Advertisement

Wireless Personal Communications

, Volume 108, Issue 4, pp 2559–2583 | Cite as

Data Redundancy-Control Energy-Efficient Multi-Hop Framework for Wireless Sensor Networks

  • Gulnaz Ahmed
  • Xi ZhaoEmail author
  • Mian Muhammad Sadiq FareedEmail author
  • Muhammad Rizwan Asif
  • Syed Ali Raza
Article
  • 39 Downloads

Abstract

Wireless Sensor Network (WSN) is an emerging technology that has attractive intelligent sensor-based applications. In these intelligent sensor-based networks, control-overhead management and elimination of redundant inner-network transmissions are still challenging because the current WSN protocols are not data redundancy-aware. The clustering architecture is an excellent choice for such challenges because it organizes control traffic, improves scalability, and reduces the network energy by reducing inner-network communication. However, the current clustering protocols periodically forward the data and consume more energy due to data redundancy. In this paper, we design a novel cluster-based redundant transmission control clustering framework that checks the redundancy of the data through the statistical tests with an appropriate degree of confidence. After that, the cluster-head separates and deletes the redundant data from the available data sets before sending it to the next level. We also designed a spatiotemporal multi-cast dynamic cluster-head role rotation that is capable of easily adjusting the non-associated cluster member nodes. Moreover, the designed framework carefully selects the forwarders based on the transmission strength and effectively eliminates the back-transmission problem. The proposed framework is compared with the recent schemes using different quality measures and we found that our proposed framework performs favorably against the existing schemes for all of the evaluation metrics.

Keywords

Wireless sensor network Data redundant Control-overhead management Cluster-based architecture Best forwarder selection 

Notes

Acknowledgements

This work is supported by the China Postdoctoral Science Foundation (Grant No. 2018M643683), Ministry of Education and China Mobile Joint Research Fund Program (Grant No. MCM20160302), and National Natural Science Foundation of China (Grant Nos. 91746111, 71702143, 71731009, 71732006).

References

  1. 1.
    Karimi, H., Medhati, O., Zabolzadeh, H., Eftekhari, A., Rezaei, F., Dehno, S. B., et al. (2015). Implementing a reliable, fault tolerance and secure framework in the wireless sensor-actuator networks for events reporting. Procedia Computer Science, 73, 384–394.CrossRefGoogle Scholar
  2. 2.
    Ahmed, G., Zou, J. H., Fareed, M. M. S., & Zeeshan, M. (2016). Sleep-awake energy efficient distributed clustering algorithm for wireless sensor networks. Computers and Electrical Engineering, 56, 385–398.CrossRefGoogle Scholar
  3. 3.
    Chirihane, G., Zibouda, A., & Benmohammed, M. (2016). An adaptive clustering approach to dynamic load balancing and energy efficiency in wireless sensor networks. Energy, 114, 647–662.CrossRefGoogle Scholar
  4. 4.
    Ahmed, M., Salleh, M., & Ibrahim, M. (2017). Routing protocols based on node mobility for underwater wireless sensor network (UWSN): A survey. Journal of Network and Computer Applications, 78, 242–252.CrossRefGoogle Scholar
  5. 5.
    Khan, J. U., & Cho, H. S. (2015). A distributed data-gathering protocol using AUV in underwater sensor networks. Sensors, 15(8), 19331–19350.CrossRefGoogle Scholar
  6. 6.
    Javaid, N., Hafeez, T., Wadud, Z., Alrajeh, N., Alabed, M. S., & Guizani, N. (2017). Establishing a cooperation-based and void node avoiding energy-efficient underwater WSN for a cloud. IEEE Access, 5, 11582–11593.CrossRefGoogle Scholar
  7. 7.
    Bahi, J., Makhoul, A., & Medlej, M. (2014). A two tiers data aggregation scheme for periodic sensor networks. Ad-Hoc & Sensor Wireless Networks, 21(1–2), 77–100.Google Scholar
  8. 8.
    Guangjie, H., Jiang, J., Bao, N., Wan, L., & Guizani, M. (2015). Routing protocols for underwater wireless sensor networks. IEEE Communications Magazine, 53(11), 72–78.CrossRefGoogle Scholar
  9. 9.
    Deqing, W., Ru, X., Xiaoyi, H., & Wei, S. (2016). Energy-efficient distributed compressed sensing data aggregation for cluster-based underwater acoustic sensor networks. International Journal of Distributed Sensor Networks, 2016(19), 1–14.Google Scholar
  10. 10.
    Liao, Y., Qi, H., & Li, W. (2013). Load-balanced clustering algorithm with distributed self-organization for wireless sensor networks. IEEE Sensing Journal, 13, 1498–1506.CrossRefGoogle Scholar
  11. 11.
    Dervis, K., Okdem, S., & Ozturk, C. (2012). Cluster-based wireless sensor network routing using artificial bee colony algorithm. Wireless Network, 18, 847–860.CrossRefGoogle Scholar
  12. 12.
    Orojloo, H., & Haghighat, A. T. (2015). A Tabu search based routing algorithm for wireless sensor networks. Wireless Networks, 22(5), 1711–1724.CrossRefGoogle Scholar
  13. 13.
    Tanwar, S., Tyagi, S., Kumar, N., & Obaidat, M. S. (2018). LA-MHR: Learning automata based multilevel heterogeneous routing for opportunistic shared spectrum access to enhance lifetime of WSN. IEEE Systems Journal, 13(1), 313–323.CrossRefGoogle Scholar
  14. 14.
    Lin, H., Chen, P., & Wang, L. (2015). Energy efficient clustering protocol for large-scale sensor networks. IEEE Sensor Journal, 15(12), 7150–7160.CrossRefGoogle Scholar
  15. 15.
    Fersi, G., Louati, W., & Jemaa, M. B. (2016). CLEVER: Cluster-based energy-aware virtual ring routing in randomly deployed wireless sensor networks. Peer-to-Peer Networking and Applications, 9(4), 640–655.CrossRefGoogle Scholar
  16. 16.
    Muthukumaran, K., Chitra, K., & Selvakumar, C. (2018). An energy efficient clustering scheme using multilevel routing for wireless sensor network. Computers and Electrical Engineering, 69, 642–652.CrossRefGoogle Scholar
  17. 17.
    Songhua, H., Jianghon, H., Wei, X., & Chen, Z. (2015). A multi-hop heterogeneous cluster-based optimization algorithm for wireless sensor networks. Wireless Networks, 21(1), 57–65.CrossRefGoogle Scholar
  18. 18.
    Sajwan, M., Devashish, G., & Sharma, A. K. (2018). Hybrid energy-efficient multi-path routing for wireless sensor networks. Computers and Electrical Engineering, 67, 96–113.CrossRefGoogle Scholar
  19. 19.
    Azharuddin, M., Pratyay, K., & Prasanta, K. P. (2015). Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks. Computers and Electrical Engineering, 41, 177–190.CrossRefGoogle Scholar
  20. 20.
    Vakily, T. V., & Jannati, M. J. (2010). A new method to improve performance of cooperative underwater acoustic wireless sensor networks via frequency controlled transmission based on length of data links. Wireless Sensor Network, 2, 381–389.CrossRefGoogle Scholar
  21. 21.
    Harb, H., Makhoul, A., Tawil, R., & Jaber, A. (2014). A suffix-based enhanced technique for data aggregation in periodic sensor networks. In International wireless communications and mobile computing conference (IWCMC), Nicosia, 494–499.Google Scholar
  22. 22.
    Tran, K. T. M., Oh, S. H., & Byun, J. Y. (2013). Well-suited similarity functions for data aggregation in cluster-based underwater wireless sensor networks. International Journal of Distributed Sensor Networks, 2013, Article ID 645243,7.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of ManagementXi’an Jiaotong UniversityXi’anChina
  2. 2.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina
  3. 3.School of Electrical EngineeringXi’an Jiaotong UniversityXi’anChina

Personalised recommendations