Skip to main content

Advertisement

SpringerLink
Log in

Data Congestion Control Using Offloading in IoT Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Internet of Things (IoT) is being used by a large number of applications and transmitting huge amounts of data. IPv6 routing protocol for low power and lossy networks (RPL) is being standardized for routing in IoT networks. However, it is difficult to handle such huge transmission as it is initially designed for Low power and lossy networks. In this paper, we present the mechanism for the reduction of overhead from the congested parent node by offloading its partial load. For offloading the packet, a suitable neighbor is selected based on its status of energy, buffer, link quality, number of child nodes, and distance. This approach focuses on the enhancement of RPL by including the mechanism for congestion control. The approach reduces the delay and packet loss rate while avoiding congestion in a suitable manner. The proposed approach is beneficial in terms of throughput and packet receiving ratio as compared to the comparative approaches.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Data Availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Code Availability

Code is available.

References

  1. Da Li, Xu., He, Wu., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on Industrial Informatics, 10(4), 2233–2243. https://doi.org/10.1109/TII.2014.2300753

    Article  Google Scholar 

  2. Gungor, V. C., & Hancke, G. P. (2009). Industrial wireless sensor networks: Challenges, design principles, and technical approaches. IEEE Transactions on industrial electronics, 56(10), 4258–4265.

    Article  Google Scholar 

  3. Kushalnagar, N., Gabriel, M., & Christian, S., (2007) IPv6 over low-power wireless personal area networks (6LoWPANs): overview, assumptions, problem statement, and goals. 1–11.

  4. Kim, H.-S., Im, H., Lee, M.-S., Paek, J., & Bahk, S. (2015). A measurement study of TCP over RPL in low-power and lossy networks. Journal of Communications and Networks, 17(6), 647–655. https://doi.org/10.1109/JCN.2015.000111

    Article  Google Scholar 

  5. Winter, T., et al. (2012). RPL: IPv6 routing protocol for low-power and lossy networks. rfc, 6550, 1–157.

    Google Scholar 

  6. Mayzaud, A., Badonnel, R., & Chrisment, I. (2017). A distributed monitoring strategy for detecting version number attacks in RPL-based networks. IEEE Transactions on Network and Service Management, 14(2), 472–486. https://doi.org/10.1109/TNSM.2017.2705290

    Article  Google Scholar 

  7. Kim, H.-S., Kim, H., Paek, J., & Bahk, S. (2017). Load balancing under heavy traffic in RPL routing protocol for low power and lossy networks. IEEE Transactions on Mobile Computing, 16(4), 964–979. https://doi.org/10.1109/TMC.2016.2585107

    Article  Google Scholar 

  8. Sheng, Z., Yang, S., Yifan, Y., Vasilakos, A., Mccann, J., & Leung, K. (2013). A survey on the IETF protocol suite for the internet of things: Standards, challenges, and opportunities. IEEE Wireless Communications, 20(6), 91–98. https://doi.org/10.1109/MWC.2013.6704479

    Article  Google Scholar 

  9. Liu, X., Sheng, Z., Yin, C., Ali, F., & Roggen, D. (2017). Performance analysis of routing protocol for low power and lossy networks (RPL) in large scale networks. IEEE Internet of Things Journal, 4(6), 2172–2185. https://doi.org/10.1109/JIOT.2017.2755980

    Article  Google Scholar 

  10. Taghizadeh, S., Bobarshad, H., & Elbiaze, H. (2018). CLRPL: Context-aware and load balancing RPL for IoT networks under heavy and highly dynamic load. IEEE Access, 6, 23277–23291.

    Article  Google Scholar 

  11. Khadak Bhandari, A., & Hosen, G. C. (2018). CoAR: Congestion-aware routing protocol for low power and lossy networks for IoT applications. Sensors, 18(11), 3838. https://doi.org/10.3390/s18113838

    Article  Google Scholar 

  12. Tang, W., Ma, X., Huang, J., & Wei, J. (2016). Toward improved RPL: A congestion avoidance multipath routing protocol with time factor for wireless sensor networks. Journal of Sensors, 2016, 1–11. https://doi.org/10.1155/2016/8128651

    Article  Google Scholar 

  13. Ullah, R., Faheem, Y., & Kim, B.-S. (2017). Energy and congestion-aware routing metric for smart grid AMI networks in smart city. IEEE access, 5, 13799–13810.

    Article  Google Scholar 

  14. Al-Kashoash, Hayder, AA, Yaarob A-N., & Andrew HK., (2016) Congestion-aware RPL for 6L0WPAN networks. In 2016 Wireless Telecommunications Symposium (WTS), IEEE.

  15. Farag, H, Cedomir, S (2021) Congestion-aware routing in dynamic IoT networks: A reinforcement learning approach. arXiv preprint arXiv:2105.09678.

  16. Maheshwari, A., & Rajesh Kumar, Y. (2020) Analysis of congestion control mechanism for iot. In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE.

  17. Gaddour, O., & Koubâa, A. (2012). RPL in a nutshell: A survey. Computer Networks, 56(14), 3163–3178.

    Article  Google Scholar 

  18. Palattella, M. (2013). Standardized protocol stack for the Internet of (important) Things. IEEE Communication Surveys Tutorials., 15, 1389–1406.

    Article  Google Scholar 

  19. Contiki, N., (Accessed on 20/11/2019). [Online] Available: http://anrg.usc.edu/contiki/index.php/Network_Stack.

  20. Ali, R., Shahin, N., Zikria, Y. B., Kim, B.-S., & Kim, S. W. (2019). Deep reinforcement learning paradigm for performance optimization of channel observation–based MAC protocols in dense WLANs. IEEE Access, 7, 3500–3511. https://doi.org/10.1109/ACCESS.2018.2886216

    Article  Google Scholar 

  21. Shreyas, J., Singh, H., Soumya Tiwari, N. N., Srinidhi, S. M., & Kumar, D. (2021). CAFOR: Congestion avoidance using fuzzy logic to find an optimal routing path in 6LoWPAN networks. Journal of Reliable Intelligent Environments, 7(4), 325–340. https://doi.org/10.1007/s40860-021-00134-5

    Article  Google Scholar 

Download references

Funding

The authors declare that they have competing interests and funding.

Author information

Authors and Affiliations

  1. Delhi Technological University, Delhi, 110042, India

    Aastha Maheshwari & Rajesh K. Yadav

  2. H N B Garhwal University, Uttrakhand, 246174, India

    Prem Nath

Authors
  1. Aastha Maheshwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajesh K. Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prem Nath
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Aastha Maheshwari or Rajesh K. Yadav.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maheshwari, A., Yadav, R.K. & Nath, P. Data Congestion Control Using Offloading in IoT Network. Wireless Pers Commun 125, 2147–2166 (2022). https://doi.org/10.1007/s11277-022-09649-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09649-3

Keywords

Search

Navigation