Universal distributed sensing via random projections
Proceedings of the 5th international conference on Information processing in sensor networks
Distortion-rate bounds for distributed estimation using wireless sensor networks
EURASIP Journal on Advances in Signal Processing
Power constrained distributed estimation with cluster-based sensor collaboration
IEEE Transactions on Wireless Communications
Amplify and forward for correlated data gathering over hierarchical sensor networks
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
A collaborative sensor-fault detection scheme for robust distributed estimation in sensor networks
IEEE Transactions on Communications
Random field estimation with delay-constrained and delay-tolerant wireless sensor networks
EURASIP Journal on Wireless Communications and Networking - Special issue on signal processing-assisted protocols and algorithms for cooperating objects and wireless sensor networks
Distributed encoding algorithm for source localization in sensor networks
EURASIP Journal on Advances in Signal Processing
Decentralized estimation over noisy channels in cluster-based wireless sensor networks
International Journal of Communication Systems
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We study the problem of estimating a physical process at a central processing unit (CPU) based on noisy measurements collected from a distributed, bandwidth-constrained, unreliable, network of sensors, modeled as an erasure network of unreliable "bit-pipes" between each sensor and the CPU. The CPU is guaranteed to receive data from a minimum fraction of the sensors and is tasked with optimally estimating the physical process under a specified distortion criterion. We study the noncollaborative (i.e., fully distributed) sensor network regime, and derive an information-theoretic achievable rate-distortion region for this network based on distributed source-coding insights. Specializing these results to the Gaussian setting and the mean-squared-error (MSE) distortion criterion reveals interesting robust-optimality properties of the solution. We also study the regime of clusters of collaborative sensors, where we address the important question: given a communication rate constraint between the sensor clusters and the CPU, should these clusters transmit their "raw data" or some low-dimensional "local estimates"? For a broad set of distortion criteria and sensor correlation statistics, we derive conditions under which rate-distortion-optimal compression of correlated cluster-observations separates into the tasks of dimension-reducing local estimation followed by optimal distributed compression of the local estimates.