configuration protocol. Computing 96(9):777–791

    Article  Google Scholar 

  • Vinu S, Muthukumar S, Kumar RS (2018) An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput Secur 77:277–288

    Article  Google Scholar 

  • Wang X, Qian H (2014) A tree-based address configuration for a MANET. Pervasive and Mobile Computing 12:123–137

    Article  Google Scholar 

  • Wang T, Wang G (2010) TIBCRPH: traffic infrastructure based cluster routing protocol with handoff in VANET. In: The 19th annual wireless and optical communications conference (WOCC 2010). IEEE, pp 1–5

  • Weniger K, Zitterbart M (2002) IPv6 autoconfiguration in large scale mobile ad-hoc networks. Proc Eur Wirel 1:142–148

    Google Scholar 

  • Xie X-F, Zhang W-J, Yang Z-L (2002) Hybrid particle swarm optimizer with mass extinction. In: IEEE 2002 international conference on communications, circuits and systems and West Sino expositions, vol 2. IEEE, pp 1170–1173

  • Yagoubi B, Meddeber M (2010) Distributed load balancing model for grid computing. Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées, INRIA, vol 12, pp 43–60

  • Zhong F, Subramani S (2005) An address autoconfiguration protocol for IPv6 hosts in a mobile ad hoc network. Comput Commun 28(4):339–350

    Article  Google Scholar 

  • Zhu X, Young D, Watson BJ, Wang Z, Rolia J, Singhal S, Mckee B, Hyser C, Gmach D, Gardner R, Christian T (2009) 1000 islands: an integrated approach to resource management for virtualized data centers. Cluster Comput 12(1):45–57

    Article  Google Scholar 

  • Zuo X, Zhang G, Tan W (2013) Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud. IEEE Trans Autom Sci Eng 11(2):564–573

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Velammal Engineering College, Chennai, India

    J. Sathya Priya

  2. Department of Electrical and Electronics Engineering, KCG College of Technology, Chennai, India

    M. A. Femina

  3. Faculty of Information and Communication Engineering, Osmania University, Hyderabad, India

    R. A. Samuel

Authors
  1. J. Sathya Priya
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  2. M. A. Femina
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  3. R. A. Samuel
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Correspondence to J. Sathya Priya or R. A. Samuel.

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Priya, J.S., Femina, M.A. & Samuel, R.A. APSO-MVS: an adaptive particle swarm optimization incorporating multiple velocity strategies for optimal leader selection in hybrid MANETs. Soft Comput 24, 18349–18365 (2020). https://doi.org/10.1007/s00500-020-05034-z

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