Circuits, Systems, and Signal Processing

, Volume 39, Issue 1, pp 154–174 | Cite as

Nonmonotonic-Based Congestion Control Schemes for a Delayed Nonlinear Network

  • Shoorangiz Shams Shamsabad FarahaniEmail author
  • Siavash Fakhimi Derakhshan


In this paper, buffer dynamic modeling for wireless sensor networks as a highly nonlinear system is accomplished in discrete time, different subsystems are achieved based on delay, and the overall model is gained by blending them. According to nonlinear dynamics point of view, considering delay in the analysis of congestion control schemes is of paramount importance. In this paper, an adaptive back-off interval selection works with the proposed robust controller. Based on queue utilization and channel estimation algorithm, congestion is detected and a suitable rate is selected by adaptive back-off interval selection. An augmented form of our proposed system is utilized for controller synthesis. A new approach is proposed for controller synthesis based on non-quadratic and common quadratic Lyapunov candidates where the former is generalized to be more relaxed. Also, the monotonicity requirement of Lyapunov’s theorem is relaxed. The closed-loop systems are globally asymptotically stable in case of delay changes resulted from queue size changes. Extended simulation results confirm the effectiveness of our proposed schemes.


Wireless sensor networks (WSNs) Congestion control Controller synthesis Non-quadratic Lyapunov stability Linear matrix inequality (LMI) Globally asymptotically stable (GAS) 



The authors gratefully acknowledge the financial and other support of this research, provided by the Islamic Azad university Islamshahr branch, Islamshahr, Iran.


  1. 1.
    M. Akar, U. Ozguner, Decentralized techniques for the analysis and control of Takagi-Sugeno fuzzy systems. IEEE Trans Fuzzy Syst 8, 691–704 (2000)CrossRefGoogle Scholar
  2. 2.
    P. Antoniou, A. Pitsillides, T. Blackwell, A. Engelbrecht, L. Michael, Congestion control in wireless sensor networks based on bird flocking behavior. Comput Netw 57, 1167–1191 (2013)CrossRefGoogle Scholar
  3. 3.
    S. Boyd, L.E. Ghaoui, E. Feron, V. Balakrishnan, Linear matrix inequalities in system and control theory, 15 (SIAM, Philadelphia, 1994)CrossRefGoogle Scholar
  4. 4.
    Chand T, Sharma B, Kour M (2015) TRCCTP: a traffic redirection-based congestion control transport protocol for wireless sensor networks. In: IEEE sensors 1–4Google Scholar
  5. 5.
    Y. Chen, Z. Wang, Y. Yuan, P. Date, Distributed H∞ filtering for switched stochastic delayed systems over sensor networks with fading measurements. IEEE Trans Cybern 99, 1–13 (2018)Google Scholar
  6. 6.
    Dubey K, Sinha A (2015) Congestion control for self-similar traffic in wireless sensor network. In: Eighth international conference on contemporary computing (IC3), pp 331–335Google Scholar
  7. 7.
    S. Fakhimi Derakhshan, A. Fathehi, Non monotonic Lyapunov functions for stability analysis and stabilization of discrete time Takagi-Sugeno fuzzy systems. Int J Innov Comput Inf Control 10, 1567–1586 (2014)Google Scholar
  8. 8.
    S.S.S. Farahani, M.R. Jahed-Motlagh, M.A. Nekoui, Novel congestion control algorithms for a class of delayed networks. Turk J Electr Eng Comput Sci 23, 824–840 (2015)CrossRefGoogle Scholar
  9. 9.
    S.S.S. Farahani, M.R. Jahed-Motlagh, M.A. Nekoui, S.V. Azhari, Robust decentralized adaptive nonquadratic congestion control algorithm for a class of delayed networks. Nonlinear Dyn 73, 2291–2311 (2013)MathSciNetCrossRefGoogle Scholar
  10. 10.
    G. Feng, C.L. Chen, D. Sun, X.P. Guan, H-infinity controller synthesis of fuzzy dynamic systems based on piecewise Lyapunov functions and bilinear matrix inequalities. IEEE Trans Fuzzy Syst 13, 94–103 (2005)CrossRefGoogle Scholar
  11. 11.
    Y. Gao, F. Xiao, J. Liu, R. Wang, Distributed soft fault detection for interval type-2 fuzzy-model-based stochastic systems with wireless sensor networks. IEEE Trans Ind Inf 15, 334–347 (2019)CrossRefGoogle Scholar
  12. 12.
    S.H. Ghwanmeh, A.R. Al-Zoubidi, Wireless network performance optimization using opnet modeler. Inf Technol J 5, 18–24 (2006)CrossRefGoogle Scholar
  13. 13.
    Justus JJ, Chandra Sekar A (2016) Congestion control in wireless sensor network using hybrid epidermic and DAIPaS approach. In: 2016 international conference on inventive computation technologies (ICICT)Google Scholar
  14. 14.
    M.A. Kafi, J. Ben-Othman, A. Ouadjaout, M. Bagaa, N. Badache, REFIACC: reliable, efficient, fair and interference-aware congestion control protocol for wireless sensor networks. Comput Commun 101, 1–11 (2017)CrossRefGoogle Scholar
  15. 15.
    C. Lee, T. Jeong, S. Lian, Tournament-based congestion control protocol for multimedia streaming in ubiquitous sensor networks. Int J Commun Syst 24, 1246–1260 (2011)CrossRefGoogle Scholar
  16. 16.
    C. Liang, F. Wen, Z. Wang, Distributed parameter estimation for univariate generalized Gaussian distribution over sensor networks. Circuits Syst Signal Process 36, 1311–1321 (2017)CrossRefGoogle Scholar
  17. 17.
    W. Lu, Y. Liu, D. Wang, A distributed secure data collection scheme via chaotic compressed sensing in wireless sensor networks. Circuits Syst Signal Process 32, 1363–1387 (2013)MathSciNetCrossRefGoogle Scholar
  18. 18.
    S. Mahdizadeh Aghdam, M. Khansari, H.R. Rabiee, M. Salehi, WCCP: a congestion control protocol for wireless multimedia communication in sensor networks. Ad Hoc Netw 13, 516–534 (2014)CrossRefGoogle Scholar
  19. 19.
    S. Misra, V. Tiwari, M.S. Obaidat, LACAS: learning automata based congestion avoidance scheme for healthcare wireless sensor networks. IEEE J Sel Areas Commun 27, 466–479 (2009)CrossRefGoogle Scholar
  20. 20.
    Mozelli LA, Palhares RM (2011) Less conservative H∞ fuzzy control for discrete-time Takagi-Sugeno systems. Hindawi Publ Corp Math Prob EngGoogle Scholar
  21. 21.
    M. Padmakar Shelke, A. Malhotra, P. Mahalle, A packet priority intimation-based data transmission for congestion free traffic management in wireless sensor networks. Comput Electr Eng 64, 248–261 (2017)CrossRefGoogle Scholar
  22. 22.
    A.A. Rezaee, M.H. Yaghmaee, A.M. Rahmani, A.H. Mohajerzadeh, HOCA: healthcare aware optimized congestion avoidance and control protocol for wireless sensor networks. J Netw Comput Appl 37, 216–228 (2014)CrossRefGoogle Scholar
  23. 23.
    Z.M. Saric, D.D. Kukolj, N.D. Teslic, Acoustic source localization in wireless sensor network. Circuits Syst Signal Process 29, 837–856 (2010)CrossRefGoogle Scholar
  24. 24.
    Singh SB, Dave M, Manshahia MS (2015) Bio inspired congestion control mechanism for wireless sensor networks. In: IEEE international conference on computational intelligence and computing research (ICCIC), 1–6Google Scholar
  25. 25.
    M. Sudip, W. Isaac, M. Subhas Chandra, Guide to wireless sensor networks, Computer Communication and Network Series (Springer, London, 2009)zbMATHGoogle Scholar
  26. 26.
    Wan CY, Eisenman SB, Campbell AT (2003) CODA: congestion detection and avoidance in sensor networks. In: Proceedings of the 1st international conference on Embedded networked sensor systems SenSys ‘03, pp 266–279Google Scholar
  27. 27.
    Wang Y, Qi SZ, Chun Sun F (2004) Stability analysis and control of discrete-time fuzzy systems: a fuzzy Lyapunov function approach. In: 5th Asian control conference, pp 1855–1860Google Scholar
  28. 28.
    C. Wenguang, N. Yugang, Z. Yuanyuan, Congestion control and energy-balanced scheme based on the hierarchy for WSNs. IET Wirel Sens Syst 7(1), 1–8 (2017)CrossRefGoogle Scholar
  29. 29.
    M.H. Yaghmaee, D. Adjeroh, Priority-based rate control for service differentiation and congestion control in wireless multimedia sensor networks. Comput Netw 53, 1798–1811 (2009)CrossRefGoogle Scholar
  30. 30.
    M. Zawodniok, S. Jagannathan, Predictive congestion control protocol for wireless sensor networks. IEEE Trans Wirel Commun 6, 3955–3963 (2007)CrossRefGoogle Scholar
  31. 31.
    M. Zawodniok, S. Jagannathan, Q. Shang, Distributed power control for cellular networks in the presence of channel uncertainties. IEEE Trans Wirel Commun 5, 540–549 (2006)CrossRefGoogle Scholar
  32. 32.
    N. Zhao, Joint optimization of cooperative spectrum sensing and resource allocation in multi-channel cognitive radio sensor networks. Circuits Syst Signal Process 35, 2563–2583 (2016)CrossRefGoogle Scholar
  33. 33.
    S. Zhao, Y.S. Shmaliy, C.K. Ahn, Bias-constrained optimal fusion filtering for decentralized WSN with correlated noise sources. IEEE Trans Signal Inf Process Netw 4, 727–735 (2018)MathSciNetCrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Islamshahr BranchIslamic Azad UniversityIslamshahrIran
  2. 2.Adaptive System DepartmentInstitute of Information Theory and Automation of the Czech Academy of SciencePragueCzech Republic

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