Elements of information theory
Elements of information theory
MiniMax Methods for Image Reconstruction
MiniMax Methods for Image Reconstruction
Power-bandwidth-distortion scaling laws for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Distributed sampling for dense sensor networks: a "Bit-conservation principle"
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Source-channel communication in sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Estimating inhomogeneous fields using wireless sensor networks
IEEE Journal on Selected Areas in Communications
Distributed classification of Gaussian space-time sources in wireless sensor networks
IEEE Journal on Selected Areas in Communications
Active wireless sensing for rapid information retrieval in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Universal distributed sensing via random projections
Proceedings of the 5th international conference on Information processing in sensor networks
Distributed field estimation with randomly deployed, noisy, binary sensors
IEEE Transactions on Signal Processing
Active wireless sensing: a versatile framework for information retrieval in sensor networks
IEEE Transactions on Signal Processing
Field estimation from randomly located binary noisy sensors
IEEE Transactions on Information Theory
On event signal reconstruction in wireless sensor networks
NETWORKING'07 Proceedings of the 6th international IFIP-TC6 conference on Ad Hoc and sensor networks, wireless networks, next generation internet
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Sensing, processing and communication must be jointly optimized for efficient operation of resource-limited wireless sensor networks. We propose a novel source-channel matching approach for distributed field estimation that naturally integrates these basic operations and facilitates a unified analysis of the impact of key parameters (number of nodes, power, field complexity) on estimation accuracy. At the heart of our approach is a distributed source-channel communication architecture that matches the spatial scale of field coherence with the spatial scale of node synchronization for phase-coherent communication: the sensor field is uniformly partitioned into multiple cells and the nodes in each cell coherently communicate simple statistics of their measurements to the destination via a dedicated noisy multiple access channel (MAC). Essentially, the optimal field estimate in each cell is implicitly computed at the destination via the coherent spatial averaging inherent in the MAC, resulting in optimal power-distortion scaling with the number of nodes. In general, smoother fields demand lower per-node power but require node synchronization over larger scales for optimal estimation. In particular, optimal mean-square distortion scaling can be achieved with sub-linear power scaling. Our results also reveal a remarkable power-density tradeoff inherent in our approach: increasing the sensor density reduces the total power required to achieve a desired distortion. A direct consequence is that consistent field estimation is possible, in principle, even with vanishing total power in the limit of high sensor density.