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Tree-Based Threshold-Sensitive Energy-Efficient Routing Approach For Wireless Sensor Networks

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Abstract

The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing protocols to forward data samples from event regions to sink via minimum cost links. Clustering is a commonly used data aggregation method in which nodes are organized into groups in order to reduce the energy consumption. However, in clustering protocols, CH has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long run operation of WSN. In this paper, a tree based clustering approach named threshold-sensitive energy-efficient tree-based routing protocol is proposed using enhanced flower pollination algorithm to extend the operational lifetime of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in terms of energy consumption, stability period and system lifetime.

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References

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

    Article  Google Scholar 

  2. Anisi, M. H., Abdul-Salaam, G., Idris, M. Y. I., Wahab, A. W. A., & Ahmedy, I. (2015). Energy harvesting and battery power based routing in wireless sensor networks. Wireless Networks, 23, 249–266.

    Article  Google Scholar 

  3. 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 

  4. Halawani, S., & Khan, A. W. (2010). Sensors lifetime enhancement techniques in wireless sensor networks—A survey. Journal of Computing, 2(5), 34–47.

    Google Scholar 

  5. Idris, M. Y. I., Znaid, A. M. A., Wahab, A. W. A., Qabajeh, L. K., & Mahdi, O. A. (2016). Low communication cost (LCC) scheme for localizing mobile wireless sensor networks. Wireless Networks, 23, 737–747.

    Google Scholar 

  6. Heinzelman, W. B., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of 33rd annual Hawaii international conference on system sciences (HICSS-33) (p. 223). IEEE. https://doi.org/10.1109/hicss.2000.926982.

  7. 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 

  8. Manjeshwar, A., & Agrawal, D. P. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In 15th International parallel and distributed processing symposium (IPDPS’01) workshops, USA, California, pp. 2009–2015.

  9. Manjeshwar, A., & Agrawal, D. P. (2002). APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In International parallel and distributed processing symposium, Florida (pp. 195–202).

  10. Smaragdakis, G., Matta, I., & Bestavros, A. (2004). SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. In Proceedings of International Workshop on SANPA. http://open.bu.edu/xmlui/bitstream/handle/2144/1548/2004-022-sep.pdf?sequence=1.

  11. Qing, L., Zhu, Q., & Wang, M. (2006). Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor network. Computer Communications, 29, 2230–2237. https://doi.org/10.1016/j.comcom.2006.02.017.

    Article  Google Scholar 

  12. Kang, S. H., & Nguyen, T. (2012). Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Communications Letters, 16(9), 1396–1399. https://doi.org/10.1109/LCOMM.2012.073112.120450.

    Article  Google Scholar 

  13. Kumar, D., Aseri, T. C., & Patel, R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications, 32, 662–667. https://doi.org/10.1016/j.comcom.2008.11.025.

    Article  Google Scholar 

  14. Kumar, D. (2014). Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks. IET Wireless Sensor Systems, 4(1), 9–16. https://doi.org/10.1049/iet-wss.2012.0150.

    Google Scholar 

  15. 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. https://doi.org/10.1109/JSEN.2014.2358567.

    Article  Google Scholar 

  16. Aderohunmu, F. A., Deng, J. D., &Purvis, M. K. (2011). A deterministic energy-efficient clustering protocol for wireless sensor networks. In Proceedings of 7th international conference on intelligent sensors, sensor networks and information processing (ISSNIP ‘11) (pp 341–346). IEEE. https://doi.org/10.1109/issnip.2011.6146592.

  17. Mittal, N., & Singh, U. (2015). Distance-based residual energy-efficient stable election protocol for WSNs. Arabian Journal of Science and Engineering, 40(6), 1637–1646. https://doi.org/10.1007/s13369-015-1641-x.

    Article  Google Scholar 

  18. Mittal, N., Singh, U., & Sohi, B. S. (2016). A stable energy efficient clustering protocol for wireless sensor networks. Wireless Networks. https://doi.org/10.1007/s11276-016-1255-6.

    Google Scholar 

  19. Adnan, Md A, Razzaque, M. A., Ahmed, I., & Isnin, I. F. (2014). Bio-Mimic optimization strategies in wireless sensor networks: A survey. Sensors, 14, 299–345. https://doi.org/10.3390/s140100299.

    Article  Google Scholar 

  20. Hussain, S., & Matin, A. W. (2006). Hierarchical cluster-based routing in wireless sensor networks. In IEEE/ACM international conference on information processing in sensor Networks, IPSN, 2006.

  21. Khalil, E. A., & Attea, B. A. (2011). Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm and Evolutionary Computation. https://doi.org/10.1016/j.swevo.2011.06.004.

    Google Scholar 

  22. Attea, B. A., & Khalil, E. A. (2012). A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Applied Soft Computing, 12, 1950–1957. https://doi.org/10.1016/j.asoc.2011.04.007.

    Article  Google Scholar 

  23. Khalil, E. A., & Attea, B. A. (2013). Stable-aware evolutionary routing protocol for wireless sensor networks. Wireless Personal Communications, 69(4), 1799–1817.

    Article  Google Scholar 

  24. Mittal, N., Singh, U., & Sohi, B. S. (2017). A novel energy efficient stable clustering approach for wireless sensor networks. Wireless Personal Communications, 95(3), 2947–2971.

    Article  Google Scholar 

  25. Mittal, N., Singh, U., & Sohi, B. S. (2017). Harmony search algorithm based threshold-sensitive energy-efficient clustering protocols for WSNs. Ad Hoc & Sensor Wireless Networks, 36(1–4), 149–174.

    Google Scholar 

  26. Mittal, N., Singh, U., & Sohi, B. S. (2018). A boolean spider monkey optimization based energy efficient clustering approach for WSNs. Wireless Networks, 24(6), 2093–2109.

    Article  Google Scholar 

  27. Lindsey, S., & Raghavendra, C. (2002). PEGASIS: Power-efficient gathering in sensor information systems. In Proceedings of IEEE Aerospace Conference (Vol. 3, pp. 1125–1130).

  28. Kim, K. T., & Youn, H. Y. (2010). Tree-based clustering (TBC) for energy efficient wireless sensor networks. In Proceedings of IEEE 24th international conference on advanced information networking and applications workshops (WAINA) (pp. 680–685).

  29. Tan, H. O., & Korpeoglu, I. (2003). Power efficient data gathering and aggregation protocol in wireless sensor networks. SIGMOD Record, 32(4), 66–71.

    Article  Google Scholar 

  30. Momani, A. L., Saadeh, M., Akhras, M. A. L., & Jawawdeh, H. A. L. (2011). A tree-based power saving routing protocol for wireless sensor networks. International Journal of Computers and Communications, 5(2), 84–92.

    Google Scholar 

  31. Satapathy, S. S., & Sarma, N. (2006). TREEPSI: Tree based energy efficient protocol for sensor information. In Wireless and optical communications networks, 2006 IFIP international conference, April 2006.

  32. Han, Z., Wu, J., Zhang, J., Liu, L., & Tian, K. (2014). A general self-organized tree based energy balance routing protocol for wireless sensor network. IEEE Transactions on Nuclear Science, 61(2), 732–740.

    Article  Google Scholar 

  33. Yang, X. S. (2012). Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240–249). Berlin: Springer.

  34. Singh, U., & Salgotra, R. (2018). Synthesis of linear antenna array using flower pollination algorithm. Neural Computing and Applications, 29(2), 435–445.

    Article  Google Scholar 

  35. Singh, U., & Salgotra, R. (2017). Pattern synthesis of linear antenna arrays using enhanced flower pollination algorithm. International Journal of Antennas and Propagation. https://doi.org/10.1155/2017/7158752.

    Google Scholar 

  36. Draa, A. (2015). On the performances of the flower pollination algorithm—Qualitative and quantitative analyses. Applied Soft Computing, 34, 349–371.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Chandigarh University, Mohali, Punjab, 140413, India

    Nitin Mittal

  2. Department of Electronics and Communication Engineering, Thapar University, Patiala, Punjab, 147004, India

    Urvinder Singh & Rohit Salgotra

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  1. Nitin Mittal
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  2. Urvinder Singh
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Correspondence to Nitin Mittal.

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Mittal, N., Singh, U. & Salgotra, R. Tree-Based Threshold-Sensitive Energy-Efficient Routing Approach For Wireless Sensor Networks. Wireless Pers Commun 108, 473–492 (2019). https://doi.org/10.1007/s11277-019-06413-y

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