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Studying the multiobjective variable neighbourhood search algorithm when solving the relay node placement problem in Wireless Sensor Networks

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Abstract

Nowadays, wireless sensor networks (WSNs) are considered in many fields of application. In this paper, we study how to efficiently deploy relay nodes into previously established static WSNs, with the purpose of optimising two relevant factors for the industry: average energy consumption of the sensors and average sensitivity area provided by the network. This is the so-called relay node placement problem, which is a known NP-hard optimisation problem in the literature. With the purpose of tackling this multiobjective (MO) optimisation problem, we consider two different approaches of the trajectory algorithm MO-VNS, assuming a wide range of stop conditions. Two additional standard genetic algorithms are included in this study, NSGA-II and SPEA2, which belong to evolutionary algorithms. The aim is to analyse the behaviour of MO-VNS compared to traditional methodologies. To this end, the four metaheuristics are applied to solve a freely available data set. The results obtained are analysed following a widely accepted statistical methodology and considering three MO quality metrics: hypervolume, set coverage, and attainment surface. After studying the results, we conclude that MO-VNS provides better performance than the standard algorithms NSGA-II and SPEA2. Moreover, we verify that the addition of relay nodes is a good way to optimise traditional WSNs.

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References

  • Akyildiz I, Su W, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun Mag 40:102–114

    Article  Google Scholar 

  • Back T (1996) Evolutionary algorithms in theory and practice: evolution strategies. In: Evolutionary programming. Genetic algorithms. Oxford University Press, Cambridge

  • Cardei M, Du DZ (2005) Improving wireless sensor network lifetime through power aware organization. Wirel Netw 11:333–340

    Article  Google Scholar 

  • Chang JH, Tassiulas L (2004) Maximum lifetime routing in wireless sensor networks. IEEE/ACM Trans Netw 12:609–619

    Article  Google Scholar 

  • Cheng X, Narahari B, Simha R, Cheng M, Liu D (2003) Strong minimum energy topology in wireless sensor networks: Np-completeness and heuristics. IEEE Trans Mobile Comput 2:248–256

    Article  Google Scholar 

  • Cormen TH, Leiserson CE, Rivest RL, Stein C (2009) Introduction to algorithms, 3rd edn. The MIT Press, Cambridge

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2000) A fast elitist multi-objective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6:182–197

    Article  Google Scholar 

  • Fonseca CM, Fleming PJ (1996) On the performance assessment and comparison of stochastic multiobjective optimizers. In: Proceedings of PPNS IV, pp 584–593

  • Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. W. H Freeman & Co, New York

    MATH  Google Scholar 

  • Geiger MJ (2008) Randomised variable neighbourhood search for multi objective optimisation. In: Proceedings of EU/ME workshop, pp 34–42. arXiv:0809.0271

  • Han X, Cao X, Lloyd EL, Shen CC (2010) Fault-tolerant relay node placement in heterogeneous wireless sensor networks. IEEE Trans Mobile Comput 9:643–656

    Article  Google Scholar 

  • Hays W, Winkler R (1970) Statistics: probability, inference, and decision. Holt, Rinehart and Winston, USA

  • Hou Y, Shi Y, Sherali H, Midkiff S (2005) On energy provisioning and relay node placement for wireless sensor networks. IEEE Trans Wirel Commun 4:2579–2590

    Article  Google Scholar 

  • Hu XM, Zhang J, Yu Y, Chung HH, Li YL, Shi YH, Luo XN (2010) Hybrid genetic algorithm using a forward encoding scheme for lifetime maximization of wireless sensor networks. IEEE Trans Evol Comput 14:766–781

    Article  Google Scholar 

  • Jia J, Chen J, Chang G, Tan Z (2009a) Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm. Comput Math Appl 57:1756–1766

    Article  MathSciNet  MATH  Google Scholar 

  • Jia J, Chen J, Chang G, Wen Y, Song J (2009b) Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius. Comput Math Appl 57:1767–1775

    Article  MathSciNet  MATH  Google Scholar 

  • Konstantinidis A, Yang K (2011) Multi-objective k-connected deployment and power assignment in wsns using a problem-specific constrained evolutionary algorithm based on decomposition. Comput Commun 34:83–98

    Article  Google Scholar 

  • Konstantinidis A, Yang K, Zhang Q (2008) An evolutionary algorithm to a multi-objective deployment and power assignment problem in wireless sensor networks. In: Proceedings of IEEE GLOBECOM, pp 1–6

  • Lanza-Gutierrez J, Gomez-Pulido J, Vega-Rodriguez M (2013a) A trajectory algorithm to solve the relay node placement problem in wireless sensor networks. In: Theory and practice of natural computing. Lecture notes in computer science, vol 8273. Springer, Berlin, pp 145–156

  • Lanza-Gutierrez JM, Gomez-Pulido JA (2011) Instance sets for optimization in wireless sensor networks. http://arco.unex.es/wsnopt

  • Lanza-Gutierrez JM, Gomez-Pulido JA, Vega-Rodriguez MA, Sanchez-Perez JM (2012) Relay node positioning in wireless sensor networks by means of evolutionary techniques. In: Autonomous and intelligent systems. Lecture notes in computer science, vol 7326. Springer, Berlin, pp 18–25

  • Lanza-Gutierrez JM, Gomez-Pulido JA, Vega-Rodriguez MA, Sanchez-Perez JM (2013b) A parallel evolutionary approach to solve the relay node placement problem in wireless sensor networks. In: Proceeding of GECCO, pp 1157–1164

  • Le Berre M, Hnaien F, Snoussi H (2011) Multi-objective optimization in wireless sensors networks. Proc ICM 1:1–4

    Google Scholar 

  • Lilliefors HW (1967) On the kolmogorov–smirnov test for normality with mean and variance unknown. J Am Stat Assoc 62:399–402

    Article  Google Scholar 

  • Liu L, Hu B, Li L (2010) Energy conservation algorithms for maintaining coverage and connectivity in wireless sensor networks. IET Commun 4:786–800

    Article  MathSciNet  Google Scholar 

  • Lloyd EL, Xue G (2007) Relay node placement in wireless sensor networks. IEEE Trans Comput 56:134–138

    Article  MathSciNet  Google Scholar 

  • Mahboubi H, Moezzi K, Aghdam A, Sayrafian-Pour K, Marbukh V (2014) Distributed deployment algorithms for improved coverage in a network of wireless mobile sensors. IEEE Trans Ind Inf 10:163–174

    Article  Google Scholar 

  • Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 1:50–60

    Article  MathSciNet  Google Scholar 

  • Martins F, Carrano E, Wanner E, Takahashi R, Mateus G (2011) A hybrid multiobjective evolutionary approach for improving the performance of wireless sensor networks. IEEE Sens J 11:545–554

    Article  Google Scholar 

  • Mini S, Udgata S, Sabat S (2014) Sensor deployment and scheduling for target coverage problem in wireless sensor networks. IEEE Sens J 14:636–644

    Article  Google Scholar 

  • Misra S, Majd N, Huang H (2011) Constrained relay node placement in energy harvesting wireless sensor networks. In: Proceedings of IEEE MASS 2011, vol 1, pp 25–34

  • Misra S, Majd NE, Huang H (2013) Approximation algorithms for constrained relay node placement in energy harvesting wireless sensor networks. In: IEEE Transactions on Computers, vol 99 (PrePrints)

  • Mukherjee JYB, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52:2292–2330

    Article  Google Scholar 

  • Nigam A, Agarwal YK (2014) Optimal relay node placement in delay constrained wireless sensor network design. Eur J Oper Res 233:220–233

    Article  MathSciNet  Google Scholar 

  • Peiravi A, Mashhadi HR, Hamed Javadi S (2013) An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm. Int J Commun Syst 26:114–126

    Article  Google Scholar 

  • Perez A, Labrador M, Wightman P (2011) A multiobjective approach to the relay placement problem in wsns. Proc IEEE WCNC 1:475–480

    Google Scholar 

  • ur Rehman A, Abbasi AZ, Islam N, Shaikh ZA (2014) A review of wireless sensors and networks: applications in agriculture. Comput Standards Interf 36(2):263–270

    Article  Google Scholar 

  • Sha K, Gehlot J, Greve R (2013) Multipath routing techniques in wireless sensor networks: a survey. Wirel Pers Commun 70:807–829

    Article  Google Scholar 

  • Shapiro SS, Wilk MB (1965) An analysis of variance test for normality (complete samples). Biometrika 52:591–611

  • Tang J, Hao B, Sen A (2006) Relay node placement in large scale wireless sensor networks. Comput Commun 29:490–501

    Article  Google Scholar 

  • Wang B (2011) Coverage problems in sensor networks: a survey. ACM Comput Surv 43:32:1–32:53

  • Wang Q, Xu K, Takahara G, Hassanein H (2007) Device placement for heterogeneous wireless sensor networks: minimum cost with lifetime constraints. IEEE Trans Wirel Commun 6:2444–2453

    Article  Google Scholar 

  • Xu K, Hassanein H, Takahara G, Wang Q (2010) Relay node deployment strategies in heterogeneous wireless sensor networks. IEEE Trans Mobile Comput 9:145–159

    Article  Google Scholar 

  • Ye W, Heidemann J, Estrin D (2002) An energy-efficient mac protocol for wireless sensor networks. Proc INFOCOM 3:1567–1576

    Google Scholar 

  • Yoon Y, Kim YH (2013) An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE Trans Cybern 43:1473–1483

    Article  Google Scholar 

  • Zhang H, Hou J (2005) Maintaining sensing coverage and connectivity in large sensor networks. Ad Hoc & Sensor, wireless networks 1

  • Zhao C, Chen P (2007) Particle swarm optimization for optimal deployment of relay nodes in hybrid sensor networks. Proc IEEE CEC 1:3316–3320

    Google Scholar 

  • Zitzler E (1999) Evolutionary algorithms for multiobjective optimization: methods and applications

  • Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evol Comput 3:257–271

    Article  Google Scholar 

  • Zitzler E, Laumanns M, Thiele L (2001) Spea 2: improving the strength pareto evolutionary algorithm Tech. rep. Computer Engineering and Networks Laboratory (TIK), ETH Zurich

    Google Scholar 

Download references

Acknowledgments

The authors thank the anonymous referees for comments and suggestions which have led to an improved version of this paper. This work was funded by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund, under the contract TIN2012-30685 (BIO project).

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Authors and Affiliations

  1. Department of Technologies of Computers and Communications, Polytechnic School, University of Extremadura, Campus Universitario s/n, 10003, Caceres, Spain

    Jose M. Lanza-Gutierrez & Juan A. Gomez-Pulido

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  1. Jose M. Lanza-Gutierrez
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  2. Juan A. Gomez-Pulido
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Correspondence to Jose M. Lanza-Gutierrez.

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Communicated by C. M. Vide.

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Lanza-Gutierrez, J.M., Gomez-Pulido, J.A. Studying the multiobjective variable neighbourhood search algorithm when solving the relay node placement problem in Wireless Sensor Networks. Soft Comput 20, 67–86 (2016). https://doi.org/10.1007/s00500-015-1670-0

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