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Resilient Consensus for Multi-agent Networks with Mobile Detectors

  • Haofeng Yan
  • Yiming Wu
  • Ming Xu
  • Ting Wu
  • Jian Xu
  • Tong Qiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11307)

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.

Keywords

Resilient consensus Network security Mobile detector 

Notes

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.

References

  1. 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)MathSciNetCrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 3.
    Zheng, Y., Ma, J., Wang, L.: Consensus of hybrid multi-agent systems. IEEE Trans. Neural Netw. Learn. Syst. 29(4), 1359–1365 (2018)CrossRefGoogle Scholar
  4. 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. 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)MathSciNetCrossRefGoogle Scholar
  6. 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. 7.
    Kieckhafer, R.M., Azadmanesh, M.H.: Reaching approximate agreement with mixed-mode faults. IEEE Trans. Parallel Distrib. Syst. 5(1), 53–63 (1994)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Dibaji, S.M., Ishii, H.: Resilient multi-agent consensus with asynchrony and delayed information. IFAC-Pap. OnLine 48(22), 28–33 (2015)CrossRefGoogle Scholar
  11. 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)MathSciNetCrossRefGoogle Scholar
  12. 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)MathSciNetCrossRefGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 14.
    Kieckhafer, R., Azadmanesh, M.: Low cost approximate agreement in partially connected networks. J. Comput. Inf. 3(1), 53–85 (1993)MathSciNetGoogle Scholar
  15. 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. 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. 17.
    Zhang, H., Fata, E., Sundaram, S.: A notion of robustness in complex networks. IEEE Trans. Control. Netw. Syst. 2(3), 310–320 (2015)MathSciNetCrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 19.
    Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)CrossRefGoogle Scholar
  20. 20.
    Pons, P., Latapy, M.: Computing communities in large networks using random walks. J. Graph Algorithms Appl. 10(2), 191–218 (2006)MathSciNetCrossRefGoogle Scholar
  21. 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)CrossRefGoogle Scholar
  22. 22.
    Arenas, A., Duch, J., Fernández, A., Gómez, S.: Size reduction of complex networks preserving modularity. New J. Phys. 9(6), 176 (2007)MathSciNetCrossRefGoogle Scholar
  23. 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)CrossRefGoogle Scholar
  24. 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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Haofeng Yan
    • 1
  • Yiming Wu
    • 2
  • Ming Xu
    • 2
  • Ting Wu
    • 2
  • Jian Xu
    • 1
  • Tong Qiao
    • 2
  1. 1.School of Computer Science and TechnologyHangzhou Dianzi UniversityHangzhouChina
  2. 2.School of CyberspaceHangzhou Dianzi UniversityHangzhouChina

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