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Energy Aware Efficient Data Aggregation (EAEDAR) with Re-scheduling Mechanism Using Clustering Techniques in Wireless Sensor Networks

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Abstract

In present scenario of wireless sensor networks and communications, efficient sensed data transmission among nodes is being a great confrontation because of the impulsive and volatile nature of sensors in the network. For providing that and enhancing network lifetime, there are several approaches are developed, specifically using clustering techniques. Still, there are requirements for energy based efficient routing in WSN. With that note, this paper develops anEnergy Aware Efficient Data Aggregation (EAEDAR) and Data Re-Schedulingwith the incorporation of clustering techniques. Moreover, the model used energy based cluster formation and cluster head selection for increasing the network stability and data delivery rate. The model comprises four main phases, namely, Energy factor based cluster formation, Aggregator_SN (Sensor Node) Selection, Efficient Data Aggregation (EDA) and Data Re-Scheduling based on delay and processing time. Furthermore, the model is updated with respect to the status of the nodes and links, for providing consistent network with improved reliable data transmissions. The simulation results portrays the effectiveness of the proposed model over other compared works in terms of the performance factors such as, throughput, packet delivery ratio, network lifetime, transmission delay and packet drop.

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References

  1. Vass, D., & Vidacs, A. (2007, July). Distributed data aggregation with geographical routing in wireless sensor networks. In IEEE international conference on pervasive services.

  2. Kohonen, K. (2004, Nov). Data gathering in sensor networks. Helsinki Institute for Information Technology in Finland.

  3. Selvaraj, J., & Mohammed, A. S. (2020). Mutation-based PSO techniques for optimal location and parameter settings of STATCOM under generator contingency. International Journal of Intelligence and Sustainable Computing, 1(1), 53.

    Article  Google Scholar 

  4. TarekAbdelzaher, T., He, & Stankovic, J. (2004). Feedback control of data aggregation in sensor networks. In IEEE conference on decision and control.

  5. Kumar, K. V., Jayasankar, T., Eswaramoorthy, V., & Nivedhitha, V. (2020). SDARP: Security based Data Aware Routing Protocol for ad hoc sensor networks. International Journal of Intelligent Networks, 1, 36–42.

    Article  Google Scholar 

  6. Nguyen, N. T., Liu, B. H., Pham, V. T., & Luo, Y. S. (2016). On maximizing the lifetime for data aggregation in wireless sensor networks using virtual data aggregation trees. Computer Networks, 105, 99–110.

    Article  Google Scholar 

  7. Haseeb, K., Bakar, K. A., Abdullah, A. H., & Darwish, T. (2017). Adaptive energy aware cluster-based routing protocol for wireless sensor networks. Wireless Networks, 23, 1953–1966.

    Article  Google Scholar 

  8. 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 

  9. Junping, H., Yuhui, J., & Liang, D. (2008). A time-based cluster-head selection algorithm forLEACH. In IEEE symposium on computers and communications, 2008. ISCC 2008, pp. 1172–1176. New York: IEEE.

  10. Cheng, C. T., Leung, H., & Maupin, P. (2013). A delay-aware network structure for wireless sensor networks with in-network data fusion. IEEE Sensors Journal, 13, 1622–1631.

    Article  Google Scholar 

  11. Thakkar, A., & Kotecha, K. (2014). Cluster head election for energy anddelay constraint applications of wireless sensor network. IEEE Sensors Journal, 14, 2658–2664.

    Article  Google Scholar 

  12. Cai, H., Zhang, Y., Yan, H., Shen, F., Zhou, K., & Zhang, C. (2016). Adelay-aware wireless sensor network routing protocol forindustrial applications. Mobile Networks and Applications, 21, 879–889.

    Article  Google Scholar 

  13. Li, W., Jia, B., Saruwatari, S., & Watanabe, T. (2016). Waterfalls partial aggregation in wireless sensor networks. International Journal of Distributed Sensor Networks. https://doi.org/10.1155/2016/2392149.

    Article  Google Scholar 

  14. Kumar, A. K., Sivalingam, K. M., & Kumar, A. (2013). On reducing delay in mobile data collection based wireless sensor networks. Wireless Networks, 19, 285–299.

    Article  Google Scholar 

  15. Yao, Y., Cao, Q., & Vasilakos, A. V. (2015). EDAL: An energy-efficient,delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking, 23, 810–823.

    Article  Google Scholar 

  16. Liao, Y., Qi, H., & Li, W. (2013). Load-balanced clustering algorithm with distributed self-organization for wirelesssensor networks. IEEE Sensors Journal, 13, 1498–1506.

    Article  Google Scholar 

  17. Baranidharan, B., Srividhya, S., & Santhi, B. (2014). Energy efficient hierarchical unequal clustering in wireless sensor networks. Indian Journal of Science and Technology ,7, 301

    Article  Google Scholar 

  18. Selvi, G. V., & Manoharan, R. (2015). Balanced unequal clustering algorithm for wireless sensor network”, i-Manager’s. Journal on Wireless Communication Networks, 3, 327–332.

    Google Scholar 

  19. Zhang, D., Liu, S., Zhang, T., & Liang, Z. (2017). Novel unequal clustering routing protocol considering energybalancing based on network partition & distance for mobile education. Journal of Network and ComputerApplications, 88, 1–9.

    Google Scholar 

  20. Rao, P. S., & Banka, H. (2017). Novel chemical reaction optimization based unequal clustering and routingalgorithms for wireless sensor networks. Wireless Networks, 23, 759–778.

    Article  Google Scholar 

  21. Jadoon, R. N., Zhou, W., Jadoon, W., & Ahmed Khan, I. (2018). RARZ: Ring-zone based routing protocol for wireless sensor networks. Applied Sciences, 8, 1023.

    Article  Google Scholar 

  22. Ding, M., Cheng, X., & Xue, G. (2003). Aggregation tree construction in sensor networks. Citeseer (pp. 2168–2172).

  23. Kim, K. T., Lyu, C. H., Moon, S. S., & Youn, H. Y. (2010). Tree-based clustering (TBC) for energy efficient wireless sensor networks, 2010, Yichang. IEEE. (pp. 680–685).

  24. Kumar, S., Verma, S. K., & Kumar, A. (2015). Enhanced threshold sensitive stable election protocol for heterogeneous wireless sensor network. Wireless Personal Communications, 85, 2643–2656.

    Article  Google Scholar 

  25. Kumar, D., Aseri, T. C., & Patel, R. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications, 32, 662–667.

    Article  Google Scholar 

  26. Yuvaraj, N., Kousik, N. V., Raja, R. A., & Saravanan, M. (2020). Automatic skull-face overlay and mandible articulation in data science by AIRS-Genetic algorithm. International Journal of Intelligent Networks, 1, 9–16.

    Article  Google Scholar 

  27. Wang, S.-S., & Chen, Z.-P. (2013). LCM: a link-aware clustering mechanism for energy-efficient routing in wireless sensor networks. IEEE Sensors Journal, 13, 728–736.

    Article  Google Scholar 

  28. Thiruchelvi, A., & Karthikeyan, N. (2020). A novel pair based sink relocation and route adjustment in mobile sink WSN integrated IoT. IET Communications, 14(3), 365–375.

    Article  Google Scholar 

  29. Bhavadharini, R. M., Karthik, S., Karthikeyan, N., & Paul, A. (2018). Wireless networking performance in IoT using adaptive contention window. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2018/7248040.

    Article  Google Scholar 

  30. Shobana, M., Sabitha, R., & Karthik, S. (2020). An enhanced soft computing-based formulation for secure data aggregation and efficient data processing in large-scale wireless sensor network. Journal of Soft Computing. https://doi.org/10.1007/s00500-020-04694-1.

    Article  Google Scholar 

  31. Raj Kannan, J., Sabitha, R., Karthik, S., et al. (2020). Mouse movement pattern based analysis of customer behavior (CBA-MMP) using cloud data analytics. Journal Wireless Personal Communications. https://doi.org/10.1007/s11277-020-07055-1.

    Article  Google Scholar 

  32. Dhanapal, R., & Visalakshi, P. (2016). Real time health care monitoring system for driver community using ADHOC sensor network. Journal of Medical Imaging and Health Informatics, 6(3), 811–815.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Info Institute of Engineering, Coimbatore, Tamilnadu, 641107, India

    D. Loganathan & M. Balasubramani

  2. Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, Tamilnadu, 641035, India

    R. Sabitha

Authors
  1. D. Loganathan
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  2. M. Balasubramani
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  3. R. Sabitha
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Correspondence to D. Loganathan.

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Loganathan, D., Balasubramani, M. & Sabitha, R. Energy Aware Efficient Data Aggregation (EAEDAR) with Re-scheduling Mechanism Using Clustering Techniques in Wireless Sensor Networks. Wireless Pers Commun 117, 3271–3287 (2021). https://doi.org/10.1007/s11277-020-07985-w

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