Efficient identification of link importance in dynamic networks

Abstract For a given network topology of linking dynamical systems, determining the least or the most important link(s) or edge(s) in dynamic networks is a complex combinatorial optimization problem. The purpose of solving this problem is to modify the given network topology, in the hope of using a less number of costly communication links while keeping or improving the network׳s performance. In this paper, this identification of link importance is approached via finding the least or the most sensitive edge(s) by analytically obtaining the sensitivity of each edge or numerically solving LMIs (linear matrix inequalities). The proposed schemes are applied to a consensus network, a vehicle network and random networks to support their merit.

[1]  Mehran Mesbahi,et al.  On maximizing the second smallest eigenvalue of a state-dependent graph Laplacian , 2006, IEEE Transactions on Automatic Control.

[2]  Frank Allgöwer,et al.  Growing optimally rigid formations , 2012, 2012 American Control Conference (ACC).

[3]  Yoonsoo Kim,et al.  Bisection algorithm of increasing algebraic connectivity by adding an edge , 2009, 2009 17th Mediterranean Conference on Control and Automation.

[4]  Yoonsoo Kim Optimal modification of dynamical network topology , 2014 .

[5]  R. Murray,et al.  Limits on the network sensitivity function for homogeneous multi-agent systems on a graph , 2010, Proceedings of the 2010 American Control Conference.

[6]  Amir G. Aghdam,et al.  Structural controllability of multi-agent networks: Robustness against simultaneous failures , 2013, Autom..

[8]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .

[9]  Jianghai Hu,et al.  Optimizing formation rigidity under connectivity constraints , 2010, 49th IEEE Conference on Decision and Control (CDC).

[10]  Gerardo Lafferriere,et al.  Decentralized control of vehicle formations , 2005, Syst. Control. Lett..

[11]  J. Doyle,et al.  Essentials of Robust Control , 1997 .

[12]  Jos F. Sturm,et al.  A Matlab toolbox for optimization over symmetric cones , 1999 .

[13]  Fu Lin,et al.  Augmented Lagrangian Approach to Design of Structured Optimal State Feedback Gains , 2011, IEEE Transactions on Automatic Control.

[14]  Fu Lin,et al.  Design of Optimal Sparse Interconnection Graphs for Synchronization of Oscillator Networks , 2013, IEEE Transactions on Automatic Control.

[15]  Jens Vygen,et al.  The Book Review Column1 , 2020, SIGACT News.

[16]  Elling W. Jacobsen,et al.  Network Structure and Robustness of Intracellular Oscillators , 2008 .

[17]  Stephen P. Boyd,et al.  Growing Well-connected Graphs , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[18]  Laurent El Ghaoui,et al.  Graph Weight Allocation to Meet Laplacian Spectral Constraints , 2012, IEEE Transactions on Automatic Control.

[19]  Richard M. Murray,et al.  Consensus problems in networks of agents with switching topology and time-delays , 2004, IEEE Transactions on Automatic Control.

[20]  Mehran Mesbahi,et al.  Graph-Theoretic Analysis and Synthesis of Relative Sensing Networks , 2011, IEEE Transactions on Automatic Control.