Skip to main content
Springer Nature Link
Log in

Reliable and energy efficient topology control in probabilistic Wireless Sensor Networks via multi-objective optimization

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In Wireless Sensor Networks (WSNs) instead of using the possible network connectivity to its maximum extent, a deliberate choice must be made to restrict the topology of the network. Constructing a virtual backbone network using Connected Dominating Sets (CDS) is a promising choice for topology control. Currently, almost all existing studies employ heuristic and/or meta-heuristic optimizations for formulating minimum-sized CDS under the deterministic network model. In this paper, we address the problem of constructing energy efficient CDS in WSNs while improving network reliability. The problem is modelled as a multi-objective optimization that simultaneously maximizes two contradictory parameters: reliability and energy efficiency. Unlike most of the existing studies, the reliability parameter is expressed as a probabilistic inference using probabilistic network model due to uncertainty in connections among sensor nodes. Extensive simulation results indicate that the proposed approach in this paper achieves more reliability, longer stability period and more energy efficient CDS compared to other approaches in the literature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52(12):2292–2330

    Google Scholar 

  2. Puccinelli D, Haenggi M (2005) Wireless sensor networks: applications and challenges of ubiquitous sensing. IEEE Circuits Syst Mag 5(3):19–29

    Article  Google Scholar 

  3. Akyildiz F, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422

    Article  Google Scholar 

  4. Arampatzis T, Lygeros J, Manesis S (2005) A survey of applications of wireless sensors and wireless sensor networks. In: Proceedings of the IEEE International Symposium on Intelligent Control, Mediterrean Conference on Control and Automation, pp 19–724

  5. Rawat P, Singh KD, Chaouchi H, Bonnin JM (2014) Wireless sensor networks: a survey on recent developments and potential synergies. J Supercomput 68(1):1–48

    Article  Google Scholar 

  6. Papadimitriou, Katsaros D, Manolopoulos Y (2010) Topology control algorithms for wireless sensor networks: a critical survey. In: Proceedings of the International Conference on Computer Systems and Technologies (CompSys Tech), pp 1–10

  7. Santi P (2005) Topology control in wireless ad hoc and sensor networks. ACM Comput Surv 37(2):164–194

    Article  MathSciNet  Google Scholar 

  8. Ozdemir S, Attea BA, Khalil OA (2013) Multi-objective evolutionary algorithm based on decomposition for energy efficient coverage in wireless sensor networks. Wirel Pers Commun 71(1):195–215

    Article  Google Scholar 

  9. Khalil EA, Ozdemir S (2015) CDS based reliable topology control in WSNs. In: Networks, Proceedings of the International Symposium on IEEE Computers and Communications (ISNCC), pp 1–5, 13–15

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

    MATH  Google Scholar 

  11. Min M, Du H, Jia X, Huang CX, Huang SC-H, Wu W (2006) Improving construction for connected dominating set with Steiner tree in wireless sensor networks. J Global Optim 35(1):111–119

    Article  MathSciNet  MATH  Google Scholar 

  12. He JS (2012) Connected dominating set based topology control in wireless sensor networks. Computer Science Dissertations, Georgia State University

  13. Ephremides A, Wieselthier JE, Baker DE (1987) A design concept for reliable mobile radio networks with frequency hopping signaling. Proc IEEE 75(1):56–73

    Article  Google Scholar 

  14. Khalil EA, Ozdemir S (2015) Prolonging stability period of CDS based WSNs. In: Wireless Communications and Mobile Computing Conference (IWCMC), pp 776–781

  15. Cerpa A, Wong J, Kuang L, Potkonjak M, Estrin D (2005) Statistical model of lossy links in wireless sensor networks, In: IPSN 2005. Los Angeles, CA

  16. Hedetniemi ST, Laskar RC (1990) Bibliography on domination in graphs and some basic definitions of domination parameters. Discrete Math 86(1–3):257–277

    Article  MathSciNet  MATH  Google Scholar 

  17. Berge C (1962) Theory of graph and its applications. Methuen, London

    MATH  Google Scholar 

  18. Haynes TW, Hedetniemi ST, Slater PJ (1998) Fundamentals of domination in graphs. Marcel Dekker Inc., New York

    MATH  Google Scholar 

  19. Yuanyuan, Z, Jia X, Yanxiang H (2006) Energy efficient distributed connected dominating sets construction in wireless sensor networks. In: Proceedings of the ACM International Conference on Communications and Mobile Computing, pp 797–802

  20. Guha S, Khuller S (1998) Approximation algorithms for connected domimating sets. Algorithmica 20:374–387

    Article  MathSciNet  MATH  Google Scholar 

  21. Butenko S, Cheng X, Oliveira C, Pardalos PM (2004) A new heuristic for the minimum connected dominating set problem on ad hoc wireless networks. Recent developments in cooperative control and optimization. Kluwer Academic Publishers, Berlin, pp 61–73

  22. Lu G, Zhou MT, Tang Y, Zhao MY, Niu XZ, She K (2009) Approximation algorithms for the connected dominating set problem in unit disk graphs. J Electron Sci Technol China 3(7):214–222

    Google Scholar 

  23. Fu D, Han L, Liu L, Gao Q, Feng Z (2015) An efficient centralized algorithm for connected dominating set on wireless networks. In: The 12th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2015), vol 56, pp 162–167

  24. Das B, Sivakumar R, Bharghavan V (1997) Routing in ad hoc networks using a spine. In: Proc. Int. Conf. Comput. and Commun. Networks. Las Vegas, NV

  25. Bharghavan V, Das B (1997) Routing in ad hoc networks using minimum connected domination sets. In: Proc. Int. Conf. Commun. Montreal, Canada

  26. Alzoubi KM, Wan P-J, Frieder O (2002) Message-optimal connected dominating sets in mobile ad hoc networks. MOBIHOC, EPFL, Lausanne

    Book  Google Scholar 

  27. Funke S, Kesselman A, Meyer U, Segal M (2006) A simple improved distributed algorithm for minimum CDS in unit disk graphs. ACM Trans Sensor Netw 2(3):444–453

    Article  Google Scholar 

  28. Rai M, Verma S, Tapaswi S (2009) A power aware minimum connected dominating set for wireless sensor networks. J Netw 4(6):511–519

    Google Scholar 

  29. Hussain S, Shafique MI, Yang LT (2010) Constructing a CDS-based network backbone for energy efficiency in industrial wireless sensor. Proceedings of the 12th IEEE International Conference on High Performance Computing and Communications (HPCC’10). Melbourne, Australia, pp 322–328

  30. Zhang J, Xu L, Zhou S-M, Wu W, Ye X (2015) An efficient connected dominating set algorithm in WSNs based on the induced tree of the crossed cube. Int J Appl Math Comput Sci 25(2):295–309

    Article  MathSciNet  MATH  Google Scholar 

  31. He J, Cai Z, Ji S, Reyah R, Pan Y (2011) A genetic algorithm for constructing a reliable MCDS in probabilistic wireless networks. WASA

  32. Jovanovic R, Tuba M (2013) Ant colony optimization algorithm with pheromone correction strategy for minimum connected dominating set problem. Comput Sci Inf Syst (ComSIS) 10(1):133–149

    Article  Google Scholar 

  33. He J, Ji S, Beyah R, Xie Y, li Y (2015) Constructing load-balanced virtual backbones in probabilistic wireless sensor networks via multi-objective genetic algorithm. Trans Emerg Tel Tech 26(2):147–163

  34. Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy efficient communication protocol for wireless microsensor networks. In: 33rd Annual Hawaii International Conference on System Sciences, pp 3005–3014

  35. Attea BA, Khalil EA, Zdemir S (2014) Biologically inspired probabilistic coverage for mobile sensor networks. Soft Comput 18(11):2313–2322

    Article  Google Scholar 

  36. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  37. Coello CAC, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems, 2nd edn. Springer, New York

    MATH  Google Scholar 

  38. Srinivas N, Deb K (1994) Multi-objective function optimization using non-dominated sorting genetic algorithms. Evol Comput 2(3):221–248

    Article  Google Scholar 

  39. Deb K, Pratap A, Agarwal S, Meyarivan T (2000) A fast an elitist multi-objective genetic algorithm: NSGA-II. In: Proceedings Parallel Problem Solving from Nature VI, pp 849–858

  40. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester

    MATH  Google Scholar 

  41. Zhang Q, Li H (2007) MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Article  Google Scholar 

  42. Zitzler E (1999) Evolutionary algorithms for multi-objective optimization: methods and applications, Ph.D. dissertation, Zurich, Switzerland: Swiss Federal Institute of Technology

  43. Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279

    Article  Google Scholar 

  44. Fonseca CM, Fleming PJ (1993) Genetic Algorithms for Multiobjective Optimization: Formulation, discussion and generalization. In: Forrest S (ed) Proceedings of the Fifth International Conference on Genetic Algorithm. California, San Mateo, pp 416–423

    Google Scholar 

  45. Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multiobjective optimization. IEEE Conf Evol Comput 1:82–87

    Google Scholar 

  46. Gong M, Jiao L, Du H, Bo L (2008) Multiobjective immune algorithm with non-dominated neighbor based selection. Evol Comput 16(2):225–255

    Article  Google Scholar 

  47. Shi C, Yan Z, Shi Z, Zhang L (2010) A fast multi-objective evolutionary algorithm based on a tree structure. Appl Soft Comput 10(2):468–480

    Article  Google Scholar 

  48. Moreno JJ, Ortega G, Filatovas E, , Martinez JA, Garzon EM (2016) Using low-power platforms for evolutionary multi-objective optimization algorithms. J Supercomput:1-14

  49. Li K, Deb K, Zhang Q, Kwong S (2015) An evolutionary many-objective optimization algorithm based on dominance and decomposition. IEEE Trans Evol Comput 19(5):694–716

    Article  Google Scholar 

  50. Elfes A (1987) Sonar-based real-world mapping and navigation. IEEE J Robot Autom 3(3):249–265

    Article  Google Scholar 

  51. The MathWorks, Inc. (2016) MATLAB Primer. https://www.mathworks.com/help/pdf_doc/matlab/getstart

  52. Messac A (2015) Optimization in practice with MATLAB: for engineering students and professionals. Cambridge University Press, Cambridge

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, Gazi University, 06570, Ankara, Turkey

    Enan A. Khalil & Suat Ozdemir

Authors
  1. Enan A. Khalil
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Suat Ozdemir
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Suat Ozdemir.

Additional information

This study is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under the Grant Number 113E328.

Appendix

Appendix

All the results are averaged over 10 networks in each case and best results are shown in bold in Tables 6, 7, 8, 9, 10 and 11.

Table 6 Average WSN reliability of 10 test instances for different communication ranges
Full size table
Table 7 Average WSN reliability of 10 test instances for different network scales
Full size table
Table 8 Average dissipated energy in 10 test instances for different communication ranges
Full size table
Table 9 Average dissipated energy in 10 test instances for different network scales
Full size table
Table 10 Average results for CDS size of 10 test instances for different communication ranges
Full size table
Table 11 Average results for CDS size of 10 test instances for different network scales
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khalil, E.A., Ozdemir, S. Reliable and energy efficient topology control in probabilistic Wireless Sensor Networks via multi-objective optimization. J Supercomput 73, 2632–2656 (2017). https://doi.org/10.1007/s11227-016-1946-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-016-1946-x

Keywords