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Web Based Cross Layer Optimization Technique for Energy Efficient WSN

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Abstract

In recent days, wireless sensor networks (WSN) plays a major role in the real time applications like military battlefield surveillance, industrial process monitoring, machine health monitoring and so on. In WSN, selecting the cluster head (CH) is the challenging task. CH selection is done by considering parameters of single layer only. In cross layer protocol more than one layers are considered for inter related parameters such as integration of MAC/physical layer and integration routing/MAC/physical layers. The main drawback of layer-based approach is not considering the effect on improvement of particular layer parameter to other layer parameters. In this paper, new cross layer technique for energy efficient module is designed to address the energy efficiency issues, which is common to all layers and used to optimize the energy from one layer parameter by others. Nowadays everything is possible with the help of Internet, so sharing the information between WSN and TCP through the energy efficient cross layer can be done. It is done with transport layer to enhance the application filed to be reliably connected to the web. In this paper, dynamically adapted sleep scheduling mechanism is used with residual energy of each node. Virtual end-to-end packet rate selection and congestion control feedback mechanism are considered for end to end delay. This reduces the packet loss with the support of data-rate adaptation technique.

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References

  1. 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 

  2. Farooq, M. O., Dogar, A. B., & Shah, G. A. (2010). MR-LEACH: Multi-hop routing with low energy adaptive clustering hierarchy. In Proceedings—4th international conference on sensor technologies and applications, SENSORCOMM 2010 (pp. 262–268).

  3. Nazir, B., & Hasbullah, H. (2010). Energy efficient multi hierarchy clustering protocol for wireless sensor network (EMHC). In IEEE—International conference on intelligence and information technology (ICIIT 2010) (pp. 609–614).

  4. Ramesh, K., & Somasundaram, K. (2011). A comparative study of cluster head selection algorithms in wireless sensor networks. International Journal of Computer Science & Engineering Survey (IJCSES),2(4), 153–164.

    Article  Google Scholar 

  5. Loh, P. K. K., & Pan, Y. (2009). An energy-aware clustering approach for wireless sensor networks. International Journal of Communications, Network and System Sciences,02(May), 131–141.

    Article  Google Scholar 

  6. Liu, M., Cao, J., Chen, G., & Wang, X. (2009). An energy-aware routing protocol in wireless sensor networks. Sensors,9(1), 445–462.

    Article  Google Scholar 

  7. Abdulla, A. E. A. A., Nishiyama, H., Yang, J., Ansari, N., & Kato, N. (2012). HYMN: A novel hybrid multi-hop routing algorithm to improve the longevity of WSNs. IEEE Transactions on Wireless Communications,11(7), 2531–2541.

    Article  Google Scholar 

  8. Kozat, U. C., Koutsopoulos, I., & Tassiulas, L. (2004). A framework for cross-layer design of energy-efficient communication with QoS provisioning in multi-hop wireless networks. In INFOCOM 2004. Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies, 2004 (Vol. 2, pp. 1446–1456).

  9. Cui, S., Madan, R., Goldsmith, A. J., & Lall, S. (2007). Cross-layer energy and delay optimization in small-scale sensor networks. IEEE Transactions on Wireless Communications,6(10), 3688–3699.

    Article  Google Scholar 

  10. Han, G., Dong, Y., Guo, H., Shu, L., & Wu, D. (2015). Cross-layer optimized routing in wireless sensor networks with duty cycle and energy harvesting. Wireless Communications and Mobile Computing, 15(16), 1957–1981.

    Article  Google Scholar 

  11. Boyd, S., & Vandenberge, L. (2003). Convex Optimization. Cambridge: Cambridge University Press.

    Google Scholar 

  12. Ramesh, K., & Somasundaram, K. (2016). Wireless sensor network lifetime enhancement using modified clustering and scheduling algorithm. Circuits and Systems,7, 1787–1793.

    Article  Google Scholar 

  13. Ramesh, K., Saritha, S., & Somasundaram, K. (2016). Enhancement of network lifetime by improving the LEACH protocol for large scale WSN. Indian Journal of Science and Technology,9(16), 1–6.

    Article  Google Scholar 

  14. Chandravathi, C., & Mahadevan, K. (2016). Low duty-cycle based optimized sleep-schedule (LDCOS) cross layer design for WSN. Asian Journal of Research in Social Sciences and Humanities,6(10), 1910–1919.

    Article  Google Scholar 

  15. Chandravathi, C., & Mahadevan, K. (2017). Low duty-cycle based optimized sleep-schedule and active-neighbours based route (LDOSAR) cross layer design for WSN. Advances in Natural and Applied Sciences.,11(6), 536–543.

    Google Scholar 

  16. Zhang, W., Wei, X., Han, G., & Tan, X. (2018). An energy-efficient ring cross-layer optimization algorithm for wireless sensor networks. IEEE Access,6, 16588–16598.

    Article  Google Scholar 

  17. Nazeer, M., & Murthy, G. R. (2018). Cognitive cross-layer, energy efficient MAC protocol in mobile wireless sensor networks. International Journal of Engineering and Advanced Technology,8(2), 22–28.

    Google Scholar 

  18. Tan, J., Liu, A., Zhao, M., et al. (2018). Cross-layer design for reducing delay and maximizing lifetime in industrial wireless sensor networks. Journal of Wireless Networking and Communications. https://doi.org/10.1186/s13638-018-1057-x.

    Article  Google Scholar 

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Correspondence to C. Chandravathi.

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Chandravathi, C., Mahadevan, K. Web Based Cross Layer Optimization Technique for Energy Efficient WSN. Wireless Pers Commun 117, 2781–2792 (2021). https://doi.org/10.1007/s11277-020-07047-1

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