Structural Cost-Optimal Design of Sensor Networks for Distributed Estimation

In this letter, we discuss cost optimization of sensor networks monitoring structurally full-rank systems under distributed observability constraint. Using structured systems theory, the problem is relaxed into two subproblems: first, sensing cost optimization; and second, networking cost optimization. Both problems are reformulated as combinatorial optimization problems. The sensing cost optimization is shown to have a polynomial-order solution. The networking cost optimization is shown to be NP-hard in general, but has a polynomial-order solution under specific conditions. A 2-approximation polynomial-order relaxation is provided for general networking cost optimization, which is applicable in large-scale system monitoring.

[1]  Ali H. Sayed,et al.  Distributed Detection Over Adaptive Networks Using Diffusion Adaptation , 2011, IEEE Transactions on Signal Processing.

[2]  Usman A. Khan,et al.  On the Genericity Properties in Distributed Estimation: Topology Design and Sensor Placement , 2012, IEEE Journal of Selected Topics in Signal Processing.

[3]  Soummya Kar,et al.  Optimal design of observable multi-agent networks: A structural system approach , 2014, 2014 European Control Conference (ECC).

[4]  Albert-László Barabási,et al.  Observability of complex systems , 2013, Proceedings of the National Academy of Sciences.

[5]  Robert E. Tarjan,et al.  Efficient algorithms for finding minimum spanning trees in undirected and directed graphs , 1986, Comb..

[6]  Usman A. Khan,et al.  Distributed Estimation Recovery Under Sensor Failure , 2017, IEEE Signal Processing Letters.

[7]  Usman A. Khan,et al.  Graph-Theoretic Distributed Inference in Social Networks , 2014, IEEE Journal of Selected Topics in Signal Processing.

[8]  R. Prim Shortest connection networks and some generalizations , 1957 .

[9]  Soummya Kar,et al.  Consensus + innovations distributed inference over networks: cooperation and sensing in networked systems , 2013, IEEE Signal Processing Magazine.

[10]  Joseph JáJá,et al.  Approximation Algorithms for Several Graph Augmentation Problems , 1981, SIAM J. Comput..

[11]  Richard M. Karp,et al.  Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems , 1972, Combinatorial Optimization.

[12]  Soummya Kar,et al.  Design of communication networks for distributed computation with privacy guarantees , 2014, 53rd IEEE Conference on Decision and Control.

[13]  C. Guestrin,et al.  Near-optimal sensor placements: maximizing information while minimizing communication cost , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[14]  José M. F. Moura,et al.  Consensus+Innovations Distributed Kalman Filter With Optimized Gains , 2016, IEEE Transactions on Signal Processing.

[15]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[16]  Usman A. Khan,et al.  Communication strategies to ensure generic networked observability in multi-agent systems , 2011, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[17]  David W. Pentico,et al.  Assignment problems: A golden anniversary survey , 2007, Eur. J. Oper. Res..

[18]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[19]  Dominique Sauter,et al.  Structural Analysis of the Partial State and Input Observability for Structured Linear Systems: Application to Distributed Systems , 2009, Eur. J. Control.

[20]  José M. F. Moura,et al.  Distributed Kalman Filtering With Dynamic Observations Consensus , 2015, IEEE Transactions on Signal Processing.

[21]  George J. Pappas,et al.  Distributed leader selection , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[22]  Soummya Kar,et al.  Optimal design of distributed sensor networks for field reconstruction , 2013, 21st European Signal Processing Conference (EUSIPCO 2013).

[23]  Stephen P. Boyd,et al.  Sensor Selection via Convex Optimization , 2009, IEEE Transactions on Signal Processing.

[24]  Usman A. Khan,et al.  Measurement partitioning and observational equivalence in state estimation , 2014, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[25]  Soummya Kar,et al.  Minimum number of information gatherers to ensure full observability of a dynamic social network: A structural systems approach , 2014, 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[26]  Christian Commault,et al.  Generic properties and control of linear structured systems: a survey , 2003, Autom..

[27]  Amir Asif,et al.  Distributed Consensus $+$ Innovation Particle Filtering for Bearing/Range Tracking With Communication Constraints , 2015, IEEE Transactions on Signal Processing.

[28]  Ali H. Sayed,et al.  Diffusion Strategies Outperform Consensus Strategies for Distributed Estimation Over Adaptive Networks , 2012, IEEE Transactions on Signal Processing.

[29]  M. Hou Discussion on: ''Structural Analysis of the Partial State and Input Observability for Structured Linear Systems. Application to Distributed Systems'' , 2009 .

[30]  Shinkyu Park,et al.  Necessary and sufficient conditions for the stabilizability of a class of LTI distributed observers , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[31]  Soummya Kar,et al.  Structurally Observable Distributed Networks of Agents Under Cost and Robustness Constraints , 2017, IEEE Transactions on Signal and Information Processing over Networks.

[32]  J. Kruskal On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[33]  Gregory Gutin,et al.  Digraphs - theory, algorithms and applications , 2002 .

[34]  Giorgio Battistelli,et al.  Consensus-based algorithms for distributed filtering , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[35]  Usman A. Khan,et al.  On the characterization of distributed observability from first principles , 2014, 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP).