Resilient Structural Sparsity in the Design of Consensus Networks

被引:3
作者
Diaz-Garcia, Gilberto [1 ]
Narvaez, Gabriel [2 ]
Giraldo, Luis Felipe [2 ]
Giraldo, Jairo [3 ]
Cardenas, Alvaro A. [4 ]
机构
[1] Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA
[2] Univ Ios Andes, Dept Elect & Elect Engn, Bogota 111711, Colombia
[3] Univ Utah, Dept Elect & Comp Engn, Salt Lake City, UT 84112 USA
[4] Univ Calif Santa Cruz, Dept Comp Engn, Santa Cruz, CA 95064 USA
关键词
Eigenvalues and eigenfunctions; Synchronization; Resilience; Optimization; Network topology; Laplace equations; Topology; Consensus; cyberattacks; cybersecurity; dynamics; networks; resilience; structural sparsity; synchronization; DISTRIBUTED FUNCTION CALCULATION; ALGEBRAIC CONNECTIVITY; LINEAR ITERATIONS; MALICIOUS AGENTS; SYNCHRONIZATION; PERFORMANCE; SYSTEMS; DYNAMICS;
D O I
10.1109/TCYB.2021.3126576
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The consensus problem is relevant to different areas ranging from biology, social psychology, and physics to power systems and robotics. Two crucial aspects of the design of a consensus system are the implementation issues that arise in densely connected networks and the presence of malicious agents that try to cause a deviation from a synchronization state. In this article, we introduce a formulation to design the topology of a consensus network to improve its resilience to attacks while remaining sparse and consistent with the a priori structural relations between the agents. Through mathematical analysis and simulations on artificial and real-world cases, we show the benefits and usefulness of using this strategy to design resilient and structurally sparse consensus networks.
引用
收藏
页码:2717 / 2726
页数:10
相关论文
共 50 条
[1]  
Angelosante D, 2009, 2009 16TH INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING, VOLS 1 AND 2, P816
[2]   Structured Sparsity through Convex Optimization [J].
Bach, Francis ;
Jenatton, Rodolphe ;
Mairal, Julien ;
Obozinski, Guillaume .
STATISTICAL SCIENCE, 2012, 27 (04) :450-468
[3]  
Bapat RB., 2018, GRAPHS MATRICES
[4]   Deploying Dense Networks for Maximal Energy Efficiency: Small Cells Meet Massive MIMO [J].
Bjornson, Emil ;
Sanguinetti, Luca ;
Kountouris, Marios .
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2016, 34 (04) :832-847
[5]  
Borwein Jonathan, 2010, Convex Analysis and Nonlinear Optimization: Theory and Examples
[6]  
Boyd S. P., 2004, Convex Optimization
[7]  
Chen C.-T., 1984, Linear System Theory and Design
[8]   Scalable Consensus in Networks of Multiagent Systems Using High-Gain Observers [J].
Chowdhury, Dhrubajit ;
Khalil, Hassan K. .
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, 2020, 7 (03) :1237-1247
[9]   Old and new results on algebraic connectivity of graphs [J].
de Abreu, Nair Maria Maia .
LINEAR ALGEBRA AND ITS APPLICATIONS, 2007, 423 (01) :53-73
[10]   Estimating Graph Robustness Through the Randic Index [J].
De Meo, Pasquale ;
Messina, Fabrizio ;
Rosaci, Domenico ;
Sarne, Giuseppe M. L. ;
Vasilakos, Athanasios V. .
IEEE TRANSACTIONS ON CYBERNETICS, 2018, 48 (11) :3232-3242