Skip to main content
SpringerLink
Log in

Hybrid Event-Time-Driven Communication and Network Optimization

  • Chapter
  • First Online:
Book cover Introduction to Hybrid Intelligent Networks

Abstract

In sensor networks (SNs), how to allocate the limited resources so as to optimize data gathering and network utility is an important and challenging task. This chapter introduces a hybrid event-time-driven communication and updating scheme, with which sensor network optimization problems can be solved. A distributed hybrid driven optimization algorithm based on the coordinate descent method is presented. The proposed optimization algorithm differs from the existing ones since the hybrid driven scheme allows more choices of actuation time, resulting a tradeoff between communications and computation performance. Applying the proposed algorithm, each sensor node is driven in a hybrid event time manner, which removes the requirement of strict time synchronization. The convergence and optimality of the proposed algorithm are analyzed, and verified by simulation examples. The developed results also show the tradeoff between communications and computation performance.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 84.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. J. A. Stankovic, “Research directions for the Internet of Things,” IEEE Internet Things J., vol. 1, no. 1, pp. 3–9, 2014.

    Article  MathSciNet  Google Scholar 

  2. J. Qin, Q. Ma, Y. Shi, and L. Wang, “Recent advances in consensus of multi-agent systems: A brief survey,” IEEE Trans. Ind. Electron., vol. 64, no. 6, pp. 4972–4983, 2017.

    Article  Google Scholar 

  3. Y. Zhang, S. He, and J. Chen, “Data gathering optimization by dynamic sensing and routing in rechargeable sensor networks,” IEEE/ACM Trans. Netw., vol. 24, no. 3, pp. 1632–1646, 2016.

    Article  Google Scholar 

  4. H. Gao, W. Zhan, H. R. Karimi, X. Yang, and S. Yin, “Allocation of actuators and sensors for coupled-adjacent-building vibration attenuation,” IEEE Trans. Ind. Electron., vol. 60, no. 12, pp. 5792–5801, 2013.

    Article  Google Scholar 

  5. L. Zheng and L. Cai, “A distributed demand response control strategy using Lyapunov optimization,” IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 2075–2083, 2014.

    Article  Google Scholar 

  6. P. Wan and M. D. Lemmon, “Event-triggered distributed optimization in sensor networks,” in Proc. Int. Conf. Inf. Process. Sensor Netw. (IPSN ’09), 2009, pp. 49–60.

    Google Scholar 

  7. S. S. Ram, A. Nedic, and V. V. Veeravalli, “Incremental stochastic subgradient algorithms for convex optimization,” SIAM J. Optimization, vol. 20, no. 2, pp. 691–717, 2009.

    Article  MathSciNet  Google Scholar 

  8. G. Shi and K. H. Johansson, “Randomized optimal consensus of multi-agent systems,” Automatica, vol. 48, no. 12, pp. 3018–3030, 2012.

    Article  MathSciNet  Google Scholar 

  9. C. Eksin and A. Ribeiro, “Distributed network optimization with heuristic rational agents,” IEEE Trans. Signal Process., vol. 60, no. 10, pp. 5396–5411, 2012.

    Article  MathSciNet  Google Scholar 

  10. E. Wei and A. Ozdaglar, “Distributed alternating direction method of multipliers,” in Proc. 51st IEEE Conf. Decision Control, 2012, pp. 5445–5450.

    Google Scholar 

  11. D. P. Bertsekas, “Incremental proximal methods for large scale convex optimization,” Mathematical Program., vol. 129, no. 2, pp. 163–195, 2011.

    Article  MathSciNet  Google Scholar 

  12. L. Xiao and S. Boyd, “Fast linear iterations for distributed averaging,” Syst. & Contr. Lett., vol. 53, no. 1, pp. 65–78, 2004.

    Article  MathSciNet  Google Scholar 

  13. L. Carlone, V. Srivastava, F. Bullo, and G. C. Calafiore, “Distributed random convex programming via constraints consensus,” SIAM J. Contr. Optimization, vol. 52, no. 1, pp. 629–662, 2014.

    Article  MathSciNet  Google Scholar 

  14. C. G. Cassandras, “The event-driven paradigm for control, communication and optimization,” J. Contr. Decision, vol. 1, no. 1, pp. 3–17, 2014.

    Article  Google Scholar 

  15. H. Li, X. Liao, T. Huang, and W. Zhu, “Event-triggering sampling based leader-following consensus in second-order multi-agent systems,” IEEE Trans. Autom. Control, vol. 60, no. 7, pp. 1998–2003, 2015.

    Article  MathSciNet  Google Scholar 

  16. W. Hu, L. Liu, and G. Feng, “Consensus of linear multi-agent systems by distributed event-triggered strategy,” IEEE Trans. Cybern., vol. 46, no. 1, pp. 148–157, 2016.

    Article  Google Scholar 

  17. S. Wen, G. Guo, and W. S. Wong, “Hybrid event-time-triggered networked control systems: Scheduling-event-control co-design,” Inform. Sci., vol. 305, pp. 269–284, 2015.

    Article  MathSciNet  Google Scholar 

  18. D. V. Dimarogonas, E. Frazzoli, and K. H. Johansson, “Distributed event-triggered control for multi-agent systems,” IEEE Trans. Autom. Control, vol. 57, no. 5, pp. 2735–2750, 2012.

    Article  MathSciNet  Google Scholar 

  19. Z. H. Guan, B. Hu, M. Chi, D. X. He, and X. M. Cheng, “Guaranteed performance consensus in second-order multi-agent systems with hybrid impulsive control,” Automatica, vol. 50, no. 9, pp. 2415–2418, 2014.

    Article  MathSciNet  Google Scholar 

  20. S. S. Kia, J. Cortes, and S. Martinez, “Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication,” Automatica, vol. 55, pp. 254–264, 2015.

    Article  MathSciNet  Google Scholar 

  21. B. Min and K. I. Goh, “Multiple resource demands and viability in multiplex networks,” Physical Review E, vol. 89, no. 4, pp. 040802, 2014.

    Google Scholar 

  22. K. W. Chang, C. J. Hsieh, and C. J. Lin, “Coordinate descent method for large-scale L2-loss linear support vector machines,” J. Machine Learn. Research, vol. 9, pp. 1369–1398, 2008.

    MathSciNet  MATH  Google Scholar 

  23. P. Tseng, “Convergence of a block coordinate descent method for nondifferentiable minimization,” J. Optim. Theory Appl., vol. 109, no. 3, pp. 475–494, 2001.

    Article  MathSciNet  Google Scholar 

  24. C. Li, X. Yu, W. Yu, T. Huang, and Z. W. Liu, “Distributed event-triggered scheme for economic dispatch in smart grids,” IEEE Trans. Ind. Informat., vol. 12, no. 5, pp. 1775–1785, 2016.

    Article  Google Scholar 

  25. J. Wu, Q. S. Jia, K. H. Johansson, and L. Shi, “Event-based sensor data scheduling: Trade-off between communication rate and estimation quality,” IEEE Trans. Autom. Control, vol. 58, no. 4, pp. 1041–1046, 2013.

    Article  MathSciNet  Google Scholar 

  26. H. Dong, Z. Wang, and H. Gao, “Distributed H∞ filtering for a class of Markovian jump nonlinear time-delay systems over lossy sensor networks,” IEEE Trans. Ind. Electron., vol. 60, no. 10, pp. 4665–4672, 2013.

    Article  Google Scholar 

  27. M. Zhong and C. G. Cassandras, “Asynchronous distributed optimization with event-driven communication,” IEEE Trans. Autom. Control, vol. 55, no. 12, pp. 2735–2750, 2010.

    Article  MathSciNet  Google Scholar 

  28. D. P. Bertsekas and J. N. Tsitsiklis, Parallel and distributed computation: Numerical methods (Chapter 7 ), Belmont, MA: Athena Scientific, 1997.

    Google Scholar 

  29. X. Liu, Z. Gao, and M. Z. Q. Chen, “Takagi-Sugeno fuzzy model based fault estimation and signal compensation with application to wind turbines,” IEEE Trans. Ind. Electron., vol. 64, no. 7, pp. 5678–5689, 2017.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Zhi-Hong Guan

  2. Wuhan National Laboratory For Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Bin Hu

  3. Electrical and Computer Engineering Department, University of Waterloo, Waterloo, ON, Canada

    Xuemin (Sherman) Shen

Authors
  1. Zhi-Hong Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuemin (Sherman) Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Guan, ZH., Hu, B., Shen, X.(. (2019). Hybrid Event-Time-Driven Communication and Network Optimization. In: Introduction to Hybrid Intelligent Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-02161-0_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-02161-0_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02160-3

  • Online ISBN: 978-3-030-02161-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation