Matrix analysis
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Convex Optimization
Source-channel communication in sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Optimal dimensionality reduction of sensor data in multisensor estimation fusion
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part II
Robust Distributed Estimation Using the Embedded Subgraphs Algorithm
IEEE Transactions on Signal Processing
Distributed Estimation Using Reduced-Dimensionality Sensor Observations
IEEE Transactions on Signal Processing
Rate-Constrained Distributed Estimation in Wireless Sensor Networks
IEEE Transactions on Signal Processing
Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case
IEEE Transactions on Signal Processing
Estimation Diversity and Energy Efficiency in Distributed Sensing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Signal Processing
Power scheduling of universal decentralized estimation in sensor networks
IEEE Transactions on Signal Processing
Sensors' optimal dimensionality compression matrix in estimation fusion
Automatica (Journal of IFAC)
Sequential signal encoding from noisy measurements using quantizers with dynamic bias control
IEEE Transactions on Information Theory
To code, or not to code: lossy source-channel communication revisited
IEEE Transactions on Information Theory
Multiterminal Source–Channel Communication Over an Orthogonal Multiple-Access Channel
IEEE Transactions on Information Theory
IEEE Communications Magazine
IEEE Journal on Selected Areas in Communications
On rate-constrained distributed estimation in unreliable sensor networks
IEEE Journal on Selected Areas in Communications
Distributed computation of averages over ad hoc networks
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Signal Processing
Decentralized estimation over noisy channels in cluster-based wireless sensor networks
International Journal of Communication Systems
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We consider the problem of distributed estimation in a power constrained collaborative wireless sensor network (WSN), where the network is divided into a set of sensor clusters, with collaboration allowed among sensors within the same cluster but not across clusters. Specifically, each cluster forms one or multiple local messages via sensor collaboration (in particular, linear operation is considered) and transmits the messages over noisy channels to a fusion center (FC). The final estimate is constructed at the FC based on the noisy data received from all clusters. In this collaborative setup, we study the following fundamental problems. Given a total transmit power constraint, shall we transmit the raw data or some low-dimensional local messages for each cluster? What is the optimal collaboration scheme for each cluster? How to optimally allocate the power among different clusters? These questions are addressed in this paper. We will show that the optimum collaboration strategy is to compress the data into one local message which, depending on the channel characteristics, is transmitted using one or multiple available channels to the FC. The optimal power allocation among the clusters is also investigated, which yields a water-filling type of scheme.