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Genetic Algorithm with Heuristic Mutation for Wireless Sensor Network Optimization

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Advances in Intelligent Networking and Collaborative Systems (INCoS 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 182))

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Abstract

Bio-inspired metaheuristics can be useful for the optimization of complex systems. Wireless sensor networks (WSNs) are massively distributed cyber-physical systems whose efficient operation requires appropriate design and control strategies. In certain contexts, like with randomly deployed WSNs, the physical network configuration can be affected only minimally, and optimal control strategies are crucial for optimizing network performance metrics like lifetime, coverage, and energy consumption. These metrics often conflict with each other, making network optimization a complex multi-objective problem. In this study, we introduce an improved version of a bi-objective genetic algorithm for the optimization of sensor network lifetime and target coverage. The new algorithm uses the generic evolutionary optimization framework together with a problem-specific heuristic mutation operator. We investigate the ability of the algorithm to find sensor schedules that extend network lifetime, and improve average target coverage while satisfying the minimum coverage requirement and show that the improved algorithm delivers better schedules than the original GA.

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References

  1. Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. Chapman & Hall/CRC, Boca Raton (2009)

    Book  MATH  Google Scholar 

  2. Cardei, M., Thai, M.T., Li, Y., Wu, W.: Energy-efficient target coverage in wireless sensor networks. In: 24th Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2005, Miami, FL, USA, 13–17 March 2005, pp. 1976–1984. IEEE (2005). https://doi.org/10.1109/INFCOM.2005.1498475

  3. Cardei, M., Wu, J.: Energy-efficient coverage problems in wireless ad-hoc sensor networks. Comput. Commun. 29(4), 413–420 (2006). https://doi.org/10.1016/j.comcom.2004.12.025

    Article  Google Scholar 

  4. Chen, J., Jia, J., Wen, Y., Zhao, D., Liu, J.: Modeling and extending lifetime of wireless sensor networks using genetic algorithm. In: Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 47–54 (2009)

    Google Scholar 

  5. Dua, A., Jastrząb, T., Czech, Z.J., Krömer, P.: A randomized algorithm for wireless sensor network lifetime optimization. In: Proceedings of the 18th ACM International Symposium on QoS and Security for Wireless and Mobile Networks, Q2SWinet 2022, pp. 87–93. Association for Computing Machinery, New York (2022). https://doi.org/10.1145/3551661.3561370

  6. Dua, A., Krömer, P., Czech, Z.J., Jastrząb, T.: A bi-objective genetic algorithm for wireless sensor network optimization. In: Barolli, L. (ed.) CISIS 2022. LNNS, vol. 497, pp. 147–159. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08812-4_15

    Chapter  Google Scholar 

  7. Gentili, M., Raiconi, A.: \(\alpha \)-coverage to extend network lifetime on wireless sensor networks. Optim. Lett. 7(1), 157–172 (2013). https://doi.org/10.1007/s11590-011-0405-0

    Article  MathSciNet  MATH  Google Scholar 

  8. Harizan, S., Kuila, P.: Coverage and connectivity aware energy efficient scheduling in target based wireless sensor networks: an improved genetic algorithm based approach. Wirel. Netw. 25(4), 1995–2011 (2019)

    Article  Google Scholar 

  9. Iqbal, M., Naeem, M., Anpalagan, A., Ahmed, A., Azam, M.: Wireless sensor network optimization: multi-objective paradigm. Sensors 15(7), 17572–17620 (2015). https://doi.org/10.3390/s150717572

    Article  Google Scholar 

  10. Jia, J., Chen, J., Chang, G., Tan, Z.: Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm. Comput. Math. Appl. 57(11–12), 1756–1766 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  11. Konak, A., Coit, D.W., Smith, A.E.: Multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Saf. 91(9), 992–1007 (2006). https://doi.org/10.1016/j.ress.2005.11.018. Special Issue - Genetic Algorithms and Reliability

  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). https://doi.org/10.1109/CEC.2007.4424930

  13. Manju: A meta-heuristic based approach with modified mutation operation for heterogeneous networks. Wirel. Pers. Commun. 122(2), 963–979 (2022)

    Google Scholar 

  14. 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 

  15. Raiconi, A., Gentili, M.: Exact and metaheuristic approaches to extend lifetime and maintain connectivity in wireless sensors networks. In: Pahl, J., Reiners, T., Voß, S. (eds.) INOC 2011. LNCS, vol. 6701, pp. 607–619. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21527-8_68

    Chapter  Google Scholar 

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

    MATH  Google Scholar 

  17. Sammut, C., Webb, G.I.: Encyclopedia of Machine Learning, 1st edn. Springer, Cham (2011)

    MATH  Google Scholar 

  18. Slijepcevic, S., Potkonjak, M.: Power efficient organization of wireless sensor networks. In: IEEE International Conference on Communications, ICC 2001, Helsinki, Finland, 11–14 June 2001, pp. 472–476. IEEE (2001). https://doi.org/10.1109/ICC.2001.936985

  19. Xu, Y., Jiao, W., Tian, M.: Energy-efficient connected-coverage scheme in wireless sensor networks. Sensors 20(21), 6127 (2020)

    Article  Google Scholar 

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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 Czech Science Foundation in the project “Constrained multi-objective Optimization Based on Problem Landscape Analysis” funded by the Czech Science Foundation (grant no. GF22-34873K) and in part by the grant of the Student Grant System no. SP2023/12, VSB - Technical University of Ostrava.

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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

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  1. Amit Dua
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  2. Pavel Krömer
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  3. Zbigniew J. Czech
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  4. Tomasz Jastrząb
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Correspondence to Pavel Krömer .

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Editors and Affiliations

  1. Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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Dua, A., Krömer, P., Czech, Z.J., Jastrząb, T. (2023). Genetic Algorithm with Heuristic Mutation for Wireless Sensor Network Optimization. In: Barolli, L. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-031-40971-4_17

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