Skip to main content
SpringerLink
Log in

A Bi-objective Genetic Algorithm for Wireless Sensor Network Optimization

  • Conference paper
  • First Online:
Complex, Intelligent and Software Intensive Systems (CISIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 497))

Abstract

When designing a wireless sensor network several performance metrics should be considered, e.g., network lifetime, target coverage, sensor energy consumption. As a rule, these metrics are in conflict with each other, which means that by optimizing some of them we worsen the others. Designing the network is therefore a problem of multi-objective optimization. In this work, we propose a bi-objective genetic algorithm that optimizes network lifetime and target coverage. We consider two variants of the algorithm, in which the fitness function comprises only the network lifetime, or where it includes both, the network lifetime and target coverage. This makes it possible to find a trade-off between these two objectives. In-depth experimental studies are carried out for both variants of the algorithm.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdulhalim, M.F., Attea, B.A.: Multi-layer genetic algorithm for maximum disjoint reliable set covers problem in wireless sensor networks. Wirel. Pers. Commun. 80(1), 203–227 (2015)

    Article  Google Scholar 

  2. Ahn, N., Park, S.: A new mathematical formulation and a heuristic for the maximum disjoint set covers problem to improve the lifetime of the wireless sensor network. Ad Hoc Sens. Wirel. Netw. 13(3–4), 209–225 (2011)

    Google Scholar 

  3. Attea, B.A., Khalil, E.A., Özdemir, S., Yildiz, O.: A multi-objective disjoint set covers for reliable lifetime maximization of wireless sensor networks. Wirel. Pers. Commun. 81(2), 819–838 (2015)

    Article  Google Scholar 

  4. Cardei, M., Du, D.: Improving wireless sensor network lifetime through power aware organization. Wirel. Netw. 11(3), 333–340 (2005)

    Article  Google Scholar 

  5. Cardei, M., Thai, M., Li, Y., Wu, W.: Energy-efficient target coverage in wireless sensor networks. In: Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 3, pp. 1976–1984 (2005)

    Google Scholar 

  6. Cardei, M., Wu, J.: Energy-efficient coverage problems in wireless ad-hoc sensor networks. Comput. Commun. 29(4), 413–420 (2006)

    Article  Google Scholar 

  7. Das, A.K., Das, S., Ghosh, A.: Ensemble feature selection using bi-objective genetic algorithm. Knowl. Based Syst. 123, 116–127 (2017)

    Article  Google Scholar 

  8. Elhoseny, M., Tharwat, A., Farouk, A., Hassanien, A.E.: K-coverage model based on genetic algorithm to extend WSN lifetime. IEEE Sens. Lett. 1(4), 1–4 (2017)

    Article  Google Scholar 

  9. Fei, Z., Li, B., Yang, S., Xing, C., Chen, H., Hanzo, L.: A survey of multi-objective optimization in wireless sensor networks: metrics, algorithms and open problems. IEEE Comm. Surv. Tutor. 19 (2016)

    Google Scholar 

  10. Hanh, N.T., Binh, H.T.T., Hoai, N.X., Palaniswami, M.S.: An efficient genetic algorithm for maximizing area coverage in wireless sensor networks. Inf. Sci. 488, 58–75 (2019)

    Article  MathSciNet  Google Scholar 

  11. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  12. Lai, C.C., Ting, C.K., Ko, R.S.: An effective genetic algorithm to improve wireless sensor network lifetime for large-scale surveillance applications. In: 2007 IEEE Congress on Evolutionary Computation, pp. 3531–3538 (2007)

    Google Scholar 

  13. Manju, Chand, S., Kumar, B.: Genetic algorithm-based meta-heuristic for target coverage problem. IET Wirel. Sens. Syst. 8(4), 170–175 (2017)

    Google Scholar 

  14. Mini, S., Udgata, S., Sabat, S.: A heuristic to maximize network lifetime for target coverage problem in wireless sensor networks. Ad Hoc Sens. Wirel. Netw. 13(3–4), 251–269 (2011)

    Google Scholar 

  15. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)

    MATH  Google Scholar 

  16. Moshref, M., Al-Sayyed, R., Al-Sharaeh, S.: Multi-objective optimization algorithms for wireless sensor networks: a comprehensive survey. J. Theor. Appl. Inf. Technol. 98, 2839–2871 (2020)

    Google Scholar 

  17. Nong, S.X., Yang, D.H., Yi, T.H.: Pareto-based bi-objective optimization method of sensor placement in structural health monitoring. Buildings 11(11) (2021)

    Google Scholar 

  18. van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworths, London (1979)

    MATH  Google Scholar 

  19. Sammut, C., Webb, G.I.: Encyclopedia of Machine Learning, 1st edn. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-30164-8

    Book  MATH  Google Scholar 

  20. Singh, A., Sharma, S., Singh, J.: Nature-inspired algorithms for wireless sensor networks: a comprehensive survey. Comput. Sci. Rev. 39, 100,342 (2021)

    Google Scholar 

  21. Tarnaris, K., Preka, I., Kandris, D., Alexandridis, A.: Coverage and k-coverage optimization in wireless sensor networks using computational intelligence methods: a comparative study. Electronics 9(4) (2020)

    Google Scholar 

  22. Tossa, F., Abdou, W., Ezin, E.C., Gouton, P.: Improving coverage area in sensor deployment using genetic algorithm. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12141, pp. 398–408. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50426-7_30

    Chapter  Google Scholar 

  23. Wang, Z.J., Zhan, Z.H., Zhang, J.: Solving the energy efficient coverage problem in wireless sensor networks: a distributed genetic algorithm approach with hierarchical fitness evaluation. Energies 11(12) (2018)

    Google Scholar 

  24. Xu, Y., Ding, O., Qu, R., Li, K.: Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization. Appl. Soft Comput. 68, 268–282 (2018)

    Article  Google Scholar 

  25. Zairi, S., Zouari, B., Niel, É., Dumitrescu, E.: Nodes self-scheduling approach for maximising wireless sensor network lifetime based on remaining energy. IET Wirel. Sens. Syst. 2(1), 52–62 (2012)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the following computing centres where the computation of the project was performed: Academic Computer Center in Gdańsk (TASK), and Wroclaw Centre for Networking and Supercomputing (WCSS). This work was also supported by the Ministry of Education, Youth and Sports of the Czech Republic in the project “Metaheuristics Framework for Multi-objective Combinatorial Optimization Problems (META MO-COP)”, reg. no. LTAIN19176, and in part by the SGS grants no. SP2022/11 and SP2022/77, VSB-TU Ostrava. Czech Republic.

Author information

Authors and Affiliations

  1. Department of Algorithmics and Software, Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland

    Amit Dua, Zbigniew J. Czech & Tomasz Jastrząb

  2. Department of Computer Science, VSB-Technical University of Ostrava, 17. listopadu 15, 70800, Ostrava, Czech Republic

    Pavel Krömer

Authors
  1. Amit Dua
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pavel Krömer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zbigniew J. Czech
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tomasz Jastrząb
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pavel Krömer .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dua, A., Krömer, P., Czech, Z.J., Jastrząb, T. (2022). A Bi-objective Genetic Algorithm for Wireless Sensor Network Optimization. In: Barolli, L. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2022. Lecture Notes in Networks and Systems, vol 497. Springer, Cham. https://doi.org/10.1007/978-3-031-08812-4_15

Download citation

Publish with us

Policies and ethics

Search

Navigation