Vector quantization and signal compression
Vector quantization and signal compression
Elements of information theory
Elements of information theory
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
On the interdependence of routing and data compression in multi-hop sensor networks
Proceedings of the 8th annual international conference on Mobile computing and networking
Single-Bit Oversampled A/D Conversion with Exponential Accuracy in the Bit-Rate
DCC '00 Proceedings of the Conference on Data Compression
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
Sampling signals with finite rate of innovation
IEEE Transactions on Signal Processing
The capacity of wireless networks
IEEE Transactions on Information Theory
MASTAQ: A Middleware Architecture for Sensor Applications with Statistical Quality Constraints
PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
Effects of A-D conversion nonidealities on distributed sampling in dense sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Fading observation alignment via feedback
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Limits of signal processing performance under thresholding
Signal Processing
Complete characterization of stable bandlimited systems under quantization and thresholding
IEEE Transactions on Signal Processing
Entropy of highly correlated quantized data
IEEE Transactions on Information Theory
Unboundedness of thresholding and quantization for bandlimited signals
Signal Processing
| Hi-index | 0.06 |
Distributed sampling and reconstruction of a physical field using an array of sensors is a problem of considerable interest in environmental monitoring applications of sensor networks. Our recent work has focused on the sampling of bandlimited sensor fields. However, sensor fields are not perfectly bandlimited but typically have rapidly decaying spectra. In a classical sampling set-up it is possible to precede the A/D sampling operation with an appropriate analog anti-aliasing filter. However, in the case of sensor networks, this is infeasible since sampling must precede filtering. We show that even though the effects of aliasing on the reconstruction cannot be prevented due to the "filter-less" sampling constraint, they can be suitably controlled by oversampling and carefully reconstructing the field from the samples. We show using a dither-based scheme that it is possible to estimate non-bandlimited fields with a precision that depends on how fast the spectral content of the field decays. We develop a framework for analyzing non-bandlimited fields that lead to upper bounds on the maximum pointwise error for a spatial bit rate of R bits/meter. We present results for fields with exponentially decaying spectra as an illustration. In particular, we show that for fields f(t) with exponential tails; i.e., F(ω) ‹ παε–αω:, the maximum pointwise error decays as c2e–α1√R+c3 1 over √R e –2α1√R with spatial bit rate R bits/meter. Finally, we show that for fields with spectra that have a finite second moment, the distortion decreases as O((1 overN)2 over 3) as the density of sensors, N, scales up to infinity . We show that if D is the targeted non-zero distortion, then the required (finite) rate R scales as O (1 over √ overD log 1 over D).