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Enhanced Cost and Sub-epoch Based Stable Energy-Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks

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Abstract

This paper presents an Enhanced Cost and Sub-Epoch based Stable Energy-Efficient Clustering (ECSSEEC) for Heterogeneous Wireless Sensor Networks. In ECSSEEC protocol, modeled cost function selects optimum number of cluster heads (CHs) and sub-epoch rotates previously selected CHs again as normal sensing nodes for future rounds as selected in CSSEEC. It also provides generalized radio model expression for different fading channels of HWSNs. Simulation results show that ECSSEEC achieves effective performance in network for stability period, usable period, and weak sensing period than previously existing hierarchical protocols (i.e., SEARCH, DEEC, SEP and LEACH) in Rayleigh fading environment for two level HWSNs.

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References

  1. Liu, A., Zhang, D., Zhang, P., Cui, G., & Chen, Z. (2014). On mitigating hotspots to maximize network lifetime in multi-hop wireless sensor network with guaranteed transport delay and reliability. Peer-to-Peer Networking and Applications, 7(3), 255–273.

    Article  Google Scholar 

  2. Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-efficient routing protocols in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 15(2), 551–591.

    Article  Google Scholar 

  3. Abbas, M. M., Muhammad, Z., Saleem, K., Saqib, N. A., & Mahmood, H. (2015). Energy harvesting and management in wireless networks for perpetual operations. Journal of Circuits Systems and Computers, 24(03), 1550041.

    Article  Google Scholar 

  4. Harb, H., & Makhoul, A. (2019). Energy-efficient scheduling strategies for minimizing big data collection in cluster-based sensor networks. Peer-to-Peer Networking and Applications, 12(3), 620–634.

    Article  Google Scholar 

  5. Gupta, I. K., Mishra, A. K., Diwan, T. D., & Srivastava, S. (2023). Unequal clustering scheme for hotspot mitigation in iot-enabled wireless sensor networks based on fire hawk optimization. Computers and Electrical Engineering, 107, 108615.

    Article  Google Scholar 

  6. Guo, W., & Zhang, W. (2014). A survey on intelligent routing protocols in wireless sensor networks. Journal of Network and Computer Applications, 38, 185–201.

    Article  Google Scholar 

  7. Venkateswarlu, K. M., Kandasamy, A., & Chandrasekaran, K. (2015). An energy-efficient hybrid clustering mechanism for wireless sensor network. Unmanned Systems, 3(02), 109–125.

    Article  Google Scholar 

  8. Xu, L., Collier, R., & O’Hare, G. M. (2017). A survey of clustering techniques in wsns and consideration of the challenges of applying such to 5g iot scenarios. IEEE Internet of Things Journal, 4(5), 1229–1249.

    Article  Google Scholar 

  9. Sharma, A., Agrawal, S., Bhatnagar, C., & Chauhan, D. (2020). Modelling and analysis of received signal strength-based emitter geolocation from single geostationary satellite with multiple antennas using stk toolkit. Australian Journal of Electrical and Electronics Engineering, 17(3), 188–195.

    Article  Google Scholar 

  10. Agrawal, S., Sharma, A., Bhatnagar, C., & Chauhan, D. Modelling and analysis of emitter geolocation using satellite tool kit. Defence Science Journal, 70(4).

  11. Ibtissem Zaatouri1, N. A., & Benfradj Guiloufi, Awatef. (2017). A comparative study of the energy efficient clustering protocols in heterogeneous and homogeneous wireless sensor networks. Wireless Personal Communications, 97(4), 6453–6468.

  12. Tanwar, S., Kumar, N., & Rodrigues, J. J. (2015). A systematic review on heterogeneous routing protocols for wireless sensor network. Journal of network and computer applications, 53, 39–56.

    Article  Google Scholar 

  13. Yu, Y., Krishnamachari, B., & Kumar, V. P. (2006). Information processing and routing in wireless sensor networks. Singapore: World Scientific.

    Book  Google Scholar 

  14. Liu, X. (2015). Atypical hierarchical routing protocols for wireless sensor networks: A review. IEEE Sensors Journal, 15(10), 5372–5383.

    Article  Google Scholar 

  15. Mohapatra, S., Behera, P. K., Sahoo, P. K., Ojha, M. K., Swarup, C., Singh, K. U., Pandey, S. K., Kumar, A., & Goswami, A. (2023). Modified ring routing protocol for mobile sinks in a dynamic sensor network in smart monitoring applications. Electronics, 12(2), 281.

    Article  Google Scholar 

  16. Afsar, M. M., & Tayarani-N, M.-H. (2014). Clustering in sensor networks: A literature survey. Journal of Network and Computer Applications, 46, 198–226.

    Article  Google Scholar 

  17. Krishna, P., Vaidya, N. H., Chatterjee, M., & Pradhan, D. K. (1997). A cluster-based approach for routing in dynamic networks. ACM SIGCOMM Computer Communication Review, 27(2), 49–64.

    Article  Google Scholar 

  18. McDonald, A. B., & Znati, T. F. (2001). Design and performance of a distributed dynamic clustering algorithm for ad-hoc networks, In Simulation symposium. Proceedings. 34th annual, IEEE, pp. 27–35.

  19. Mhatre, V., Rosenberg, C., Kofman, D., Mazumdar, R., & Shroff, N. (2004). Design of surveillance sensor grids with a lifetime constraint. In European workshop on wireless sensor networks, Springer, pp. 263–275.

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

  21. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

    Article  Google Scholar 

  22. Smaragdakis, G., Matta, I., & Bestavros, A. (2004). Sep: A stable election protocol for clustered heterogeneous wireless sensor networks. Tech. rep., Boston University Computer Science Department.

  23. 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 

  24. Masaeli, N., Javadi, H. H. S., & Noori, E. (2013). Optimistic selection of cluster heads based on facility location problem in cluster-based routing protocols. Wireless Personal Communications, 72(4), 2721–2740.

    Article  Google Scholar 

  25. Zhen, H., Li, Y., & ZHANG, G.-J. (2013). Efficient and dynamic clustering scheme for heterogeneous multi-level wireless sensor networks. Acta Automatica Sinica, 39(4), 454–460.

    Article  Google Scholar 

  26. Wang, M.-Y., Ding, J., Chen, W.-P., & Guan, W.-Q. (2015). Search: A stochastic election approach for heterogeneous wireless sensor networks. IEEE Communications Letters, 19(3), 443–446.

    Article  Google Scholar 

  27. Zeb, A., Islam, A. M., Zareei, M., Al Mamoon, I., Mansoor, N., Baharun, S., Katayama, Y., & Komaki, S. (2016). Clustering analysis in wireless sensor networks: the ambit of performance metrics and schemes taxonomy. International Journal of Distributed Sensor Networks, 12(7), 4979142.

    Article  Google Scholar 

  28. Mekonnen, M. T., & Rao, K. N. (2017). Cluster optimization based on metaheuristic algorithms in wireless sensor networks. Wireless Personal Communications, 97(2), 2633–2647.

    Article  Google Scholar 

  29. Elshrkawey, M., Elsherif, S. M., & Wahed, M. E. An enhancement approach for reducing the energy consumption in wireless sensor networks. Journal of King Saud University-Computer and Information Sciences.

  30. Arasu, K., & Ganesan, R. (2018). Effective implementation of energy aware routing for wireless sensor network. Materials Today: Proceedings, 5(1), 1186–1193.

    Google Scholar 

  31. Li, C., Bai, J., Gu, J., Yan, X., & Luo, Y. (2018). Clustering routing based on mixed integer programming for heterogeneous wireless sensor networks. Ad Hoc Networks, 72, 81–90.

    Article  Google Scholar 

  32. Ding, X.-X., Ling, M., Wang, Z.-J., & Song, F.-L. (2017). Dk-leach: An optimized cluster structure routing method based on leach in wireless sensor networks. Wireless Personal Communications, 96(4), 6369–6379.

    Article  Google Scholar 

  33. Verma, A., Rashid, T., Gautam, P. R., Kumar, S., & Kumar, A. (2019). Cost and sub-epoch based stable energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Wireless Personal Communications, 1–15.

  34. de Oliveira Brante, G. G., Kakitani, M. T., & Souza, R. D. (2011). Energy efficiency analysis of some cooperative and non-cooperative transmission schemes in wireless sensor networks. IEEE Transactions on Communications, 59(10), 2671–2677.

    Article  Google Scholar 

  35. Afsar, M. M., Tayarani-N, M.-H., & goldsmith, A. (2014). Wireless communications, 1st edition. Cambridge University Press, 2005, Journal of Network and Computer Applications ,46, 198–226.

  36. Wang, Z., & Giannakis, G. B. (2003). A simple and general parameterization quantifying performance in fading channels. IEEE Transactions on Communications, 51(8), 1389–1398.

    Article  Google Scholar 

  37. Cui, S., Goldsmith, A. J., & Bahai, A. (2005). Energy-constrained modulation optimization. IEEE Transactions on Wireless Communications, 4(5), 2349–2360.

    Article  Google Scholar 

  38. Bandyopadhyay, S., & Coyle, E. J. (2004). Minimizing communication costs in hierarchically-clustered networks of wireless sensors. Computer Networks, 44(1), 1–16.

    Article  Google Scholar 

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Correspondence to Akshay Verma.

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Verma, A., Kumar, S., Gautam, P.R. et al. Enhanced Cost and Sub-epoch Based Stable Energy-Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks. Wireless Pers Commun 131, 3053–3072 (2023). https://doi.org/10.1007/s11277-023-10601-2

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