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An effective hybrid genetic algorithm and tabu search for maximizing network lifetime using coverage sets scheduling in wireless sensor networks

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Abstract

Two important issues in Wireless Sensor Networks (WSNs) are coverage and network lifetime. Network operation in an environment can be divided into two phases, first, making coverage sets and then scheduling them. The preliminary reviews have provided a number of solutions for problem-solving in the first phase, but there is no enough solution provided for the second one. Once the number of sensors in the environment increases, the number of coverage sets as well as the number of order sequences (in which the coverage sets can be scheduled) will also increase. The present study aims to detect a near-optimal scheduling for coverage sets to prolong the network lifetime. This problem was previously introduced as MCSS (Maximum Coverage Sets Scheduling) and it was proved to be an NP-hard problem. This paper presents two algorithms, a Genetic Algorithm (GA) and a Hybrid Algorithm (HA) integrating GA and Tabu Search (TS) to solve the MCSS problem. To reveal its efficiency, the algorithms were compared with a recently proposed algorithm in terms of scheduling the coverage sets. The simulations performed in this study showed that the proposed algorithms were more successful in finding near-optimal scheduling coverage sets.

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References

  1. Gulati K et al (2022) A review paper on wireless sensor network techniques in internet of things (IoT). Mater Today Proc 51:161–165. https://doi.org/10.1016/j.matpr.2021.05.067

    Article  Google Scholar 

  2. Mohammed SB et. al (2020) Wireless sensor network design methodologies: a Survey. In: Journal of Sensors. 2020, https://doi.org/10.1155/2020/9592836

  3. Mottaki NA et al (2021) Multi-objective optimization for coverage aware sensor node scheduling in directional sensor networks. In J Appl Dyn Syst Control 4(1):43–52

    Google Scholar 

  4. Sangwan A, Singh RP (2015) Survey on coverage problems in wireless sensor networks. Wirel Pers Commun 80(4):1475–1500

    Article  Google Scholar 

  5. Ajam L, Nodehi A, Mohamadi H (2021) A Genetic-based algorithm to solve priority-based target coverage problem in directional sensor networks. In: Journal of Applied Dynamic Systems and Control, 4(1):89-96

  6. Mottaki NA, Motameni H, Mohamadi H (2022) A genetic algorithm-based approach for solving the target Q-coverage problem in over and under provisioned directional sensor networks. Phys Commun 54:101719. https://doi.org/10.1016/j.phycom.2022.101719

    Article  Google Scholar 

  7. Hanh NT et al (2019) An efficient genetic algorithm for maximizing area coverage in wireless sensor networks. Inf Sci 488:58–75

    Article  MathSciNet  MATH  Google Scholar 

  8. Tao D, Wu T (2015) A survey on barrier coverage problem in directional sensor networks. In: IEEE Sensors Journal. 15(2):876-885

  9. Singh j, Kaur R, Singh D (2020) A survey and taxonomy on energy management schemes in wireless sensor networks. In: Journal of Systems Architecture 111:101782

  10. Boukerche A, Sun P (2018) Connectivity and coverage based protocols for wireless sensor networks. Ad Hoc Netw 80:54–69. https://doi.org/10.1016/j.adhoc.2018.07.003

    Article  Google Scholar 

  11. Luo C et al (2020) Maximizing network lifetime using coverage sets scheduling in wireless sensor networks. In: Ad Hoc Networks. 98:102037

  12. Chowdhury SM, Hossain A (2020) Different energy saving schemes in wireless sensor networks: a survey. Wirel Pers Commun 114:2043–2062. https://doi.org/10.1007/s11277-020-07461-5

    Article  Google Scholar 

  13. SPermutations (2022) ByDarrell whitley book evolutionary computation 1 Edition1st Edition first published2000 ImprintCRC Press Pages12 eBook ISBN9781315274638, https://doi.org/10.1016/j.jksuci.2019.05.006

  14. Zishan AA et al (2018) Maximizing heterogeneous coverage in over and under provisioned visual sensor networks. J Netw Comput Appl 124:44–62

    Article  Google Scholar 

  15. Kim Y et al (2013) Lifetime maximization considering target coverage and connectivity in directional image/video sensor networks. In: The Journal of Supercomputing. 65(1): 365-382

  16. Mohamadi H, Salleh S, Razali MN (2014) Heuristic methods to maximize network lifetime in directional sensor networks with adjustable sensing ranges. In: Journal of Network and Computer Applications. 26-35

  17. Alibeiki A, Motameni H, Mohamadi H (2019) A new genetic-based approach for maximizing network lifetime in directional sensor networks with adjustable sensing ranges. In: Pervasive and Mobile Computing. 52:1-12

  18. Balaji S et al (2020) Energy efficient target coverage for a wireless sensor network. Measurement 165:108167. https://doi.org/10.1016/j.measurement.2020.108167

    Article  Google Scholar 

  19. Arivudainambi D et al (2021) Cuckoo search algorithm for target coverage and sensor scheduling with adjustable sensing range in wireless sensor network. J Discret Math Sci Cryptogr. https://doi.org/10.1080/09720529.2020.1753301

    Article  MathSciNet  MATH  Google Scholar 

  20. El-Sherif M et al (2018) Lifetime maximisation of disjoint wireless sensor networks using multiobjective genetic algorithm. IET Wirel Sens Syst 8(5):200–207. https://doi.org/10.1049/iet-wss.2017.0069

    Article  Google Scholar 

  21. Mohamadi H, Salleh S, Ismail AS (2014) A learning automata-based solution to the priority-based target coverage problem in directional sensor networks. Wirel Pers Commun 79(3):2323–2338

    Article  Google Scholar 

  22. Gil JM, Han YH (2011) A target coverage scheduling scheme based on genetic algorithms in directional sensor networks. In: Sensors. 1888-1906

  23. Katti A (2019) Target coverage in random wireless sensor networks using cover sets. J King Saud Univ Comput Inf Sci 34:734–746

    Google Scholar 

  24. Katoch S et al (2021) A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80:8091–8126

    Article  Google Scholar 

  25. Hussain A et al (2017) Genetic algorithm for traveling salesman problem with modified cycle crossover operator. Comput Intell Neurosci 2017:1–7

    Article  Google Scholar 

  26. Garai G, Chaudhurii BB (2013) A novel hybrid genetic algorithm with Tabu search for optimizing multi-dimensional functions and point pattern recognition. Inf Sci 221:28–48

    Article  Google Scholar 

  27. Li X, Gao L (2016) An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. Int J Prod Econ 174:93–110

    Article  Google Scholar 

  28. Prajapati V, p et al Tabu Search Algorithm (TSA): A Comprehensive Survey. In:2020 3rd International Conference on emerging technologies in computer engineering: Machine Learning and Internet of Things (ICETCE), https://doi.org/10.1109/ICETCE48199.2020.9091743.

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Correspondence to Homayun Motameni.

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Mottaki, N.a., Motameni, H. & Mohamadi, H. An effective hybrid genetic algorithm and tabu search for maximizing network lifetime using coverage sets scheduling in wireless sensor networks. J Supercomput 79, 3277–3297 (2023). https://doi.org/10.1007/s11227-022-04710-1

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