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
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
The CEO problem [multiterminal source coding]
IEEE Transactions on Information Theory
The rate-distortion function for the quadratic Gaussian CEO problem
IEEE Transactions on Information Theory
Networked Slepian-Wolf: theory, algorithms, and scaling laws
IEEE Transactions on Information Theory
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We consider a sensor network employing sensor nodes that have been placed in specific locations. An area phenomenon is detected and tracked by the activated sensors. The area phenomenon is modelled to consist of K spatially distributed point phenomena. The activated sensors collect data samples characterizing the parameters of the involved component point phenomena. They compress the observed data readings and transport them to a processing center. The center processes the received data to derive estimates of the component point phenomena's parameters. Our sensing stochastic process models account for distance dependent observation noise perturbations as well as for location dependent observation noise correlations. At the processing center, sample mean calculations are used to derive estimates of the underlying area phenomenon's parameters. We develop a computationally efficient algorithm for determining the specific set of sensors selected for activation under capacity and energy resource constraints, so that a sufficiently low reproduction distortion level is attained. We demonstrate our algorithm to yield distortion levels that are quite close to those characterized by a lower bound function.