Skip to main content
SpringerLink
Log in

Resilient Consensus for Multi-agent Networks with Mobile Detectors

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11307))

Included in the following conference series:

Abstract

This paper investigates the problem of resilient consensus for multi-agent systems under malicious attacks. Compared with most of existing works, a more flexible network topology scheme is considered, where a kind of specific agents as the mobile detectors and builders of network robustness are adopted. Specifically, the mobile agents can perceive the message of their nearby agents in the dynamic network, and acquire both in-degree and state information of each node as characteristics to judge the network state as well as communication links between nodes. It is shown that even in poor network robustness, the non-faulty agents can still achieve a consensus in finite time with the help of mobile agents. Finally, the simulation results show the effectiveness of the proposed method.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Cheng, L., Wang, Y., Ren, W., Hou, Z.G., Tan, M.: On convergence rate of leader-following consensus of linear multi-agent systems with communication noises. IEEE Trans. Autom. Control. 61(11), 3586–3592 (2016)

    Article  MathSciNet  Google Scholar 

  2. Cheng, L., Wang, Y., Ren, W., Hou, Z.G., Tan, M.: Containment control of multiagent systems with dynamic leaders based on a \(PI^{n}\)-type approach. IEEE Trans. Cybern. 46(12), 3004–3017 (2016)

    Article  Google Scholar 

  3. Zheng, Y., Ma, J., Wang, L.: Consensus of hybrid multi-agent systems. IEEE Trans. Neural Netw. Learn. Syst. 29(4), 1359–1365 (2018)

    Article  Google Scholar 

  4. Zhu, Y., Li, S., Ma, J., Zheng, Y.: Bipartite consensus in networks of agents with antagonistic interactions and quantization. IEEE Trans. Circ. Syst. II Express Briefs (2018). https://doi.org/10.1109/TCSII.2018.2811803

  5. Dolev, D., Lynch, N.A., Pinter, S.S., Stark, E.W., Weihl, W.E.: Reaching approximate agreement in the presence of faults. J. ACM (JACM) 33(3), 499–516 (1986)

    Article  MathSciNet  Google Scholar 

  6. LeBlanc, H.J., Koutsoukos, X.D.: Consensus in networked multi-agent systems with adversaries. In: 14th International Conference on Hybrid Systems: Computation and Control, pp. 281–290. ACM (2011)

    Google Scholar 

  7. Kieckhafer, R.M., Azadmanesh, M.H.: Reaching approximate agreement with mixed-mode faults. IEEE Trans. Parallel Distrib. Syst. 5(1), 53–63 (1994)

    Article  Google Scholar 

  8. LeBlanc, H.J., Zhang, H., Koutsoukos, X., Sundaram, S.: Resilient asymptotic consensus in robust networks. IEEE J. Sel. Areas Commun. 31(4), 766–781 (2013)

    Article  Google Scholar 

  9. Wu, Y., He, X., Liu, S., Xie, L.: Consensus of discrete-time multi-agent systems with adversaries and time delays. Int. J. Gen. Syst. 43(3–4), 402–411 (2014)

    Article  MathSciNet  Google Scholar 

  10. Dibaji, S.M., Ishii, H.: Resilient multi-agent consensus with asynchrony and delayed information. IFAC-Pap. OnLine 48(22), 28–33 (2015)

    Article  Google Scholar 

  11. Wu, Y., He, X.: Secure consensus control for multi-agent systems with attacks and communication delays. IEEE/CAA J. Autom. Sin. 4(1), 136–142 (2017)

    Article  MathSciNet  Google Scholar 

  12. Zhao, C., He, J., Chen, J.: Resilient consensus with mobile detectors against malicious attacks. IEEE Trans. Signal Inf. Process. Netw. 4(1), 60–69 (2018)

    Article  MathSciNet  Google Scholar 

  13. Mi, S., Han, H., Chen, C., Yan, J., Guan, X.: A secure scheme for distributed consensus estimation against data falsification in heterogeneous wireless sensor networks. Sensors 16(2), 252 (2016)

    Article  Google Scholar 

  14. Kieckhafer, R., Azadmanesh, M.: Low cost approximate agreement in partially connected networks. J. Comput. Inf. 3(1), 53–85 (1993)

    MathSciNet  Google Scholar 

  15. Vaidya, N.H., Tseng, L., Liang, G.: Iterative approximate byzantine consensus in arbitrary directed graphs. In: 2012 ACM Symposium on Principles of Distributed Computing, pp. 365–374. ACM (2012)

    Google Scholar 

  16. Zhang, H., Sundaram, S.: Robustness of information diffusion algorithms to locally bounded adversaries. In: 2012 American Control Conference (ACC 2012), pp. 5855–5861. IEEE (2012)

    Google Scholar 

  17. Zhang, H., Fata, E., Sundaram, S.: A notion of robustness in complex networks. IEEE Trans. Control. Netw. Syst. 2(3), 310–320 (2015)

    Article  MathSciNet  Google Scholar 

  18. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proc. Natl. Acad. Sci. U. S. A. 101(9), 2658–2663 (2004)

    Article  Google Scholar 

  19. Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)

    Article  Google Scholar 

  20. Pons, P., Latapy, M.: Computing communities in large networks using random walks. J. Graph Algorithms Appl. 10(2), 191–218 (2006)

    Article  MathSciNet  Google Scholar 

  21. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)

    Article  Google Scholar 

  22. Arenas, A., Duch, J., Fernández, A., Gómez, S.: Size reduction of complex networks preserving modularity. New J. Phys. 9(6), 176 (2007)

    Article  MathSciNet  Google Scholar 

  23. Ma, C.Y., Yau, D.K., Chin, J.c., Rao, N.S., Shankar, M.: Matching and fairness in threat-based mobile sensor coverage. IEEE Trans. Mob. Comput. 8(12), 1649–1662 (2009)

    Article  Google Scholar 

  24. Duan, X., He, J., Cheng, P., Chen, J.: Exploiting a mobile node for fast discrete time average consensus. IEEE Trans. Control. Syst. Technol. 24(6), 1993–2001 (2016)

    Article  Google Scholar 

Download references

Acknowledgment

This work is supported by the cyberspace security Major Program in National Key Research and Development Plan of China under grant 2016YFB0800201, Natural Science Foundation of China under grants 61572165, 61702150 and 61803135, State Key Program of Zhejiang Province Natural Science Foundation of China under grant LZ15F020003, Key Research and Development Plan Project of Zhejiang Province under grants 2017C01062 and 2017C01065, and Zhejiang Provincial Basic Public Welfare Research Project under grant LGG18F020015.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

    Haofeng Yan & Jian Xu

  2. School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China

    Yiming Wu, Ming Xu, Ting Wu & Tong Qiao

Authors
  1. Haofeng Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yiming Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ming Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ting Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jian Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tong Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ming Xu .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yan, H., Wu, Y., Xu, M., Wu, T., Xu, J., Qiao, T. (2018). Resilient Consensus for Multi-agent Networks with Mobile Detectors. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04239-4_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04238-7

  • Online ISBN: 978-3-030-04239-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation