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Data Association Coverage Algorithm Based on Energy Balance and Controlled Parameters in Wireless Sensor Networks

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Abstract

Based on a position-independent and computationally simple node scheduling algorithm, a scheduling algorithm based on energy balance is proposed. The analysis and simulation results showed that the algorithm can extend the lifespan of the entire network whereas ensuring energy balance. Data aggregation was a relatively time-consuming operation in sensor networks, especially in high-density networks. Therefore, minimizing the problem of data aggregation delay had become a hot topic of research. The algorithm adopted a clustering idea of low power in the cluster and high power between clusters, combined with channel allocation to reduce data aggregation delay, and data aggregation between clusters can be performed without collisions. The number of channels used in different network topologies tends to be constant.

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References

  1. Han, G., Liu, L., Jiang, J., et al. (2017). Analysis of energy-efficient connected target coverage algorithms for industrial wireless sensor networks. IEEE Transactions on Industrial Informatics, 13(1), 135–143.

    Article  Google Scholar 

  2. Naranjo, P. G. V., Shojafar, M., Mostafaei, H., et al. (2017). P-SEP: A prolong stable election routing algorithm for energy limited heterogeneous fog-supported wireless sensor networks. The Journal of Supercomputing, 73(2), 733–755.

    Article  Google Scholar 

  3. Zhao, M., Yang, Y., & Wang, C. (2015). Mobile data gathering with load balanced clustering and dual data uploading in wireless sensor networks. IEEE Transactions on Mobile Computing, 14(4), 770–785.

    Article  Google Scholar 

  4. Amodu, O. A., & Raja Mahmood, R. A. (2018). Impact of the energy-based and location-based LEACH secondary clusteraggregation on WSN lifetime. Wireless Networks, 24(5), 1379–1402.

    Article  Google Scholar 

  5. Kumar, B., & Chand, S. (2016). Maximising network lifetime for target coverage problem in wireless sensor networks. IET Wireless Sensor Systems, 6(6), 192–197.

    Article  Google Scholar 

  6. Chen, B., Jamieson, K., Balakrishnan, H., et al. (2002). SPAN: An energy efficient coordination algorithm for topology maintenance in Adhoc wireless sensor networks. ACM Wireless Networks, 8(5), 481–494.

    Article  Google Scholar 

  7. Tian, D., & Georganas, N. D. (2003). A node scheduling scheme for energy conservation in large wireless sensor networks. Wireless Communications & Mobile Computing, 3(2), 271–290.

    Article  Google Scholar 

  8. Chowdhury, S., & Giri, C. (2019). Energy and network balanced distributed clustering in wireless sensor network. Wireless Personal Communications, 105(3), 1083–1109.

    Article  Google Scholar 

  9. Fawzy, A. E., & Shokair, M. (2018). Balanced and energy-efficient multi-hop techniques for routing in wireless sensor networks. IET Networks, 7(1), 33–43.

    Article  Google Scholar 

  10. Bai, Y., Liu, S., et al. (2017). EBTM: An Energy-balanced topology method for wireless sensor networks. International Journal of Innovative Computing, 13(5), 1453–1465.

    Google Scholar 

  11. Randhawa, S., & Jain, S. (2018). Energy-efficient load balancing scheme for two-tier communication in wireless sensor networks. The Journal of Supercomputing, 74(1), 386–416.

    Article  Google Scholar 

  12. Khanmirza, H., & Yazdani, N. (2016). Game of energy consumption balancing in heterogeneous sensor networks. Wireless Communications & Mobile Computing, 16(12), 1457–1477.

    Article  Google Scholar 

  13. Baoqiang, K., Li, C., Hongsong, Z., et al. (2008). Accurate energy model for WSN node and its optimal design. Journal of Systems Engineering and Electronics, 19(3), 427–433.

    Article  Google Scholar 

  14. Tyagi, S., & Kumar, N. (2013). A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks. Journal of Network and Computer Applications, 36(2), 623–645.

    Article  Google Scholar 

  15. Lu, S., Huang, X., Cui, L., et al. (2009). Design and implementation of an ASIC-based sensor device for WSN applications. IEEE Transactions on Consumer Electronics, 55(4), 1959–1967.

    Article  Google Scholar 

  16. Wang, A., Wang, M., Pan, G., et al. (2017). Salient object detection with high-level prior based on Bayesian fusion. IET Computer Vision, 11(3), 199–206.

    Article  MathSciNet  Google Scholar 

  17. Hou, R. H., Lui, K. S., et al. (2012). Hop-by-hop routing in wireless mesh networks with bandwidth guarantees. IEEE Transactions on Mobile Computing, 11(2), 264–277.

    Article  Google Scholar 

  18. Han, G., Dong, Y., Guo, H., et al. (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 

  19. Jafarizadeh, V., Keshavarzi, A., & Derikvand, T. (2017). Efficient cluster head selection using Naive Bayes classifier for wireless sensor networks. Wireless Networks, 23(3), 779–785.

    Article  Google Scholar 

  20. Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67(8), 104–122.

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge Science and Technology Research Program of Jilin Provincial Education Department (Grant #:JJKH20190239SK) for supporting this study.

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Correspondence to Jia Mao.

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Li, W., Mao, J. & Chen, Q. Data Association Coverage Algorithm Based on Energy Balance and Controlled Parameters in Wireless Sensor Networks. Wireless Pers Commun 119, 3053–3062 (2021). https://doi.org/10.1007/s11277-021-08386-3

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  • DOI: https://doi.org/10.1007/s11277-021-08386-3

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