Article

Peer-to-peer estimation over wireless sensor networks via Lipschitz optimization

Authors:

Carlo Fischione,

Alberto Speranzon,

Karl Henrik Johansson,

Alberto Sangiovanni-VincentelliAuthors Info & Claims

IPSN '09: Proceedings of the 2009 International Conference on Information Processing in Sensor Networks

Pages 241 - 252

Published: 13 April 2009 Publication History

Abstract

Motivated by a peer-to-peer estimation algorithm in which adaptive weights are optimized to minimize the estimation error variance, we formulate and solve a novel nonconvex Lipschitz optimization problem that guarantees global stability of a large class of peer-to-peer consensus-based algorithms for wireless sensor network. Because of packet losses, the solution of this optimization problem cannot be achieved efficiently with either traditional centralized methods or distributed Lagrangian message passing. We prove that the optimal solution can be obtained by solving a set of nonlinear equations. A fast distributed algorithm, which requires only local computations, is presented for solving these equations. Analysis and computer simulations illustrate the algorithm and its application to various network topologies.

References

[1]

P. Alrikson and A. Rantzer. Experimental evaluation of a distributed kalman filter algorithm. In In Proceedings of IEEE CDC, 2007.

[2]

D. P. Bertsekas and J. N. Tsitsiklis. Parallel and Distributed Computation: Numerical Methods. Athena Scientific, 1997.

Digital Library

[3]

S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.

Digital Library

[4]

R. Carli, A. Chiuso, L. Schenato, and A. Zampieri. Distributed kalman filtering using consensus strategies. In In Proceedings of IEEE CDC, 2007.

[5]

R. Carli, F. Fagnani, A. Speranzon, and S. Zampieri. Communication constraints in the average consensus problem. Automatica, 44(3), 2008.

Digital Library

[6]

C. Fischione, A. Speranzon, K. H. Johansson, and A. Sangiovanni-Vincentelli. Distributed estimation over wireless sensor networks with packet losses. Online http://arxiv.org/abs/0810.3715, 2008.

[7]

H. Gharavi and P. R. Kumar, editors. Proceedings of IEEE: Special Issue on Sensor Networks and Applications, volume 91, 2003.

[8]

R. A. Horn and C. R. Johnson. Matrix Analysis. Cambridge University Press, 1985.

Digital Library

[9]

R. Horst, P. M. Pardalos, and N. V. Thoai. Introduction to Global Optimization, Nonconvex Optimization and its Applications. Kluwer Academic Publisher, 1995.

Digital Library

[10]

A. Jadbabaie, J. Lin, and A. S. Morse. Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Transactions on Automatic Control, 48(6):988-1001, 2003.

[11]

B. Johansson. On Distributed Optimization in Networked Systems. PhD thesis, KTH, 2009.

[12]

Y. Kim, D. Gu, and I. Postlethwaite. Fault-tolerant cooperative target tracking in distributed uav networks. In IFAC World Congress, 2008.

[13]

E. J. Msechu, A. Ribeiro, S. I. Roumeliotis, and G. B. Giannakis. Distributed Kalman filtering based on quantized innovations. In Proceedings of IEEE ICASSP, 2007.

[14]

R. Olfati-Saber and J. S. Shamma. Consensus filters for sensor networks and distributed sensor fusion. In Proceedings of IEEE CDC, 2005.

[15]

L. Shi. Resource optimization for networked estimator with guaranteed estimation quality. PhD thesis, Caltech, 2008.

[16]

D. P. Spanos, R. Olfati-Saber, and R. M. Murray. Approximate distributed Kalman filtering in sensor networks with quantifiable performance. In In Proceedings of IEEE CDC, 2005.

[17]

A. Speranzon, C. Fischione, B. Johansson, and K. Johansson. Adaptive distributed estimation over wireless sensor networks with packet losses. In In Proceedings of IEEE CDC, 2007.

[18]

A. Speranzon, C. Fischione, K. H. Johansson, and A. Sangiovanni-Vincentelli. A distributed minimum variance estimator for sensor networks. IEEE JSAC, Special Issue on Control and Communication, 2008.

Digital Library

[19]

S. Stankovic, M. Stankovic, and D. Stipanovic. Decentralized parameter estimation by consensus based stochastic approximation. In Proceedings of IEEE CDC, 2007.

[20]

J. J. Xiao and Z.-Q. Luo. Universal decentralized estimation in a bandwidth-constrained sensor network. IEEE Transactions on Signal Processing, 2005.

Digital Library

[21]

J. J. Xiao, A. Riberio, Z.-Q. Luo, and G. B. Giannakis. Distributed compression-estimation using wirless sensor netowrks. IEEE Signal Processing Magazine, 2006.

[22]

L. Xiao, S. Boyd, and S. J. Kim. Distributed average consensus with least-mean-square deviation. Journal of Parallel and Distributed Computing, 2006.

Digital Library

[23]

L. Xiao, S. Boyd, and S. Lall. A scheme for robust distributed sensor fusion based on average consensus. In Proceedings of IEEE IPSN, 2005.

Digital Library

Cited By

Asorey-Cacheda RGarcia-Sanchez AGarcia-Sanchez FGarcia-Haro J(2017)A survey on non-linear optimization problems in wireless sensor networksJournal of Network and Computer Applications10.1016/j.jnca.2017.01.00182:C(1-20)Online publication date: 15-Mar-2017
https://dl.acm.org/doi/10.1016/j.jnca.2017.01.001
Leontiadis IEfstratiou CMascolo CCrowcroft J(2012)SenShareProceedings of the 9th European conference on Wireless Sensor Networks10.1007/978-3-642-28169-3_5(65-81)Online publication date: 15-Feb-2012
https://dl.acm.org/doi/10.1007/978-3-642-28169-3_5
Kim YSchmid TSrivastava M(2010)Design and implementation of a robust sensor data fusion system for unknown signalsProceedings of the 6th IEEE international conference on Distributed Computing in Sensor Systems10.5555/2163970.2163976(77-91)Online publication date: 21-Jun-2010
https://dl.acm.org/doi/10.5555/2163970.2163976
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cover image ACM Conferences

IPSN '09: Proceedings of the 2009 International Conference on Information Processing in Sensor Networks

April 2009

441 pages

ISBN:9781424451081

Sponsors

SIGBED: ACM Special Interest Group on Embedded Systems

Publisher

IEEE Computer Society

United States

Publication History

Published: 13 April 2009

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Overall Acceptance Rate 143 of 593 submissions, 24%

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Cited By

Asorey-Cacheda RGarcia-Sanchez AGarcia-Sanchez FGarcia-Haro J(2017)A survey on non-linear optimization problems in wireless sensor networksJournal of Network and Computer Applications10.1016/j.jnca.2017.01.00182:C(1-20)Online publication date: 15-Mar-2017
https://dl.acm.org/doi/10.1016/j.jnca.2017.01.001
Leontiadis IEfstratiou CMascolo CCrowcroft J(2012)SenShareProceedings of the 9th European conference on Wireless Sensor Networks10.1007/978-3-642-28169-3_5(65-81)Online publication date: 15-Feb-2012
https://dl.acm.org/doi/10.1007/978-3-642-28169-3_5
Kim YSchmid TSrivastava M(2010)Design and implementation of a robust sensor data fusion system for unknown signalsProceedings of the 6th IEEE international conference on Distributed Computing in Sensor Systems10.5555/2163970.2163976(77-91)Online publication date: 21-Jun-2010
https://dl.acm.org/doi/10.5555/2163970.2163976
Park PFischione CJohansson KAbdelzaher TVoigt TWolisz A(2010)Adaptive IEEE 802.15.4 protocol for energy efficient, reliable and timely communicationsProceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks10.1145/1791212.1791251(327-338)Online publication date: 12-Apr-2010
https://dl.acm.org/doi/10.1145/1791212.1791251

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