Theory of linear and integer programming
Theory of linear and integer programming
Elements of information theory
Elements of information theory
Directed diffusion for wireless sensor networking
IEEE/ACM Transactions on Networking (TON)
The impact of spatial correlation on routing with compression in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Spatio-temporal correlation: theory and applications for wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: In memroy of Olga Casals
Efficient gathering of correlated data in sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
A Distributed Active Sensor Selection Scheme for Wireless Sensor Networks
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Collaborative in-network processing for target tracking
EURASIP Journal on Applied Signal Processing
Capacity aware optimal activation of sensor nodes under reproduction distortion measures
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
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
The CEO problem [multiterminal source coding]
IEEE Transactions on Information Theory
The quadratic Gaussian CEO problem
IEEE Transactions on Information Theory
The rate-distortion function for the quadratic Gaussian CEO problem
IEEE Transactions on Information Theory
Universal decentralized estimation in a bandwidth constrained sensor network
IEEE Transactions on Information Theory
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We consider a sensor network involving sensors placed in specific locations. An area phenomenon is detected and tracked by activated sensors. The area phenomenon is modeled to consist of K spatially distributed point phenomena. The activated sensors collect data samples characterizing the parameters of the involved point phenomena. They compress observed data readings and transport them to a processing center. The center processes the received data to derive estimates of the point phenomena's parameters. Our sensing stochastic process models account for distance-dependent observation noise perturbations as well as noise correlations. At the processing center, sample mean calculations are used to derive the estimates of the underlying area phenomenon's parameters. We develop computationally efficient algorithms to determine the specific set of sensors for activation under capacity and energy resource constraints so that a sufficiently low reproduction distortion level is attained. We derive lower bounds on the realizable levels of the distortion measure. Using illustrative cases, we demonstrate one of our algorithms to yield distortion levels that are very close to the lower bound, while other lower-complexity schemes often yield distortion levels relatively close to the lower bound.