Robust message-passing for statistical inference in sensor networks

Authors:
Jeremy Schiff;Dominic Antonelli;Alexandros G. Dimakis;David Chu;Martin J. Wainwright
Affiliations:
University of California, Berkeley, CA;University of California, Berkeley, CA;University of California, Berkeley, CA;University of California, Berkeley, CA;University of California, Berkeley, CA
Venue:
Proceedings of the 6th international conference on Information processing in sensor networks
Year:
2007

Citing 13
Cited 8

Loopy Belief Propagation: Convergence and Effects of Message Errors

The Journal of Machine Learning Research
Globally Optimal Solutions for Energy Minimization in Stereo Vision Using Reweighted Belief Propagation

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Data gathering tours in sensor networks

Proceedings of the 5th international conference on Information processing in sensor networks
Geographic gossip: efficient aggregation for sensor networks

Proceedings of the 5th international conference on Information processing in sensor networks
A robust architecture for distributed inference in sensor networks

IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Ubiquitous access to distributed data in large-scale sensor networks through decentralized erasure codes

IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Convergent Tree-Reweighted Message Passing for Energy Minimization

IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting

The Journal of Machine Learning Research
Linear Programming Relaxations and Belief Propagation -- An Empirical Study

The Journal of Machine Learning Research
Loopy belief propagation and Gibbs measures

UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Learning to combine bottom-up and top-down segmentation

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Constructing free-energy approximations and generalized belief propagation algorithms

IEEE Transactions on Information Theory
MAP estimation via agreement on trees: message-passing and linear programming

IEEE Transactions on Information Theory

A methodology for in-network evaluation of integrated logical-statistical models

Proceedings of the 6th ACM conference on Embedded network sensor systems
Belief Propagation in Wireless Sensor Networks - A Practical Approach

WASA '08 Proceedings of the Third International Conference on Wireless Algorithms, Systems, and Applications
Statistical methods to estimate vehicle count using traffic cameras

Multidimensional Systems and Signal Processing
Nonparametric belief propagation for distributed tracking of robot networks with noisy inter-distance measurements

IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Efficient clustering for improving network performance in wireless sensor networks

EWSN'08 Proceedings of the 5th European conference on Wireless sensor networks
Order-optimal consensus through randomized path averaging

IEEE Transactions on Information Theory
Distributed inference and query processing for RFID tracking and monitoring

Proceedings of the VLDB Endowment
Distributed data association in smart camera networks using belief propagation

ACM Transactions on Sensor Networks (TOSN)

Quantified Score

Hi-index	0.06

Visualization

Abstract

Large-scale sensor network applications require in-network processing and data fusion to compute statistically relevant summaries of the sensed measurements. This paper studies distributed message-passing algorithms, in which neighboring nodes in the network pass local information relevant to a global computation, for performing statistical inference. We focus on the class of reweighted belief propagation (RBP) algorithms, which includes as special cases the standard sum-product and max-product algorithms for general networks with cycles, but in contrast to standard algorithms has attractive theoretical properties (uniqueness of fixed points, convergence, and robustness). Our main contribution is to design and implement a practical and modular architecture for implementing RBP algorithms in real networks. In addition, we show how intelligent scheduling of RBP messages can be used to minimize communication between motes and prolong the lifetime of the network. Our simulation and Mica2 mote deployment indicate that the proposed algorithms achieve accurate results despite real-world problems such as dying motes, dead and asymmetric links, and dropped messages. Overall, the class of RBP provides provides an ideal fit for sensor networks due to their distributed nature, requiring only local knowledge and coordination, and little requirements on other services such as reliable transmission.