Skip to main content
Springer Nature Link
Log in

Cross-layer congestion control of wireless sensor networks based on fuzzy sliding mode control

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Wireless sensor networks (WSNs) act as a building block of Internet of Things and have been used in various applications to sense environment and transmit data to the Internet. However, WSNs are very vulnerable to congestion problem, resulting in higher packet loss ratio, longer delay and lower throughput. To address this issue, this paper presents a fuzzy sliding mode congestion control algorithm (FSMC) for WSNs. Firstly, by applying the signal-to-noise ratio of wireless channel to TCP model, a new cross-layer congestion control model between transmission layer and MAC layer is proposed. Then, by combining fuzzy control with sliding mode control (SMC), a fuzzy sliding mode controller (FSMC) is designed, which adaptively regulates the queue length of buffer in congested nodes and significantly reduces the impact of external uncertain disturbance. Finally, numerous simulations are implemented in MATLAB/Simulink and NS-2.35 by comparing with traditional control strategies such as fuzzy, PID and SMC, which show that the proposed FSMC effectively adapts to the change of queue length and has good performance, such as rapid convergence, lower average delay, less packet loss ratio and higher throughput.

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

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Mittal N, Singh U, Sohi BS (2019) An energy-aware cluster-based stable protocol for wireless sensor networks. Neural Comput Appl 31:7269–7286

    Article  Google Scholar 

  2. Zhao M, Tian Z, Chow TW (2019) Fault diagnosis on wireless sensor network using the neighborhood kernel density estimation. Neural Comput Appl 31:4019–4030

    Article  Google Scholar 

  3. Binh HTT, Hanh NT, Dey N (2018) Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks. Neural Comput Appl 30:2305–2317

    Article  Google Scholar 

  4. Wu B, Luo J, Yang C (2018) Wireless sensor network minimum beacon set selection algorithm based on tree model. Neural Comput Appl 30:965–976

    Article  Google Scholar 

  5. Sergiou C, Antoniou P, Vassiliou V (2014) A comprehensive survey of congestion control protocols in wireless sensor networks. IEEE Commun Surv Tutor 16:1839–1859

    Article  Google Scholar 

  6. Jan MA, Jan SRU, Alam M, Akhunzada A, Rahman IU (2018) A comprehensive analysis of congestion control protocols in wireless sensor networks. Mobile Netw Appl 23:456–468

    Article  Google Scholar 

  7. Javaid S, Fahim H, Hamid Z, Hussain FB (2018) Traffic-aware congestion control (TACC) for wireless multimedia sensor networks. Multimed Tools Appl 77:4433–4452

    Article  Google Scholar 

  8. Alipio MI, Tiglao NMC (2018) RT-CaCC: a reliable transport with cache-aware congestion control protocol in wireless sensor networks. IEEE Trans Wirel Commun 17:4607–4619

    Article  Google Scholar 

  9. Zhuang Y, Yu L, Shen H, Kolodzey W, Iri N, Caulfield G, He S (2018) Data collection with accuracy-aware congestion control in sensor networks. IEEE Trans Mob Comput 18:1068–1082

    Article  Google Scholar 

  10. Sonmez C, Incel OD, Isik S, Donmez MY, Ersoy C (2014) Fuzzy-based congestion control for wireless multimedia sensor networks. EURASIP J Wirel Commun Netw 2014:63

    Article  Google Scholar 

  11. Yang X, Chen X, Xia R, Qian Z (2018) Wireless sensor network congestion control based on standard particle swarm optimization and single neuron PID. Sensors 18:1265

    Article  Google Scholar 

  12. Kafi MA, Djenouri D, Ben-Othman J, Badache N (2014) Congestion control protocols in wireless sensor networks: a survey. IEEE Commun Surv Tutor 16:1369–1390

    Article  Google Scholar 

  13. Nikokheslat HD, Ghaffari A (2017) Protocol for controlling congestion in wireless sensor networks. Wirel Pers Commun 95:3233–3251

    Article  Google Scholar 

  14. Kafi MA, Ben-Othman J, Ouadjaout A, Bagaa M, Badache N (2017) REFIACC: reliable, efficient, fair and interference-aware congestion control protocol for wireless sensor networks. Comput Commun 101:1–11

    Article  Google Scholar 

  15. Li M-W, Jing Y-W, Chen X-Y (2012) Cross-layer congestion control for wireless sensor network based on sliding mode variable structure. Control Decis 27:451–454

    MathSciNet  Google Scholar 

  16. Hollot CV, Misra V, Towsley D, Gong W (2002) Analysis and design of controllers for AQM routers supporting TCP flows. IEEE Trans Autom Control 47:945–959

    Article  MathSciNet  Google Scholar 

  17. Ma T, Yu T, Huang J, Yang X, Gu Z (2020) Adaptive odd impulsive consensus of multi-agent systems via comparison system method. Nonlinear Ana Hybrid Syst 35:100824

    Article  MathSciNet  Google Scholar 

  18. Ma T, Cui B, Wang Y, Liu K (2019) Stochastic synchronization of delayed multiagent networks with intermittent communications: an impulsive framework. Int J Robust Nonlinear Control 29:4537–4561

    Article  MathSciNet  Google Scholar 

  19. Fu J, Bai J, Lai J, Li P, Yu M, Lam H-K (2019) Adaptive fuzzy control of a magnetorheological elastomer vibration isolation system with time-varying sinusoidal excitations. J Sound Vib 456:386–406

    Article  Google Scholar 

  20. Fu J, Dai Z, Yang Z, Lai J, Yu M (2019) Time delay analysis and constant time-delay compensation control for MRE vibration control system with multiple-frequency excitation. Smart Mater Struct 29:014001

    Article  Google Scholar 

  21. Liu J, Wang X (2012) Advanced sliding mode control for mechanical systems. Springer, Berlin

    MATH  Google Scholar 

  22. Azar AT, Zhu Q (2015) Advances and applications in sliding mode control systems. Springer, Cham

    Book  Google Scholar 

  23. Biddut MJH, Islam N, Arif MFH, Rahman MS (2016) On the analysis of RED algorithm in ZigBee network for queue management. In: 2016 5th international conference on informatics, electronics and vision (ICIEV), pp 408–412

  24. Rastogi S, Zaheer H (2016) Comparative analysis of queuing mechanisms: Droptail, RED and NLRED. Soc Netw Anal Min 6:70

    Article  Google Scholar 

  25. Rajaram ML, Kougianos E, Mohanty SP, Choppali U (2016) Wireless sensor network simulation frameworks: a tutorial review: MATLAB/Simulink bests the rest. IEEE Consum Electron Mag 5:63–69

    Article  Google Scholar 

  26. Gholipour M, Haghighat A, Meybodi MR (2018) Congestion avoidance in cognitive wireless sensor networks using TOPSIS and response surface methodology. Telecommun Syst 67:519–537

    Article  Google Scholar 

  27. Zhao L, Qu S, Yi Y (2018) A modified cluster-head selection algorithm in wireless sensor networks based on LEACH. EURASIP J Wirel Commun Netw 2018:287

    Article  Google Scholar 

  28. Singh K, Singh K, Aziz A (2018) Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm. Comput Netw 138:90–107

    Article  Google Scholar 

Download references

Acknowledgements

This work was financially supported by National Natural Science Foundation of China (Grant Nos. 61673190/F030101), self-determined research funds of CCNU from the colleges’ basic research and operation of MOE (CCNU 18TS042), and Graduate Innovation Program of CCNU (2019CXZZ102).

Author information

Authors and Affiliations

  1. Department of Electronics and Information Engineering, College of Physical Science and Technology, Central China Normal University, Wuhan, 430079, China

    Shaocheng Qu, Liang Zhao & Zhili Xiong

  2. School of Electric Information, Huanggang Normal University, Huanggang, 438000, China

    Zhili Xiong

Authors
  1. Shaocheng Qu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Liang Zhao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Zhili Xiong
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Zhili Xiong.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest. The article is considered for publication on the understanding that the article neither has been published nor will be published anywhere else before being published in the journal of Neural Computing and Applications.

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

Qu, S., Zhao, L. & Xiong, Z. Cross-layer congestion control of wireless sensor networks based on fuzzy sliding mode control. Neural Comput & Applic 32, 13505–13520 (2020). https://doi.org/10.1007/s00521-020-04758-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04758-1

Keywords