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Energy-saving distributed monitoring-based firefly algorithm in wireless sensors networks

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Abstract

Coverage control represents an important research challenge during the design of wireless sensor networks (WSNs) in an energy-efficient way. It is an indicator used to assess network services performance. In order to provide network services quality guarantee, it is essential to ensure the network coverage with a minimum consumed energy to extend the lifespan of the network. In this paper, an Energy-saving Distributed Monitoring based Firefly Algorithm (EDiMoFA) Protocol in wireless sensor networks is proposed to ensure the coverage and to enhance the lifetime of WSNs. In the first phase, the sensing field is divided into smaller virtual regions using the concept divide-and conquer. The EDiMoFA protocol is distributed on every node in the resulted small regions in the second phase. The EDiMoFA protocol mixes three powerful approaches to work efficiently: virtual network division, dynamic distributed virtual region head selection in every region, wireless nodes scheduling-based Firefly Algorithm (FA) is performed by every chosen head of the virtual region. The EDiMoFA protocol is periodic. Every period is composed of two different phases: a steady-state phase and monitoring one. The network information exchange, virtual region head selection, and a wireless sensors scheduling optimization-based FA are achieved in the steady-state phase. In the monitoring phase, the best sensor devices schedule produced by the FA will take the responsibility for monitoring the sensing field in every virtual region. The produced sensors schedule ensures coverage at a low consumed energy cost. Simulation results, which are obtained using the OMNeT++ network simulator, prove that the EDiMoFA protocol can increase the wireless sensors’ lifetime and produces enhanced coverage control performances in comparison with some recent existing works in the literature. The EDiMoFA protocol has, respectively, prolonged the network lifetime from 3.2% up to 21.8%, from 10.4% up to 86.4%, from 35.2% up to 68.4%, and from 1.6% up to 6.7% in comparison with the DiLCO, DESK, GAF, and PeCO protocols while maintaining the suitable level of coverage for the sensing field of interest.

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References

  1. Alhussaini R, Idrees A K, Salman M A (2018) Data transmission protocol for reducing the energy consumption in wireless sensor networks. In: International Conference on New Trends in Information and Communications Technology Applications, pp 35–49. Springer, Berlin

  2. Harb H, Idrees AK, Jaber A, Makhoul A, Zahwe O, Taam MA (2017) Wireless sensor networks: a big data source in internet of things. Int J Sens Wirel Commun Control 7(2):93–109

    Google Scholar 

  3. Witwit AJH, Idrees AK (2018) A comprehensive review for rpl routing protocol in low power and lossy networks. In: International Conference on New Trends in Information and Communications Technology Applications. Springer, Berlin, pp 50–66

  4. Idrees AK, Harb H, Jaber A, Zahwe O, Taam MA (2017) Adaptive distributed energy-saving data gathering technique for wireless sensor networks. In: 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp 55–62. IEEE

  5. Idrees AK, Al-Yaseen WL, Taam MA, Zahwe O (2018) Distributed data aggregation based modified k-means technique for energy conservation in periodic wireless sensor networks. In: 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM). IEEE, pp 1–6

  6. Li Q, Liu N (2020) Monitoring area coverage optimization algorithm based on nodes perceptual mathematical model in wireless sensor networks. Comput Commun 155:227–234

    Article  Google Scholar 

  7. Idrees AK, Deschinkel K, Salomon M, Couturier R (2018) Multiround distributed lifetime coverage optimization protocol in wireless sensor networks. J Supercomput 74(5):1949–1972

    Article  Google Scholar 

  8. Gupta S, Gupta S (2020) Analysis and comparison of sensor node scheduling heuristic for wsn and energy harvesting wsn. In: Smart Systems and IoT: innovations in computing. Springer, Berlin, pp 131–139

  9. Kadhum IA, Karine D, Michel S, Raphael C (2015) Distributed lifetime coverage optimization protocol in wireless sensor networks. J Supercomput 71(12):4578–4593

    Article  Google Scholar 

  10. Idrees AK, Deschinkel K, Salomon M, Couturier R (2016) Perimeter-based coverage optimization to improve lifetime in wireless sensor networks. Eng Optim 48(11):1951–1972

    Article  Google Scholar 

  11. Xu Y, Heidemann J, Estrin D (2001) Geography-informed energy conservation for ad hoc routing. In: Proceedings of the 7th Annual International Conference on Mobile Computing and Networking, pp 70–84. ACM

  12. Chinh V, Gao S, Deshmukh W, Li Y (2006) Distributed energy-efficient scheduling approach for k-coverage in wireless sensor networks. MILCOM, pp 1–7

  13. Varga A (2003) Omnet++ discrete event simulation system. http://www.omnetpp.org

  14. Dowlatshahi MB, Rafsanjani MK, Gupta BB (2021) An energy aware grouping memetic algorithm to schedule the sensing activity in wsns-based iot for smart cities. Appl Soft Comput 107473

  15. Huang C-F, Tseng Y-C (2005) The coverage problem in a wireless sensor network. Mob Netw Appl 10(4):519–528

    Article  Google Scholar 

  16. Yue Y, You H, Wang S, Cao L (2021) Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks. Int J Distrib Sens Netw 17(5):15501477211018140

    Article  Google Scholar 

  17. Liang D, Shen H, Chen L (2021) Maximum target coverage problem in mobile wireless sensor networks. Sensors 21(1):184

    Article  Google Scholar 

  18. Ran Y, Huang X, Zhang Z, Du D-Z (2012) Approximation algorithm for minimum power partial multi-coverage in wireless sensor networks. J Glob Optim 1–17

  19. Manju PB, Kumar S (2020) Target k-coverage problem in wireless sensor networks. J Dis Math Sci Cryptogr 23(2):651–659

    Google Scholar 

  20. Chuanwen L, Yi H, Deying L, Yongcai W, Wenping C, Qian H (2020) Maximizing network lifetime using coverage sets scheduling in wireless sensor networks. Ad Hoc Netw 98:102037

    Article  Google Scholar 

  21. Prasan S, Hiren T, Hwang I et al (2017) Pre-scheduled and self organized sleep-scheduling algorithms for efficient k-coverage in wireless sensor networks. Sensors 17(12):2945

    Article  Google Scholar 

  22. Xu L, Rongjun C, Jun L, Lianglun C (2018) Square partition-based node scheduling algorithm for wireless passive sensor networks. Int J Commun Syst 31(8):e3531

    Article  Google Scholar 

  23. Mishra R, Tripathi RK, Sharma AK (2019) Design of probability density function targeting efficient coverage in wireless sensor networks. Wireless Pers Commun 105(1):61–85

    Article  Google Scholar 

  24. Artur M, Krzysztof T (2020) Maximization of the sensor network lifetime by activity schedule heuristic optimization. Ad Hoc Netw 96:101994

    Article  Google Scholar 

  25. Tchakonte DT, Simeu E, Tchuente M (2020) Lifetime optimization of wireless sensor networks with sleep mode energy consumption of sensor nodes. Wireless Netw 26(1):91–100

    Article  Google Scholar 

  26. Yue Y, Cao L, Luo Z (2019) Hybrid artificial bee colony algorithm for improving the coverage and connectivity of wireless sensor networks. Wireless Pers Commun 108(3):1719–1732

    Article  Google Scholar 

  27. Idrees AK, Deschinkel K, Salomon M, Couturier R (2014) Coverage and lifetime optimization in heterogeneous energy wireless sensor networks. ICN 2014, The Thirteenth International Conference on Networks, pp 49–54, Nice, France. IARIA

  28. Xin-She Y (2010) Nature-inspired metaheuristic algorithms. Luniver Press, United Kingdom

    MATH  Google Scholar 

  29. Pedraza Fernán, Medaglia Andrés L, Garcia Alfredo. (2006) Efficient coverage algorithms for wireless sensor networks. In: 2006 IEEE Systems and Information Engineering Design Symposium, pp 78–83. IEEE

  30. Ezugwu AE, Shukla AK, Nath R, Akinyelu AA, Agushaka JO, Chiroma H, Muhuri PK (2012) Metaheuristics: a comprehensive overview and classification along with bibliometric analysis. Artif Intell Rev 1–80

  31. Yang X-S (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms. Springer, Berlin, pp 169–178

  32. Łukasik S, Żak S. Firefly algorithm for continuous constrained optimization tasks. In: International Conference on Computational Collective Intelligence. Springer, Berlin, pp 97–106

  33. Korte B, Vygen J (2018) Combinatorial optimization: theory and algorithms, 6th edn. Springer, Berlin

    Book  Google Scholar 

  34. Daniel B, George N (2020) Integer programming and combinatorial optimization. Springer, Berlin

    Google Scholar 

  35. Chandrasekaran K, Simon Sishaj P, Prasad PN (2013) Binary real coded firefly algorithm for solving unit commitment problem. Inform Sci 249:67–84

    Article  Google Scholar 

  36. Li Y, Chinh V, Ai C, Chen G, Zhao Y (2011) Transforming complete coverage algorithms to partial coverage algorithms for wireless sensor networks. IEEE Trans Parallel Distrib Syst 22(4):695–703

    Article  Google Scholar 

Download references

Acknowledgements

This paper is funded by the EIPHI Graduate School (contract “ANR-17-EURE-0002”).

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Authors and Affiliations

  1. Department of Computer Science, University of Babylon, Babylon, Iraq

    Ali Kadhum Idrees

  2. FEMTO-ST Institute/CNRS, University of Bourgogne Franche-Comte, Belfort, France

    Raphael Couturier

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  1. Ali Kadhum Idrees
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  2. Raphael Couturier
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Correspondence to Raphael Couturier.

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Idrees, A.K., Couturier, R. Energy-saving distributed monitoring-based firefly algorithm in wireless sensors networks. J Supercomput 78, 2072–2097 (2022). https://doi.org/10.1007/s11227-021-03944-9

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