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Enhancing Network lifetime and Throughput in Heterogeneous Wireless Sensor Networks

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Abstract

In the modern era, WSNs broadly used in many research areas. Mainly researchers are focusing on rising the network lifetime, throughput and decreasing the energy utilization to make the network more reliable, robust and more responsive for a longer period of time. In this paper, two key aspects are taken into account; (i) network lifetime (ii) throughput of the network. The proposed approach is based on multilevel heterogeneity inspired by SEP (Stable Election Protocol). First node dead in the network plays a vital role in network lifetime because if the first node dead after a long period then definitely network lifetime becomes better. To get better the network life time, the proposed approach is another effort to make the network more responsive. Proposed approach compared with NEECP (Novel Energy-Efficient clustering protocol), ICACO (Inter Cluster Ant Colony optimization) and DCHSM (Dynamic Cluster Head Selection Method) gives the improved outcome in conditions of Network lifetime and throughput. In addition to this, comparison with existing approaches is carried out by considering the research papers from the year 2000 to 2017for 16 approaches.

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References

  1. Ouchitachen, H., Hair, A., & Idrissi, N. (2017). Improved multi-objective weighted clustering algorithm in wireless sensor network. Egyptian Informatics Journal, 18(1), 45–54.

    Article  Google Scholar 

  2. Ren, J., Zhang, Y., Zhang, K., Liu, A., Chen, J., & Shen, X. S. (2016). Lifetime and energy hole evolution analysis in data-gathering wireless sensor networks. IEEE Transactions on Industrial Informatics, 12(2), 788–800.

    Article  Google Scholar 

  3. Long, J., Dong, M., Ota, K., & Liu, A. (2017). A Green TDMA Scheduling algorithm for prolonging lifetime in wireless sensor networks. IEEE Systems Journal, 11(2), 868–877.

    Article  Google Scholar 

  4. Nayak, P., & Devulapalli, A. (2016). A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sensors Journal, 16(1), 137–144.

    Article  Google Scholar 

  5. Pananjady, A., Bagaria, V. K., & Vaze, R. (2017). Optimally approximating the coverage lifetime of wireless sensor networks. IEEE/ACM Transactions on Networking, 25(1), 98–111.

    Article  Google Scholar 

  6. Zhou, F., Chen, Z., Guo, S., & Li, J. (2016). Maximizing lifetime of data-gathering trees with different aggregation modes in WSNs. IEEE Sensors Journal, 16(22), 8167–8177.

    Article  Google Scholar 

  7. Fei, Z., Li, B., Yang, S., Xing, C., Chen, H., & Hanzo, L. (2017). A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms, and open problems. IEEE Communications Surveys & Tutorials, 19(1), 550–586.

    Article  Google Scholar 

  8. Sun, Y., Dong, W., & Chen, Y. (2017). An improved routing algorithm based on ant colony optimization in wireless sensor networks. IEEE Communications Letters, 21(6), 1317–1320.

    Article  Google Scholar 

  9. Kumar, H., & Singh, P.K. (2017). Analyzing data aggregation in wireless sensor networks, In 4th international conference on computing for sustainable global development INDIACom, pp. 4024–4029.

  10. Kumar, H., Singh, P.K. (2017). Node energy based approach to improve network lifetime and throughput in wireless sensor networks. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3–6): 79–88.

    Google Scholar 

  11. Kumar, H., & Singh, P. K. (2018). Comparison and analysis on artificial intelligence based data aggregation techniques in wireless sensor networks. Procedia Computer Science, 132, 498–506.

    Article  Google Scholar 

  12. Kumar, H., & Singh, P. K. (2018). Power transmission analysis in wireless sensor networks using data aggregation techniques. International Journal of Information System Modeling and Design, 9(4), 49–66.

    Article  Google Scholar 

  13. Kumar, H., & Singh, P. K. (2019). Average energy analysis in wireless sensor networks using multitier architecture. International Journal of Performability Engineering, 15(4), 1199–1208.

    Google Scholar 

  14. Kumar, H., & Singh, P. K. (2020). Network lifetime and throughput analysis in wireless sensor networks using fuzzy logic. Recent Advances in Electrical and Electronic Engineering, 13(2), 227–235.

    Google Scholar 

  15. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000, January). Energy-efficient communication protocol for wireless microsensor networks. In System sciences, 2000. Proceedings of the 33rd annual Hawaii international conference (pp. 10-pp). IEEE.

  16. Handy, M. J., Haase, M., & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In 4th international workshop onmobile and wireless communications network, 2002. (pp. 368–372). IEEE.

  17. Younis, O., & Fahmy, S. (2004). HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.

    Article  Google Scholar 

  18. Smaragdakis, G., Matta, I., & Bestavros, A. (2004). SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. Boston University Computer Science Department, pp. 1–11.

  19. Qing, L., Zhu, Q., & Wang, M. (2006). Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Computer Communications, 29(12), 2230–2237.

    Article  Google Scholar 

  20. Kim, J. M., Park, S. H., Han, Y. J., & Chung, T. M. (2008, February). CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks. In 10th international conference on Advanced communication technology, 2008. ICACT 2008. (Vol. 1, pp. 654–659). IEEE.

  21. Taheri, H., Neamatollahi, P., Younis, O. M., Naghibzadeh, S., & Yaghmaee, M. H. (2012). An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic. Ad Hoc Networks, 10(7), 1469–1481.

    Article  Google Scholar 

  22. Dahnil, D. P., Singh, Y. P., & Ho, C. K. (2012). Topology-controlled adaptive clustering for uniformity and increased lifetime in wireless sensor networks. IET Wireless Sensor Systems, 2(4), 318–327.

    Article  Google Scholar 

  23. Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Applied Soft Computing, 13(4), 1741–1749.

    Article  Google Scholar 

  24. Salim, A., Osamy, W., & Khedr, A. M. (2014). IBLEACH: intra-balanced LEACH protocol for wireless sensor networks. Wireless Networks, 20(6), 1515–1525.

    Article  Google Scholar 

  25. Kim, J. Y., Sharma, T., Kumar, B., Tomar, G. S., Berry, K., & Lee, W. H. (2014). Intercluster ant colony optimization algorithm for wireless sensor network in dense environment. International Journal of Distributed Sensor Networks, 10(4), 457402.

    Article  Google Scholar 

  26. Tarhani, M., Kavian, Y. S., & Siavoshi, S. (2014). SEECH: Scalable energy efficient clustering hierarchy protocol in wireless sensor networks. IEEE Sensors Journal, 14(11), 3944–3954.

    Article  Google Scholar 

  27. Jia, D., Zhu, H., Zou, S., & Hu, P. (2016). Dynamic cluster head selection method for wireless sensor network. IEEE Sensors Journal, 16(8), 2746–2754.

    Article  Google Scholar 

  28. Balakrishnan, B., & Balachandran, S. (2017). FLECH: fuzzy logic based energy efficient clustering hierarchy for nonuniform wireless sensor networks. Wireless Communications and Mobile Computing, 2017(1), 1–13.

    Article  Google Scholar 

  29. Zhou, Y., Wang, N., & Xiang, W. (2017). Clustering hierarchy protocol in wireless sensor networks using an improved PSO algorithm. IEEE Access, 5, 2241–2253.

    Article  Google Scholar 

  30. Latha, A., Prasanna, S., Hemalatha, S., & Sivakumar, B. (2019). A harmonized trust assisted energy efficient data aggregation scheme for distributed sensor networks. Cognitive Systems Research, 56, 14–22.

    Article  Google Scholar 

  31. Dattatraya, K. N., & Rao, K. R. (2019). Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN. Journal of King Saud University-Computer and Information Sciences.

  32. Dietrich, I., & Dressler, F. (2009). On the lifetime of wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 5(1), 1–38.

    Article  Google Scholar 

  33. Yildiz, H. U., Gungor, V. C., & Tavli, B. (2019). Packet size optimization for lifetime maximization in underwater acoustic sensor networks. IEEE Transactions on Industrial Informatics, 15(2), 719–729.

    Article  Google Scholar 

  34. Movva, P., & Rao, P. T. (2019). Novel two-fold data aggregation and MAC scheduling to support energy efficient routing in wireless sensor network. IEEE Access, 7, 1260–1274.

    Article  Google Scholar 

  35. Dutt, S., Agrawal, S., & Vig, R. (2019). Impact of variable packet length on the performance of heterogeneous multimedia wireless sensor networks. Wireless Personal Communications, 107(4), 1–15.

    Article  Google Scholar 

  36. Redhu, S., & Hegde, R. M. (2019). Network lifetime improvement using landmark-assisted mobile sink scheduling for cyber-physical system applications. Ad Hoc Networks, 87, 37–48.

    Article  Google Scholar 

  37. Saranraj, G., Selvamani, K., & Kanagachidambaresan, G. R. Optimal Energy-Efficient Cluster Head Selection (OEECHS) for Wireless Sensor Network. Journal of The Institution of Engineers (India): Series B, 100(4), 1–8.

  38. Sharma, D., Ojha, A., & Bhondekar, A. P. (2018). Heterogeneity consideration in wireless sensor networks routing algorithms: a review. The Journal of Supercomputing, 75(5), 1–54.

    Google Scholar 

  39. Tabatabaei, S., Rajaei, A., & Rigi, A. M. (2019). Wireless Personal Communications. https://doi.org/10.1007/s11277-019-06497-6

    Article  Google Scholar 

  40. Nawrocki, P., & Sniezynski, B. (2020). Adaptive context-aware energy optimization for services on mobile devices with use of machine learning. Wireless Personal Communications, 115(3), 1839–1867.

    Article  Google Scholar 

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Correspondence to Hradesh Kumar.

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Kumar, H., Singh, P.K. Enhancing Network lifetime and Throughput in Heterogeneous Wireless Sensor Networks. Wireless Pers Commun 120, 2971–2989 (2021). https://doi.org/10.1007/s11277-021-08594-x

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