SpringerLink
Log in

CGARP: Chaos genetic algorithm-based relay node placement for multifaceted heterogeneous wireless sensor networks

  • S.I. : Multifaceted Intelligent Computing Systems (MICS)
  • Published:
Innovations in Systems and Software Engineering Aims and scope Submit manuscript

Abstract

Relay node deployment in wireless sensor network (WSN) is significantly explored in specialized literature. There is a wide spectrum of performance issues like connectivity, coverage, energy efficiency, latency of packet delivery that have been considered as the target criteria to be optimized with the deployment strategies of relay nodes. The dynamic variation of different attributes of the sensor nodes leverages a heterogeneous topology. None of the literature has considered a heterogeneity-aware relay node placement strategy to pacify the effect of structural diversity on network performance. In this work, we propose chaos genetic algorithm-based relay node placement (CGARP) which takes care of the structural heterogeneity of WSN. CGARP is based on chaos genetic algorithm (CGA), which overcomes the problem of premature convergence of genetic algorithm. Tent Map-based generation of the initial population and Logistic Map-based chaotic crossover enable CGARP to work well in WSN. The proposed technique has been compared with other relay node placement solutions available in the literature. CGARP achieves 68% improvement in average network lifetime in a 2D grid. It also results in 27% better connectivity and 23% improvement in the usage of relay nodes compared to EERP. The results are obtained through properly designed experiments with simulated realistic environments. These results substantiate the improvements achieved by the proposed approach.

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
EUR 32.99 /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
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Availability of data and material

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. All the data are generated based on the simulation of different parameters with theoretical constraints.

Code availability

The working environment for the current study has been designed in MATLAB by the authors. The codes are available with the corresponding author and can be shared with the respected Editors and Reviewers as and when required.

References

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

    Article  Google Scholar 

  2. Vieira MAM, Coelho CN, Da Silva DC, da Mata JM (2003) Survey on wireless sensor network devices. In: Proceedings of IEEE Conference on Emerging Technology Fact. Autom., ETFA03, pp 537–544

  3. Mohemed RE, Saleh AI, Abdelrazzak M, Samra AS (2017) Energy-efficient routing protocols for solving energy hole problem in wireless sensor networks. Comput Netw 114:51–66

    Article  Google Scholar 

  4. Chang J-H et al (2011) An efficient relay sensor placing algorithm for connectivity in wireless sensor networks. J Inf Sci Eng 27(1):381–392

    MathSciNet  Google Scholar 

  5. Djenouri D, Bagaa M (2017) Energy-aware constrained relay node deployment for sustainable wireless sensor networks. IEEE Trans Sustain Comput 2(1):30–42

    Article  Google Scholar 

  6. Ayinde BO, Barnawi AY (2014) Differential evolution based deployment of wireless sensor networks. In: 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA). IEEE

  7. Hashim HA, Bo A, Abido MA (2016) Optimal placement of relay nodes in wireless sensor network using artificial bee colony algorithm. J Netw Comput Appl 64:239–248

    Article  Google Scholar 

  8. Ayinde BO, Hashim HA (2018) Energy-efficient deployment of relay nodes in wireless sensor networks using evolutionary techniques. Int J Wireless Inf Networks 25(2):157–172

    Article  Google Scholar 

  9. Xu H, Huang L, Gang W, Zhang Y (2009) Spanner-aware relay node placement in wireless ad hoc sensor networks. In: Proceedings of the 5th International Conference on Mobile Ad-hoc and Sensor Networks (MSN), pp 180–186. https://doi.org/10.1109/MSN.2009.14.

  10. Han B, Li J, Su J (2013) Optimal relay node placement for multi-pair cooperative communication in wireless networks. In: Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), pp 4724–4729. https://doi.org/10.1109/WCNC.2013.6555340 .

  11. Misra S, Hong SD, Xue G, Tang J (2009) Constrained relay node placement in wireless sensor networks: formulation and approximations. IEEE/ACM Trans Netw 18(2):434–447

    Article  Google Scholar 

  12. Srivastava AK, Gupta SK (2019) EERP: energy-efficient relay node placement for k-connected wireless sensor networks using genetic algorithm. In: Hu YC, Tiwari S, Mishra K, Trivedi M (eds) Ambient communications and computer systems. Advances in intelligent systems and computing, vol 904. Springer, Singapore. https://doi.org/10.1007/978-981-13-5934-7_1

  13. Azharuddin MD, Jana PK (2015) A GA-based approach for fault-tolerant relay node placement in wireless sensor networks. In: Proceedings of the 2015 Third International Conference on Computer, Communication, Control, and Information Technology (C3IT). IEEE, pp 1–6

  14. Magán-Carrión R et al (2016) Optimal relay placement in multi-hop wireless networks. Ad Hoc Netw 46:23–36

    Article  Google Scholar 

  15. V. Mhatre, C. Rosenberg (2004) Homogeneous vs. heterogeneous clustered sensor networks: a comparative study. In: International Conference on Communication. IEEE, pp 3646–3651

  16. Javidi M, Hosseinpourfard R (2015) Chaos Genetic Algorithm Instead Genetic Algorithm. Int Arab J Inf Technol (IAJIT) 12(2):1

  17. Kim M, Leskovec J (2012) Multiplicative attribute graph model of real world networks. Internet Math 8(1–2):113–160

    Article  MathSciNet  Google Scholar 

  18. Hu H-B, Wang X-F (2008) Unified index to quantifying heterogeneity of complex networks. Physica A 387(14):3769–3780

    Article  Google Scholar 

  19. Estrada E (2010) Quantifying network heterogeneity. Phys Rev E 82:066102

  20. Rinku J, Harikrishnan KP, Mishra R, Ambika G et al (2017) Measure for degree hetero geneity in complex networks and its application to recurrence network analysis. R Soc Open Sci 4(1):1657

    Google Scholar 

  21. Hu X, Leydesdorff L, Rousseau R (2017) Heterogeneity in an undirected network: definition and measurement. J Informet 11(2):669–682

    Article  Google Scholar 

  22. Li X, Zhou S, Lian G, Chen G, Liu J (2018) Heterogeneity of social network based on degree sequence and community. Int J Mod Phys B 32(30):1850331

    Article  MathSciNet  Google Scholar 

  23. Hu H-B, Wang L (2005) The Gini coefficient’s application to general complex networks. Adv Complex Syst 8(01):159–167

    Article  Google Scholar 

  24. Jun Wu, Yue-** T, Hong-Zhong D, Da-Zhi Z (2007) Normalized entropy of rank distribution: a novel measure of the heterogeneity of complex networks. Chin Phys 16(6):1576

    Article  Google Scholar 

  25. Wu J, Tan Y-J, Deng H-Z, Zhu D-Z (2007) Heterogeneity of scale-free networks. Syst Eng-Theory Pract 27(5):101–105

    Article  Google Scholar 

  26. Estrada E, Vargas-Estrada E (2012) Distance-sum heterogeneity in graphs and complex networks. Appl Math Comput 218(21):10393–10405

    MathSciNet  MATH  Google Scholar 

  27. Safaei F, Tabrizchi S, Hadian Rasanan AH (2019) Zare M (2019) An energy-based heterogeneity measure for quantifying structural irregularity in complex networks. J Comput Sci 36:101011

    Article  MathSciNet  Google Scholar 

  28. Ok C, Lee S, Mitra P, Kumara S (2010) Distributed routing in wireless sensor networks using energy welfare metric. Inf Sci 180(9):1656–1670

    Article  Google Scholar 

  29. Wei L, Sun W, Chen H, Yuan P, Yin F, Luo Q, Chen L (2018) A fast neighbor discovery algorithm in WSNs. Sensors 18(10):3319

    Article  Google Scholar 

  30. Sarathi BP, Mandal SN, De D, Maiti B (2022) MAHI: Multiple Attribute Heterogeneity Index for Wireless Sensor Networks. In: Proceedings of international conference on advanced computing applications. Springer, pp 299–312

  31. Javidi M, Hosseinpourfard R (2015) Chaos Genetic Algorithm Instead Genetic Algorithm. Int Arab J Inf Technol (IAJIT) 12(2):1

    Google Scholar 

  32. Snaselova P, Zboril F (2015) Genetic algorithm using a theory of chaos. Procedia Comput Sci 51:316–325

    Article  Google Scholar 

  33. Dai Lu, Wang B, Yang LT, Deng X, Yi L (2019) A nature-the inspired node deployment strategy for connected confident information coverage in dustrial Internet of Things. IEEE Internet Things J 6(6):9217–9225

    Article  Google Scholar 

  34. Chowdhury A, De D (2020) MSLG-RGSO: Movement score based limited grid-mobility approach using reverse Glowworm Swarm Optimization algorithm for mobile wireless sensor networks. Ad Hoc Networks 106:102191

  35. Battiston F, Cencetti G, Iacopini I, Latora V, Lucas M, Patania A, Petri G (2020) Networks beyond pairwise interactions: structure and dynamics. Phys Rep

  36. Gao X, Li K, Chen B (2017) Invulnerability measure of a military heterogeneous network based on network structure entropy. IEEE Access 6:6700–6708

    Article  Google Scholar 

  37. Yadav SL, Sohal A (2017) Study of the various selection techniques in genetic algorithms. Int J Eng Sci Math 6(3):198–204

  38. Yadav SL, Sohal L (2017) Study of the various selection techniques in genetic algorithms. Int J Eng Sci Math 6(3):198–204

Download references

Acknowledgements

This work is an extended version of the paper entitled “MAHI: Multiple Attribute Heterogeneity Index for Wireless Sensor Networks,” accepted in the conference ICACA2021, AISC, Springer.

Funding

The authors did not receive any funds support from any organization for conducting the research mentioned in the submitted paper.

Author information

Authors and Affiliations

  1. Kalyani Government Engineering College, Nadia, 741235, India

    Partha Sarathi Banerjee & Satyendra Nath Mandal

  2. Maulana Abul Kalam Azad University of Technology, West Bengal, Haringhata, West Bengal, India

    Debashis De

  3. Maulana Azad College, Kolkata, India

    Biswajit Maiti

Authors
  1. Partha Sarathi Banerjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Satyendra Nath Mandal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Debashis De
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Biswajit Maiti
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.S.B.: Conceptualization, Methodology, Simulation, Investigation, Formal Analysis, Writing-Original Draft. S.N.M.: Data curation, Methodology, Investigation, Reviewing and Editing. D.D.: Conceptualization, Visualization, Supervision, Validation, Reviewing, and Editing. B.M.: Formal Analysis, Supervision, Reviewing and Editing.

Corresponding author

Correspondence to Partha Sarathi Banerjee.

Ethics declarations

Conflict of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Banerjee, P.S., Mandal, S.N., De, D. et al. CGARP: Chaos genetic algorithm-based relay node placement for multifaceted heterogeneous wireless sensor networks. Innovations Syst Softw Eng (2022). https://doi.org/10.1007/s11334-022-00439-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11334-022-00439-5

Keywords

Search

Navigation