iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/s41650-018-0030-5
Channel List Selection Based on Quality Prediction in WirelessHART Networks | Journal of Communications and Information Networks Skip to main content
Springer Nature Link
Log in

Channel List Selection Based on Quality Prediction in WirelessHART Networks

  • Research paper
  • Published:
Journal of Communications and Information Networks

Abstract

WirelessHART is one of the most widely used technologies in industrial wireless networks. However, its performance is highly influenced by the quality of wireless channels. To improve the reliability of wireless communications, WirelessHART employs channel blacklisting and channel hopping mechanisms, which highlights the importance of channel assessment. Traditional methods generally resort to packet reception ratio (PRR) of the previous time slot to assess and allocate channels, but this is not accurate. In this paper, we propose a learning-based framework for predicting the PRR, and on the basis of the predicted PRR, we develop a heuristic channel selection algorithm to confirm the channel list, which takes into account the balance of channel diversity and route diversity. Simulation results demonstrate that our algorithm outperforms existing ones in terms of achieved reliability.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. IEC 62591: Industrial communication networks–wireless communication network and communication profiles–WirelessHART [S]. IEC, 2016.

  2. K. A. Agha, M. H. Bertin, T. Dang, et al. Which wireless technology for industrial wireless sensor networks? The development of OCARI technology [J]. IEEE Transactions on Industrial Electronics, 2009, 56(10): 4266–4278.

    Article  Google Scholar 

  3. IEEE Standard for Low-Rate Wireless Networks, IEEE Std 802.15.4-2015 (Revision of IEEE Std 802.15.4-2011) [S]. 2016: 1–709.

  4. B. Martinez, X. Vilajosana, F. Chraim, et al. When scavengers meet industrial wireless [J]. IEEE Transactions on Industrial Electronics, 2015, 62(5): 2994–3003.

    Article  Google Scholar 

  5. X. Zhu, P. C. Huang, J. Meng, et al. ColLoc: A collaborative location and tracking system on WirelessHART [J]. ACM Transactions on Embedded Computing Systems, 2014, 13(4): 125.

    Google Scholar 

  6. S. Vitturi, F. Tramarin, L. Seno. Industrial wireless networks: The significance of timeliness in communication systems [J]. IEEE Industrial Electronics Magazine, 2013, 7(2): 40–51.

    Article  Google Scholar 

  7. D. Gunatilaka, M. Sha, C. Lu. Impacts of channel selection on industrial wireless sensor-actuator networks [J]. IEEE International Conference on Computer Communications, Atlanta, 2017: 1–9.

    Google Scholar 

  8. D. Öhmann, A. Awada, I. Viering, et al. SINR model with best server association for high availability studies of wireless networks [J]. IEEE Wireless Communications Letters, 2016, 5(1): 60–63.

    Article  Google Scholar 

  9. S. A. Kim, D. G. An, H. Ryu, et al. Efficient SNR estimation in OFDM system [C]//IEEE Radio and Wireless Symposium, Phoenix, 2011: 182–185.

    Google Scholar 

  10. H. Li, L. Chen. RSSI-aware energy saving for large file downloading on smartphones [J]. IEEE Embedded Systems Letters, 2015, 7(2): 63–66.

    Article  Google Scholar 

  11. X. Ma. Packet reception ratios in two-dimensional broadcast ad hoc networks [C]//International Conference on Computing, Networking and Communications (ICNC), Maui, 2012: 412–416.

    Google Scholar 

  12. E. N. Gilbert. Capacity of a burst-noise channel [J]. The Bell System Technical Journal, 1960, 39(5): 1253–1265.

    Article  MathSciNet  Google Scholar 

  13. A. Willig, M. Kubisch, C. Hoene. Measurements of a wireless link in an industrial environment using an IEEE 802.11-compliant physical layer [J]. IEEE Transactions on Industrial Electronics, 2002, 49(6): 1265–1282.

    Article  Google Scholar 

  14. E. Tanghe, W. Joseph, L. Verloock, et al. The industrial indoor channel: large-scale and temporal fading at 900, 2 400, and 5 200 MHz [J]. IEEE Transactions on Wireless Communications, 2008, 7(7): 2740–2751.

    Article  Google Scholar 

  15. W. Lee, H. Kim. Channel quality estimation for improving awareness of communication situation in the 2.4GHz ISM band [J]. IEEE Transactions on Mobile Computing, 2018, 17(9): 2002–2013.

    Article  Google Scholar 

  16. Y. Lin, K. Zhou, J. Li. Application of cloud model in rock burst prediction and performance comparison with three machine learning algorithms [J]. IEEE Access, 2018, 6(30): 958–968.

    Google Scholar 

  17. Y. Yu, V. Jindal, F. Bastani. Improving the smartness of cloud management via machine learning based workload prediction [C]//IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, 2018: 38–44.

    Google Scholar 

  18. J. Riihijarvi, P. Mahonen. Machine learning for performance prediction in mobile cellular networks [J]. IEEE Computational Intelligence Magazine, 2018, 13(1): 51–60.

    Article  Google Scholar 

  19. D. Wei, B. Wang, G. Lin, et al. Research on unstructured text data mining and fault classification based on RNN-LSTM with malfunction inspection report [J]. Energies, 2017, 10(3): 406.

    Article  Google Scholar 

  20. X. Cheng, L. Fang, L. Yang, et al. Mobile big data: The fuel for datadriven wireless [J]. IEEE Internet of Things Journal, 2017, 4(5): 1489–1516.

    Article  Google Scholar 

  21. L. Lei, Y. Kuang, X. S. Shen, et al. Optimal reliability in energy harvesting industrial wireless sensor networks [J]. IEEE Transactions on Wireless Communications, 2016, 15(8): 5399–5413.

    Article  Google Scholar 

  22. X. Cao, R. Ma, L. Liu, et al. A machine learning based algorithm for joint scheduling and power control in wireless networks [J]. IEEE Internet of Things Journal, 2018.

    Google Scholar 

  23. X. Cheng, L. Fang, X. Hong, et al. Exploiting mobile big data: Sources, features, and applications [J]. IEEE Network, 2017, 31(1): 72–79.

    Article  Google Scholar 

  24. L. Liu, B. Yin, S. Zhang, et al. Deep learning meets wireless network optimization: Identify critical links [J]. IEEE Transactions on Network Science and Engineering, 2018.

    Google Scholar 

  25. A. Azzouni, G. Pujolle. A long short-term memory recurrent neural network framework for network traffic matrix prediction [J]. CoRR, 2017, abs/1705.05690.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Southeast University, Nanjing, 210096, China

    Gongpu Chen, Rui Ma, Mengdan Lei & Xianghui Cao

Authors
  1. Gongpu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mengdan Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xianghui Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rui Ma.

Additional information

This work was supported in part by the National Natural Science Foundation of China (No. 61573103), the State Key Laboratory of Synthetical Automation for Process Industries, and the Fundamental Research Funds for the Central Universities. The associate editor coordinating the review of this paper and approving it for publication was X. Cheng.

Gongpu Chen received his B.S. degree in automation engineering from University of Electronic Science and Technology of China, Chengdu, China, in 2016. He is currently working towards his M.S. degree in Control Science and Engineering in Southeast University, Nanjing, China. His research interests include cyber physical system and network resource allocation.

Rui Ma [corresponding author] received her B.S. degree in automation engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2016. She is currently working towards her M.S. degree in Control Science and Engineering in Southeast University, Nanjing, China. Her research interests include machine learning and network scheduling.

Mengdan Lei received her B.S. degree in electrical engineering and automation from Soochow University of Rail Transportation, Suzhou, China, in 2017. She is currently working towards her M.S. degree in Control Science and Engineering in Southeast University, Nanjing, China. Her research interests include cyber physical system and network security.

Xianghui Cao (S’08-M’11-SM’16) received his B.S. and Ph.D. degrees in control science and engineering from Zhejiang University, Hangzhou, China, in 2006 and 2011, repectively. From 2012 to 2015, he was a Senior Research Associate with the Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA. He is currently an Associate Professor with the School of Automation, Southeast University, Nanjing, China. His current research interests include cyber-physical systems, wireless network performance analysis, wireless networked control, and network security. Dr. Cao was a recipient of the Best Paper Runner-Up Award of ACM MobiHoc14. He also serves as an Associate Editor for several journals, including Acta Auromatica Sinica, IEEE/CAA Journal of Automatica Sinica, KSII Transactions on Internet and Information Systems, Security and Communication Networks, and International Journal of Ad Hod and Ubiquitous Computing. He served as the Publicity Co-Chair for ACM MobiHoc15, the Symposium Co-Chair for ICNC17 and IEEE/CIC ICCC15, and has been a TPC member for a number of conferences.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, G., Ma, R., Lei, M. et al. Channel List Selection Based on Quality Prediction in WirelessHART Networks. J. Commun. Inf. Netw. 3, 49–56 (2018). https://doi.org/10.1007/s41650-018-0030-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41650-018-0030-5

Keywords

Search

Navigation