[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Log in

A Novel Hybrid Protocol in Achieving QoS Regarding Data Aggregation and Dynamic Traffic Routing in IoT WSNs

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

This article has been updated

Abstract

The Internet of Things in 5G and next-generation data communication networks have relied heavily on Wireless Sensor Networks (WSNs). Data aggregation, Energy consumption, bandwidth utilization, and dynamic traffic routing play a significant role and impose challenges in achieving QoS in a sensor network. Therefore, it is crucial to concentrate on these factors in order to increase the network's lifetime and quality of service. The research's goal is to analyse IoT WSNs in terms of their architecture, framework, security, data aggregation, and routing techniques. In IoT WSNs, the data aggregation technique reduces the energy consumption of the network's nodes. This improves the network's energy and other QoS parameter's efficiency. The network layer hybrid data aggregation and routing protocol proposed in this study is new and efficient. In this work, a novel method for data aggregation based on anchor-based routing and matrix filling theory is proposed. It is suggested to use improved Anchor-based Routing protocol for Event Reporting (ARER), an anchor-based routing technique that includes dynamic clustering and constrained flooding. In order to achieve QoS in IoT WSN, the proposed hybrid network layer protocol has outperformed. The proposed protocol has performed better in terms of QoS metrics like throughput, end-to-end delay, routing overhead, packet delivery ratio, and energy consumption.

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

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29

Similar content being viewed by others

Data Availability

All the data is collected from the simulation reports of the software and tools used by the authors. Authors are working on implementing the same using real world data with appropriate permissions.

Change history

  • 15 May 2023

    The original online version of this article was revised: The photos in the author biographies were interchanged.

References

  1. Chandnani, N., & Khairnar, C. N. (2022). Bio-Inspired multilevel security protocol for data aggregation and routing in IoT WSNs. Mobile Networks and Applications, 27, 1030–1049. https://doi.org/10.1007/s11036-021-01859-6

  2. Faheem, M., & Gungor, V. C. (2018). MQRP: Mobile sinks-based QoS-aware data gathering protocol for wireless sensor networks-based smart grid applications in the context of industry 4.0-based on internet of things. Future Generation Computer Systems, 82, 358–374.

    Article  Google Scholar 

  3. Jazebi, S. J., & Ghaffari, A. (2020). RISA: Routing scheme for Internet of Things using shuffled frog leaping optimization algorithm. Journal of Ambient Intelligence and Humanized Computing, 11(10), 4273–4283.

    Article  Google Scholar 

  4. Preeth, S. K. S. L., Dhanalakshmi, R., Kumar, R., & Shakeel, P. M. (2018). An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-018-1154-z

  5. Lemeshko, O., Papan, J., Yeremenko, O., Yevdokymenko, M., & Segec, P. (2021). Research and development of delay-sensitive routing tensor model in IoT core networks. Sensors, 21(11), 3934.

    Article  Google Scholar 

  6. Amiri, I. S., Prakash, J., Balasaraswathi, M., Sivasankaran, V., Sundararajan, T. V. P., Hindia, M. H. D., Tilwari, V., Dimyati, K., & Henry, O. (2020). DABPR: A large-scale internet of things-based data aggregation back pressure routing for disaster management. Wireless Networks, 26(4), 2353–2374.

    Article  Google Scholar 

  7. Li, X., Liu, A., Xie, M., Xiong, N. N., Zeng, Z., & Cai, Z. (2018). Adaptive aggregation routing to reduce delay for multi-layer wireless sensor networks. Sensors, 18(4), 1216.

    Article  Google Scholar 

  8. Homaei, M. H., Salwana, E., & Shamshirband, S. (2019). An enhanced distributed data aggregation method in the Internet of Things. Sensors, 19(14), 3173.

    Article  Google Scholar 

  9. Xiang, X., Liu, W., Wang, T., Xie, M., Li, X., Song, H., Liu, A., & Zhang, G. (2019). Delay and energy-efficient data collection scheme-based matrix filling theory for dynamic traffic IoT. EURASIP Journal on Wireless Communications and Networking, 2019(1), 1–25.

    Article  Google Scholar 

  10. Safara, F., Souri, A., Baker, T., Al Ridhawi, I., & Aloqaily, M. (2020). PriNergy: A priority-based energy-efficient routing method for IoT systems. The Journal of Supercomputing, 76(11), 8609–8626.

    Article  Google Scholar 

  11. Li, Z., Liu, Y., Liu, A., Wang, S., & Liu, H. (2020). Minimizing convergecast time and energy consumption in green internet of things. IEEE Transactions on Emerging Topics in Computing, 8(3), 797–813.

    Article  Google Scholar 

  12. Farnaghi, M., Ghaemi, Z., & Mansourian, A. (2020). Dynamic spatio-temporal tweet mining for event detection: A case study of Hurricane Florence. International Journal of Disaster Risk Science, 11(3), 378–393. https://doi.org/10.1007/s13753-020-00280-z

    Article  Google Scholar 

  13. Behera, T. M., Mohapatra, S. K., Samal, U. C., Khan, M. S., Daneshmand, M., & Gandomi, A. H. (2020). I-SEP: An improved routing protocol for heterogeneous WSN for IoT-based environmental monitoring. IEEE Internet of Things Journal, 7(1), 710–717. https://doi.org/10.1109/jiot.2019.2940988

    Article  Google Scholar 

  14. Huang, M., Liu, W., Wang, T., Song, H., Li, X., & Liu, A. (2019). A queuing delay utilization scheme for on-path service aggregation in services-oriented computing networks. IEEE Access, 7, 23816–23833.

    Article  Google Scholar 

  15. Li, J. (2019). Battery-friendly relay selection scheme for prolonging the life- times of sensor nodes in the Internet of Things. IEEE Access, 7, 33180–33201.

    Article  Google Scholar 

  16. Xiang, X., Liu, W., Liu, A., Xiong, N. N., Zeng, Z., & Cai, Z. (2019). Adaptive duty cycle control–based opportunistic routing scheme to reduce delay in cyber physical systems. International Journal of Distributed Sensor Networks, 15(4), 155014771984187–155014771984187. https://doi.org/10.1177/1550147719841870

    Article  Google Scholar 

  17. Tan, J. (2019). An adaptive collection scheme-based matrix completion for data gathering in energy-harvesting wireless sensor networks. IEEE Access, 7, 6703–6723.

    Article  Google Scholar 

  18. Liu, X., Liu, Y., Zhang, N., Wu, W., & Liu, A. (2019). Optimizing trajectory of un- manned aerial vehicles for efficient data acquisition: A matrix completion approach. IEEE Internet of Things Journal, 6(2), 1829–1840.

    Article  Google Scholar 

  19. Ren, Y., Liu, Y., Zhang, N., Liu, A., Xiong, N. N., & Cai, Z. (2018). Minimum-cost mobile crowdsourcing with QoS guarantee using matrix completion technique. Pervasive and Mobile Computing, 49, 23–44. https://doi.org/10.1016/j.pmcj.2018.06.012

    Article  Google Scholar 

  20. Wu, M. (2018). An effective delay reduction approach through a portion of nodes with a larger duty cycle for industrial WSNs. Sensors, 18(5), 1535–1535.

    Article  Google Scholar 

  21. Xu, X. (2018). A cross-layer optimized opportunistic routing scheme for loss- and-delay sensitive WSNs. Sensors (Switzerland), 18(5), 1422.

    Article  Google Scholar 

  22. Zhang, D., Zhang, T., Zhang, J., Dong, Y., & Zhang, X. (2018). A kind of effective data aggregating method based on compressive sensing for wireless sensor network. EURASIP Journal on Wireless Communications and Networking, 2018(1), 1–15.

    Article  Google Scholar 

  23. Jabbar, W. A., Saad, W. K., & Ismail, M. (2018). MEQSA-OLSRv2: A multicriteria-based hybrid multipath protocol for energy-efficient and QoS-aware data routing in MANET-WSN convergence scenarios of IoT. IEEE Access, 6, 76546–76572.

    Article  Google Scholar 

  24. Jaiswal, K., & Anand, V. (2021). A grey-wolf based optimized clustering approach to improve QoS in wireless sensor networks for IoT applications. Peer-to-Peer Networking and Applications, 14(4), 1943–1962.

    Article  Google Scholar 

  25. Badiger, V. S., & Ganashree, T. S. (2022). Data aggregation scheme for IOT based wireless sensor network through optimal clustering method. Measurement: Sensors, 24, 100538.

    Google Scholar 

  26. Kavitha, V. (2021). Privacy preserving using multi-hop dynamic clustering routing protocol and elliptic curve cryptosystem for WSN in IoT environment. Peer-to-Peer Networking and Applications, 14(2), 821–836.

    Article  MathSciNet  Google Scholar 

  27. Arulprakash, A., Baalamurugan, K. M., Dhanaraj, R. K., Sampath Kumar, K., Gupta, P., & Rehman, S. (2022). Aggregation technique using dynamic cross-propagation clustering algorithm in wireless body sensor networks. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2022/6102584

    Article  Google Scholar 

  28. Krishnan, M., & Lim, Y. (2021). Reinforcement learning-based dynamic routing using mobile sink for data collection in WSNs and IoT applications. Journal of Network and Computer Applications, 194, 103223.

    Article  Google Scholar 

  29. Altowaijri, S. M. (2022). Efficient next-hop selection in multi-hop routing for IoT enabled wireless sensor networks. Future Internet, 14(2), 35.

    Article  Google Scholar 

  30. Mahesh, N., & Vijayachitra, S. (2022). Hierarchical autoregressive bidirectional least-mean-square algorithm for data aggregation in WSN based IoT network. Advances in Engineering Software, 173, 103275.

    Article  Google Scholar 

  31. Ramezanifar, H., Ghazvini, M., & Shojaei, M. (2021). A new data aggregation approach for WSNs based on open pits mining. Wireless Networks, 27(1), 41–53.

    Article  Google Scholar 

  32. Jothi Kumar, C., Deeban Chakravarthy, V., Ramana, K., Maddikunta, P. K. R., Xin, Q., & Surya Narayana, G. (2022). OTP-ER: An ordered transmission paradigm for effective routing in IoT based wireless sensor networks. Optical and Quantum Electronics, 54(7), 1–15.

    Article  Google Scholar 

  33. Dogra, R., Rani, S., Shafi, J., Kim, S., & Ijaz, M. F. (2022). ESEERP: Enhanced smart energy efficient routing protocol for Internet of Things in wireless sensor nodes. Sensors, 22(16), 6109.

    Article  Google Scholar 

  34. Haque, I., & Saha, D. (2021). SoftIoT: A resource-aware SDN/NFV-based IoT network. Journal of Network and Computer Applications, 193, 103208.

    Article  Google Scholar 

  35. Jothikumar, C., Ramana, K., Chakravarthy, V. D., Singh, S., & Ra, I. H. (2021). An efficient routing approach to maximize the lifetime of IoT-based wireless sensor networks in 5G and beyond. Mobile Information Systems. https://doi.org/10.1155/2021/9160516

    Article  Google Scholar 

  36. Liu, Y., Liu, A., Zhang, N., Liu, X., Ma, M., & Hu, Y. (2019). DDC: Dynamic duty cycle for improving delay and energy efficiency in wireless sensor networks. Journal of Network and Computer Applications, 131, 16–27.

    Article  Google Scholar 

Download references

Funding

No fund received for this project.

Author information

Authors and Affiliations

Authors

Contributions

All authors are approved for this work.

Corresponding author

Correspondence to Neeraj Chandnani.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chandnani, N., Khairnar, C.N. A Novel Hybrid Protocol in Achieving QoS Regarding Data Aggregation and Dynamic Traffic Routing in IoT WSNs. Wireless Pers Commun 131, 295–335 (2023). https://doi.org/10.1007/s11277-023-10429-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-023-10429-w

Keywords

Navigation