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Energy optimized micro genetic algorithm based LEACH protocol for WSN

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Abstract

This article presents the design, analyses and implementation of the novel routing protocol for energy optimization based on LEACH for WSN. Network Lifetime is the major problem in various routing protocols used in WSN. In order to overcome that problem, our proposed routing protocol is developed, which is a combination of Micro Genetic algorithm with LEACH protocol. Our proposed µGA-LEACH protocol, strengthen the cluster head (CH) selection and also reduce the energy consumption of the network when compared to existing protocols. This paper shows the improvement of network lifetime and energy consumption with the optimal CH selection based on a micro genetic algorithm and also compared the results with an existing hierarchical routing protocol like LEACH, LEACH-C, LEACH GA and GADA LEACH routing protocol with various packet sizes, and initial energy.

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Data availability

The datasets analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Dong, D., Liao, X., Liu, K., Liu, Y., & Xu, W. (2012). Distributed coverage in wireless ad hoc and sensor networks by topological graph approaches. IEEE Transactions on Computers, 61(10), 1417–1428. https://doi.org/10.1109/TC.2011.149.

    Article  MathSciNet  MATH  Google Scholar 

  2. Demigha, O., Hidouci, W. K., & Ahmed, T. (2013). On energy efficiency in collaborative target tracking in wireless sensor network: A review. IEEE Communications Surveys and Tutorials, 15(3), 1210–1222. https://doi.org/10.1109/SURV.2012.042512.00030.

    Article  Google Scholar 

  3. Rawat, P., Singh, K. D., Chaouchi, H., & Bonnin, J. M. (2014). Wireless sensor networks: A survey on recent developments and potential synergies. Journal of Supercomputing, 68(1), 1–48. https://doi.org/10.1007/s11227-013-1021-953.

    Article  Google Scholar 

  4. Pei, H., Li, X., Soltani, S., Mutka, M. W., & Ning, X. (2013). The evolution of MAC protocols in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 15(1), 101–120. https://doi.org/10.1109/SURV.2012.040412.00105.

    Article  Google Scholar 

  5. Ibrahim, A., Han, Z., & Liu, K. (2008). Distributed energy-efficient cooperative routing in wireless networks. IEEE Transaction on Wireless Communications, 7(10), 3930–3941. https://doi.org/10.1109/T-WC.2008.070502.

    Article  Google Scholar 

  6. Basaran, C., & Kang, K. D. (2009). Quality of service in wireless sensor networks. In Guide to wireless sensor networks (pp. 305–321). https://doi.org/10.1007/978-1-84882-218-4_12.

  7. Bajaber, F., & Awan, I. (2014). An efficient cluster-based communication protocol for wireless sensor networks. Telecommunication Systems, 55(3), 387–401. https://doi.org/10.1007/s11235-013-9794-y.

    Article  Google Scholar 

  8. Mansourkiaie, F., & Ahmed, M. H. (2015). Cooperative routing in wireless networks: A comprehensive survey. IEEE Communications Surveys and Tutorials, 17(2), 604–626. https://doi.org/10.1109/COMST.2014.2386799.

    Article  Google Scholar 

  9. Han, G., Xu, H., Duong, T. Q., Jiang, J., & Hara, T. (2013). Localization algorithms of wireless sensor networks: A survey. Telecommunication Systems, 52(4), 2419–2436. https://doi.org/10.1007/s11235-011-9564-7.

    Article  Google Scholar 

  10. Abo-Zahhad, M., Amin, O., Farrag, M., & Ali, A. (2014). Survey on energy consumption models in wireless sensor networks. Open Transaction on Wireless Sensor Network, 1(1), 1–4.

    Google Scholar 

  11. Karahan, A., Erturk, I., Atmaca, S., & Cakici, S. (2014). Effects of transmit-based and receive-based slot allocation strategies on energy efficiency in WSN MACs. Ad Hoc Networks, 13, 404–413. https://doi.org/10.1016/j.adhoc.2013.09.001.

    Article  Google Scholar 

  12. Zhai, C., Liu, J., Zheng, L., Xu, H., & Chen, H. (2012). Maximise lifetime of wireless sensor networks via a distributed cooperative routing algorithm. Transaction of Emerging Telecommunication and Technology, 23(5), 414–428. https://doi.org/10.1002/ett.2498.

    Article  Google Scholar 

  13. Shaikh, F. K., & Zeadally, S. (2016). Energy harvesting in wireless sensor networks: A comprehensive review. Renewable and Sustainable Energy Reviews, 55, 1041–1054. https://doi.org/10.1016/j.rser.2015.11.010.

    Article  Google Scholar 

  14. Anastasi, G., Conti, M., Di Francesco, M., & Passarella, A. (2009). Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks, 7(3), 537–568. https://doi.org/10.1016/j.adhoc.2008.06.003.

    Article  Google Scholar 

  15. Jung, J. W., & Weitnauer, M. (2013). On using cooperative routing for lifetime optimization of multi-hop wireless sensor networks: Analysis and guidelines. IEEE Transaction on Communications, 61(8), 3413–3423. https://doi.org/10.1109/TCOMM.2013.052013.120707.

    Article  Google Scholar 

  16. Chidean, M. I., Morgado, E., Sanromán-Junquera, M., Ramiro-Bargueno, J., Ramos, J., & Caamano, A. J. (2016). Energy efficiency and quality of data reconstruction through data-coupled clustering for self-organized large-scale WSNs. IEEE Sensors Journal, 12(16), 5010–5020. https://doi.org/10.1109/jsen.2016.2551466.

    Article  Google Scholar 

  17. Peiravi, A., Mashhadi, H. R., & Javadi, S. H. (2013). An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm. International Journal of Communication Systems, 1(26), 114–126. https://doi.org/10.1002/dac.1336.

    Article  Google Scholar 

  18. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 12(52), 2292–2330. https://doi.org/10.1016/j.comnet.2008.04.002.

    Article  Google Scholar 

  19. Snajder, B., Jelicic, V., Kalafatic, Z., & Bilas, V. (2016). Wireless sensor node modelling for energy efficiency analysis in data intensive periodic monitoring. Ad Hoc Networks, 49, 29–41. https://doi.org/10.1016/j.adhoc.2016.06.004.

    Article  Google Scholar 

  20. Raza, U., Bogliolo, A., Freschi, V., Lattanzi, E., & Murphy, A. L. (2016). A two-prong approach to energy-efficient WSNs:Wake-up receivers plus dedicated, model-based sensing. Ad Hoc Networks, 45, 1–12. https://doi.org/10.1016/j.adhoc.2016.03.005.

    Article  Google Scholar 

  21. Al Karaki, J. N., & Kamal, A. E. (2004). Routing techniques in Wireless Sensor Networks: A survey. IEEE Wireless Communications. https://doi.org/10.1109/mwc.2004.1368893.

    Article  Google Scholar 

  22. Echoukairi, H., Bourgbu, K., & Ouzzif, M. (2016). A survey on flat routing protocols in wireless sensor networks. In The international symposium on ubiquitous networking, LNEE 366 (pp. 311–324). https://doi.org/10.1007/978-981-287-990-5_25.

  23. Kumar, A., Shwe, H. Y., Wong, K. J., & Chong, P. H. J. (2017). Location-based routing protocols for wireless sensor networks: A survey. Wireless Sensor Network, 9, 25–72. https://doi.org/10.4236/wsn.2017.91003.

    Article  Google Scholar 

  24. DaWei, X., & Jing, G. (2011). Comparison study to hierarchical routing protocols in wireless sensor networks. Procedia Environmental Sciences, 10, 595–600. https://doi.org/10.1016/j.proenv.2011.09.096.

    Article  Google Scholar 

  25. Jha, S. K., & Eyong, E. M. (2017). An energy optimization in wireless sensor networks by using genetic algorithm. Telecommunication System. https://doi.org/10.1007/s11235-017-0324-1.

    Article  Google Scholar 

  26. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). LEACH: Energy efficient communication protocol for wireless microsensor networks. In Proceedings of Hawai international conference on system science, Maui, Hawaii (pp. 3005–3014). https://doi.org/10.1109/hicss.2000.926982.

  27. Xu, J., Jin, N., Lou, X., Peng, T., Zhou, Q., & Chen, Y. (2012). Improvement of leach protocol for WSN. In IEEE sponsored 9th international conference on fuzzy systems and knowledge discovery (pp. 2174–2177). https://doi.org/10.1109/fskd.2012.6233907.

  28. Liu, J. L., & Ravishankar, C. V. (2011). LEACH-GA: Genetic algorithm-based energy efficient adaptive clustering protocol for wireless sensor networks. International Journal of Machine Learning and Computing, 1(1), 79–85. https://doi.org/10.7763/IJMLC.2011.V1.12.

    Article  Google Scholar 

  29. Sivakumar, P., & Radhika, M. (2017). Performance analysis of LEACH-GA over LEACH and LEACH-C in WSN. In Procedia computer science, ICSCC (pp. 248–256). https://doi.org/10.1016/j.procs.2017.12.034.

  30. Bhatia, T., Kansal, S., Goel, S., & Verma, A. K. (2016). A genetic algorithm based distance-aware routing protocol for wireless sensor networks. Computers and Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2016.09.016.

    Article  Google Scholar 

  31. Al-Baz, A., & El-Sayed, A. (2018). A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks. International Journal of Communication System, 31(1), 1–13. https://doi.org/10.1002/dac.3407.

    Article  Google Scholar 

  32. Anzola, J., Pascual, J., Tarazona, G., & Crespo, R. G. (2018). A clustering WSN routing protocol based on k–d tree algorithm. Sensors, 2899(18), 1–26. https://doi.org/10.3390/s18092899.

    Article  Google Scholar 

  33. Salim, A., Osamy, W., & Khedr, A. M. (2014). IBLEACH: Intra-balanced Leach protocol for wireless sensor networks. Wireless Network, 6(20), 1515–1525. https://doi.org/10.1007/s11276-014-0691-4.

    Article  Google Scholar 

  34. Naregal, K. (2012). Improved cluster routing protocol for wireless sensor network through simplification. In 18th annual international conference advanced computing and communications (pp. 1–3). https://doi.org/10.1109/adcom.2012.6563576.

  35. Miao, H., Xiao, X., Qi, B., & Wang, K. (2015). Improvement and application of LEACH protocol based on genetic algorithm for WSN, in computer aided modelling and design of communication links and networks (CAMAD). In IEEE 20th international workshop (pp. 242–245). https://doi.org/10.1109/camad.2015.7390517.

  36. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 4(1), 660–670. https://doi.org/10.1109/twc.2002.804190.

    Article  Google Scholar 

  37. Rao, P. C. S., Jana, P. K., & Banka, H. (2017). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Network, 23, 2005–2020. https://doi.org/10.1007/s11276-016-1270-7.

    Article  Google Scholar 

  38. Srinivasa Rao, P. C., Banka, H., & Jana, P. K. (2016). Energy efficient clustering for wireless sensor networks: A gravitational search algorithm. In B. Panigrahi, P. Suganthan, S. Das, & S. Satapathy (Eds.), Swarm, evolutionary, and memetic computing. SEMCCO 2015. Lecture Notes in Computer Science (Vol. 9873). Cham: Springer.

    Google Scholar 

  39. Srinivasa Rao, P. C., & Banka, H. (2017). Energy efficient clustering algorithms for wireless sensor networks: Novel chemical reaction optimization approach. Wireless Network, 23, 433–452. https://doi.org/10.1007/s11276-015-1156-0.

    Article  Google Scholar 

  40. Gambhir, S., & Parul (2016). OE-LEACH: An optimized energy efficient LEACH algorithm for WSNs. In Ninth international conference on contemporary computing (IC3), Noida (pp. 1–6). https://doi.org/10.1109/ic3.2016.7880225.

  41. Daanoune, Baghdad, A. & Balllouk, A. (2019). BRE-LEACH: A new approach to extend the lifetime of wireless sensor network. In Third international conference on intelligent computing in data sciences (ICDS), Marrakech, Morocco (pp. 1–6). https://doi.org/10.1109/icds47004.2019.8942253.

  42. Abushiba, W., Johnson, P., Alharthi, S., & Wright, C. (2017). An energy efficient and adaptive clustering for wireless sensor network (CH-leach) using Leach protocol. In 13th international computer engineering conference (ICENCO), Cairo (pp. 50–54). https://doi.org/10.1109/icenco.2017.8289762.

  43. Liu, Y., Wu, Q., Zhao, T., Tie, Y., Bai, F., & Jin, M. (2019). An improved energy-efficient routing protocol for wireless sensor networks. Sensors, 19(4579), 1–20.

    Google Scholar 

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Radhika, M., Sivakumar, P. Energy optimized micro genetic algorithm based LEACH protocol for WSN. Wireless Netw 27, 27–40 (2021). https://doi.org/10.1007/s11276-020-02435-8

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