Skip to main content

Advertisement

Log in

A new data aggregation approach for WSNs based on open pits mining

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Wireless sensor networks consist of a large number of sensor nodes with limited energy, which is widely used in various Internet of things scenarios in recent years. Regarding the vast use of smart objects and applications, one of the big challenges is to collect and analyse the data. Sensor energy limitations and data redundancy are the primary challenges in these networks and reduce network lifetime as well. Therefore, the nodes try to eliminate redundant data, before transferring it to the central station. Data aggregation in IoT such as wireless sensor network plays an important role because in IoT there are heterogeneous data collected from different sources which need more energy to send data. One of the solutions to reduce energy, in this case, is to process and aggregate data prior to sending it. Data aggregation is an effective technique in reducing the data redundancy as well as improving energy efficiency; It also increases the lifespan of Wireless Sensor Networks. Integrating and combining relevant and identical data prevents sending additional packets, and minimizes the redundancy, saves energy, and increases network lifetime. The main purpose of this paper is to provide a new data aggregation method based on the open-pit mining idea efficiently. In this approach, the wireless sensor network is divided into several clusters, and in each cluster, a central node is specified, around which some hypothetical pits are considered to aggregate and send data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Dehkordi, S. A., et al. (2020). A survey on data aggregation techniques in IoT sensor networks. Wireless Networks, 26(2), 1243–1263.

    Article  Google Scholar 

  2. Szewczyk, R., Mainwaring, A., Polastre, J., Anderson, J., & Culler, D. (2004). An analysis of a large scale habitat monitoring application. In Proceedings of the 2nd international conference on embedded networked sensor systems (pp. 214–226). ACM.

  3. Fang, Q., Zhao, F., Guibas, L. (2003). Lightweight sensing and communication protocols for target enumeration and aggregation. In Proceedings of the 4th ACM international Symposium on Mobile ad hoc networking and computing (pp. 165–176). ACM.

  4. Hefeeda, M., & Bagheri, M. (2009). Forest fire modeling and early detection using wireless sensor networks. Ad Hoc & Sensor Wireless Networks, 7(3–4), 169–224.

    Google Scholar 

  5. Ren, F., Zhang, J., Wu, Y., He, T., Chen, C., & Lin, C. (2012). Attribute-aware data aggregation using potential-based dynamic routing in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 24(5), 881–892.

    Article  Google Scholar 

  6. Liu, H., Zhang, Z., Srivastava, J., Firoiu, V., & Decleene, B. (2007). PWave: Flexible potentila-based routing framework for wireless sensor networks. In: Proceeding of IFIP/TC6 networking conference (pp. 14–18).

  7. Kwon, D. Y., Chung, J.-H., Suh, T., Lee, W. G., & Hury, K. (2009). A potential based routing protocol for mobile ad hoc networks. In 2009 11th IEEE international conference on high performance computing and communications (pp. 273–280). IEEE.

  8. Basu, A., Lin, A., & Ramanathan, S. (2003). Routing using potentials: A dynamic traffic-aware routing algorithm. In Proceedings of the 2003 conference on applications, technologies, architectures, and protocols for computer communications (pp. 37–48).

  9. Sasirekha, S., & Swamynathan, S. (2015). A comparative study and analysis of data aggregation techniques in WSN. Indian Journal of Science and Technology, 8(26), 1–10.

    Article  Google Scholar 

  10. Randhawa, S., & Jain, S. (2017). Data aggregation in wireless sensor networks: Previous research, current status and future directions. Wireless Personal Communications, 97(3), 3355–3425.

    Article  Google Scholar 

  11. Roy, N. R., & Chandra, P. (2019). Analysis of data aggregation techniques in WSN. In International conference on innovative computing and communications (Vol. 2, p. 571). Springer.

  12. Ghate, V., & Vijayakumar, V. (2018). Machine learning for data aggregation in WSN: A survey. International Journal of Pure and Applied Mathematics, 118(24), 1–12.

    Google Scholar 

  13. Kumar, H., & Singh, P. K. (2018). Comparison and analysis on artificial intelligence based data aggregation techniques in wireless sensor networks. Procedia Computer Science, 132, 498–506.

    Article  Google Scholar 

  14. Li, X., Zhou, Z., Guo, J., Wang, S., & Zhang, J. (2019). Aggregated multi-attribute query processing in edge computing for industrial IoT applications. Computer Networks, 151, 114–123.

    Article  Google Scholar 

  15. Movva, P., & Rao, P. T. (2018). Novel two-fold data aggregation and MAC scheduling to support energy efficient routing in wireless sensor network. IEEE Access, 7, 1260–1274.

    Article  Google Scholar 

  16. Wang, X., Zhou, Q., & Tong, J. (2019). V-matrix-based scalable data aggregation scheme in WSN. IEEE Access, 7, 56081–56094.

    Article  Google Scholar 

  17. Lin, J.-W., Chelliah, P. R., Hsu, M.-C., & Hou, J.-X. (2019). Efficient fault-tolerant routing in IoT wireless sensor networks based on bipartite-flow graph modeling. IEEE Access, 7, 14022–14034.

    Article  Google Scholar 

  18. Xu, X., Ansari, R., Khokhar, A., & Vasilakos, A. V. (2015). Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Transactions on Sensor Networks (TOSN), 11(3), 45.

    Article  Google Scholar 

  19. Al-Qurabat, A. K. M., & Idrees, A. K. (2019). Two level data aggregation protocol for prolonging lifetime of periodic sensor networks. Wireless Networks, 25(6), 3623–3641.

    Article  Google Scholar 

  20. Uke, S., & Thool, R. (2016). UML based modeling for data aggregation in secured wireless sensor network. Procedia Computer Science, 78, 706–713.

    Article  Google Scholar 

  21. Liu, F., & Chang, Y. (2019). An energy aware adaptive kernel density estimation approach to unequal clustering in wireless sensor networks. IEEE Access, 7, 40569–40580.

    Article  Google Scholar 

  22. Nguyen, N.-T., Liu, B.-H., Chu, S.-I., & Weng, H.-Z. (2018). Challenges, designs, and performances of a distributed algorithm for minimum-latency of data-aggregation in multi-channel WSNs. IEEE Transactions on Network and Service Management, 16(1), 192–205.

    Article  Google Scholar 

  23. Siddiqui, S., Khan, A. A., & Ghani, S. (2015). A survey on data aggregation mechanisms in wireless sensor networks. In: 2015 international conference on information and communication technologies (ICICT) (pp. 1–7). IEEE.

  24. Mahalakshmi, B., & Rajavignesh, R. (2014). ADA: Data aggregation scheme for dynamic application using PBDR in wireless sensor networks.

  25. Liao, W.-H., Kao, Y., & Fan, C.-M. (2008). Data aggregation in wireless sensor networks using ant colony algorithm. Journal of Network and Computer Applications, 31(4), 387–401.

    Article  Google Scholar 

  26. Zou, Z., & Qian, Y. (2019). Wireless sensor network routing method based on improved ant colony algorithm. Journal of Ambient Intelligence and Humanized Computing, 10(3), 991–998.

    Article  Google Scholar 

  27. Taruna, S., Lata, J. K., & Purohit, G. (2012). Optimal clustering in zone based protocol of wireless sensor network. In International conference on computer science and information technology (pp. 465–472). Springer.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mahdieh Ghazvini.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramezanifar, H., Ghazvini, M. & Shojaei, M. A new data aggregation approach for WSNs based on open pits mining. Wireless Netw 27, 41–53 (2021). https://doi.org/10.1007/s11276-020-02442-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-020-02442-9

Keywords

Navigation