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Energy Efficient Cluster Analysis for Heterogeneous Wireless Sensor Networks

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Abstract

The rising demands of Wireless Sensor Networks increase day by day because applications of sensors are increases by leap and bound. There are endless applications of WSNs in our daily lives in safety, environmental monitoring, healthcare to animal monitoring, etc. The sensor nodes are operating through the battery. Communication that takes place in the network required a certain amount of battery power. This paper has considered a heterogeneous network scenario, where sensor nodes are deployed and moving at a different speed. Our main objective is to find an energy-efficient cluster. The proposed protocol functions in two phases; phase-i, we classify nodes and form clusters based on the same node speed. In the second phase, the energy consumption of an individual node is computed as well as cluster-wise. Finally, clusters having minimum energy consumption are chosen as energy-efficient clusters. Our simulation has four clusters; result analysis shows that cluster -1 is efficient compared to others. We can conclude that node moving with lower speed consumes a lower amount of energy.

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References

  1. Anzola, J., Pascual, J., Tarazona, G., & González, R. (2018). A clustering WSN routing protocol based on k-d tree algorithm. Sensors (Switzerland), 18(9), 1–26. https://doi.org/10.3390/s18092899

    Article  Google Scholar 

  2. Behera, T. M., Mohapatra, S. K., Samal, U. C., & Khan, M. S. (2019). Hybrid heterogeneous routing scheme for improved network performance in WSNs for animal tracking. Internet of Things, 6, 100047. https://doi.org/10.1016/j.iot.2019.03.001

    Article  Google Scholar 

  3. Das, I., Shaw, R. N., & Das, S. (2020). Location based and multipath routing performance analysis for energy consumption in wireless sensor networks. In M. N. Favorskaya, S. Mekhilef, R. K. Pandey, & N. Singh (Eds.), Innovations in electrical and electronic engineering. Singapore: Springer. https://doi.org/10.1007/978-981-15-4692-1

    Chapter  Google Scholar 

  4. Dehghani, S., Pourzaferani, M., & Barekatain, B. (2015). Comparison on energy-efficient cluster based routing algorithms in wireless sensor network. Procedia Computer Science, 72, 535–542. https://doi.org/10.1016/j.procs.2015.12.161

    Article  Google Scholar 

  5. Dimitriou, G., Kikiras, P. K., Stamoulis, G. I., & Avaritsiotis, I. N. (2005). A tool for calculating energy consumption in wireless sensor networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3746 LNCS (pp. 611–621). https://doi.org/10.1007/11573036_58.

  6. Kandari, S. (2015). Design of Scenarios for Service Life Estimator Battery Model using QualNet 5.0. International Journal of Computer Networks and Wireless Communications, 5(4), 562–572.

    Google Scholar 

  7. Lakshmi, M., & Prashanth, C. R. (2018). Performance analysis of energy efficient cluster based heterogeneous wireless sensor network against malicious attack. In 3rd International conference on electrical, electronics, communication, computer technologies and optimization techniques, ICEECCOT 2018, (December) (pp. 241–248). https://doi.org/10.1109/ICEECCOT43722.2018.9001655.

  8. Lee, J. G., Chim, S., & Park, H. H. (2019). Energy-efficient cluster-head selection for wireless sensor networks using sampling-based spider monkey optimization. Sensors (Switzerland), 19(23), 5281. https://doi.org/10.3390/s19235281

    Article  Google Scholar 

  9. Marin-Perianu, R. S., Scholten, J., Havinga, P. J. M., & Hartel, P. H. (2008). Cluster-based service discovery for heterogeneous wireless sensor networks. International Journal of Parallel, Emergent and Distributed Systems, 23(4), 325–346. https://doi.org/10.1080/17445760801930948

    Article  MathSciNet  MATH  Google Scholar 

  10. Pandey, M., Vishwakarma, L. K., & Bhagat, A. (2018). An energy efficient clustering algorithm for increasing lifespan of heterogeneous wireless sensor networks. In Communications in computer and information science (Vol. 828). https://doi.org/10.1007/978-981-10-8660-1_20

  11. Purkar, S. V., & Deshpande, R. S. (2018). Energy efficient clustering protocol to enhance performance of heterogeneous wireless sensor network: EECPEP-HWSN. Journal of Computer Networks and Communications. https://doi.org/10.1155/2018/2078627

    Article  Google Scholar 

  12. Ramya, G., Nagarajan, R., & Kannadhasan, S. (2021). Energy efficient cluster based algorithm technique for wireless sensor networks. IOP Conference Series: Materials Science and Engineering, 1085(1), 012034. https://doi.org/10.1088/1757-899x/1085/1/012034

    Article  Google Scholar 

  13. Raval, A. S. (2015). Cluster head selection for in wireless sensor networks. International Journal of Computer Engineering and Sciences, 1(3), 6. https://doi.org/10.26472/ijces.v1i3.32

    Article  Google Scholar 

  14. Ray, N. K., & Turuk, A. K. (2016). A hybrid energy efficient protocol for mobile ad hoc networks. Journal of Computer Networks and Communications. https://doi.org/10.1155/2016/2861904

    Article  Google Scholar 

  15. Ren, Q., & Yao, G. (2020). An energy-efficient cluster head selection scheme for energy-harvesting wireless sensor networks. Sensors (Switzerland), 20(1), 1–17. https://doi.org/10.3390/s20010187

    Article  MathSciNet  Google Scholar 

  16. Rodríguez, A., Del-Valle-Soto, C., & Velázquez, R. (2020). Energy-efficient clustering routing protocol for wireless sensor networks based on yellow saddle goatfish algorithm. Mathematics, 8(9), 1515. https://doi.org/10.3390/math8091515

    Article  Google Scholar 

  17. Samundiswary, P., & Naik, M. R. K. (2013). Performance analysis of deterministic energy efficient clustering protocol for WSN. International Journal of Soft Computing and Engineering, 2(6), 431–435.

    Google Scholar 

  18. Singh, P., & Singh, R. (2019). Energy-efficient qos-aware intelligent hybrid clustered routing protocol for wireless sensor networks. Journal of Sensors. https://doi.org/10.1155/2019/8691878

    Article  Google Scholar 

  19. Thalore, R., Bhattacharya, P. P., & Jha, M. K. (2017). Performance comparison of homogeneous and heterogeneous 3D wireless sensor networks. Journal of Telecommunications and Information Technology, 2017(2), 32–37. https://doi.org/10.26636/jtit.2017.110216

    Article  Google Scholar 

  20. Thandapani, P., Arunachalam, M., & Sundarraj, D. (2020). An energy-efficient clustering and multipath routing for mobile wireless sensor network using game theory. International Journal of Communication Systems, 33(7), 1–18. https://doi.org/10.1002/dac.4336

    Article  Google Scholar 

  21. Torres, M. G. C. (2006). energy consumption in wireless sensor networks using GSP (University of Pittsburgh). https://doi.org/10.1109/ICW.2005.69

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Authors and Affiliations

  1. Department of Computer Science, Assam University, Silchar, India

    Indrani Das

  2. Department of Computer Science, Indira Gandhi National Tribal University, Regional Campus Manipur, Imphal, India

    Sanjoy Das

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  1. Indrani Das
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  2. Sanjoy Das
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Correspondence to Sanjoy Das.

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Das, I., Das, S. Energy Efficient Cluster Analysis for Heterogeneous Wireless Sensor Networks. Wireless Pers Commun 121, 337–352 (2021). https://doi.org/10.1007/s11277-021-08638-2

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