A Deterministic Annealing Approach for Parsimonious Design of Piecewise Regression Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design of Optimal Quantizers for Distributed Source Coding
DCC '03 Proceedings of the Conference on Data Compression
The impact of spatial correlation on routing with compression in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Low-complexity coding and source-optimized clustering for large-scale sensor networks
ACM Transactions on Sensor Networks (TOSN)
Joint source decoding in large scale sensor networks using Markov random field models
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Distributed predictive coding for spatio-temporally correlated sources
IEEE Transactions on Signal Processing
Design of scalable decoders for sensor networks via Bayesian network learning
IEEE Transactions on Communications
Robust distributed source coder design by deterministic annealing
IEEE Transactions on Signal Processing
A global optimization technique for statistical classifier design
IEEE Transactions on Signal Processing
Distributed source coding using syndromes (DISCUS): design and construction
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Networked Slepian-Wolf: theory, algorithms, and scaling laws
IEEE Transactions on Information Theory
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There has been considerable interest in distributed source coding within the compression and sensor network research communities in recent years, primarily due to its potential contributions to low-power sensor networks. However, two major obstacles pose an existential threat on practical deployment of such techniques in real world sensor networks, namely, the exponential growth of decoding complexity with network size and coding rates, and the critical requirement for error-resilience given the severe channel conditions in many wireless sensor networks. Motivated by these challenges, this paper proposes a novel, unified approach for large scale, error-resilient distributed source coding, based on an optimally designed classifier-based decoding framework, where the design explicitly controls the decoding complexity. We also present a deterministic annealing (DA) based global optimization algorithm for the design due to the highly non-convex nature of the cost function, which further enhances the performance over basic greedy iterative descent technique. Simulation results on data, both synthetic and from real sensor networks, provide strong evidence that the approach opens the door to practical deployment of distributed coding in large sensor networks. It not only yields substantial gains in terms of overall distortion, compared to other state-of-the-art techniques, but also demonstrates how its decoder naturally scales to large networks while constraining the complexity, thereby enabling performance gains that increase with network size.