The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Distributed compression-estimation using wireless sensor networks
Abstract
This paper provides an overview of distributed estimation-compression problems encountered with wireless sensor networks (WSN). A general formulation of distributed compression-estimation under rate constraints was introduced, pertinent signal processing algorithms were developed, and emerging tradeoffs were delineated from an information theoretic perspective. Specifically, we designed rate-constrained distributed estimators for various signal models with variable knowledge of the underlying data distributions. We proved theoretically, and corroborated with examples, that when the noise distributions are either completely known, partially known or completely unknown, distributed estimation is possible with minimal bandwidth requirements which can achieve the same order of mean square error (MSE) performance as the corresponding centralized clairvoyant estimators.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 36,808
Wireless sensor networks: a survey
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002) · Computer Networks · doi:10.1016/s1389-1286(01)00302-4 · Cited by 17,418
Fundamentals of Statistical Signal Processing: Estimation Theory
Sengijpta, S. K. (1995) · Technometrics · doi:10.1080/00401706.1995.10484391 · Cited by 11,076
Fundamentals of statistical signal processing: Estimation theory
Prasad, K. V. (1994) · Control Engineering Practice · doi:10.1016/0967-0661(94)90195-3 · Cited by 7,902
Routing techniques in wireless sensor networks: a survey
Al-Karaki, J. N., & Kamal, A. E. (2004) · IEEE Wireless Communications · doi:10.1109/mwc.2004.1368893 · Cited by 4,500
Wireless sensor networks for habitat monitoring
Mainwaring, A., Culler, D., Polastre, J., Szewczyk, R., & Anderson, J. (2002) · Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications · doi:10.1145/570738.570751 · Cited by 4,166
Particle filters for positioning, navigation, and tracking
Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson, R., & Nordlund, P.-J. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978396 · Cited by 1,701
Energy-constrained modulation optimization
Cui, S., Goldsmith, A. J., & Bahai, A. (2005) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2005.853882 · Cited by 1,491
Cited by
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
Schizas, I. D., Ribeiro, A., & Giannakis, G. B. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.906734 · Cited by 685
Self-citationInformation fusion for wireless sensor networks: Methods, models, and classifications
Nakamura, E. F., Loureiro, A. A. F., & Frery, A. C. (2007) · ACM Computing Surveys · doi:10.1145/1267070.1267073 · Cited by 537
Channel-aware distributed detection in wireless sensor networks
Chen, B., Tong, L., & Varshney, P. K. (2006) · IEEE Signal Processing Magazine · doi:10.1109/msp.2006.1657814 · Cited by 297
Linear Coherent Decentralized Estimation
Xiao, J.-J., Cui, S., Luo, Z.-Q., & Goldsmith, A. J. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.906762 · Cited by 277
Self-citationData Compression for Multi-Parameter Estimation for Emitter Location
Chen, M., & Fowler, M. L. (2010) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2010.5417164 · Cited by 22
Metadata
- Authors
- Xiao, J.-J., Ribeiro, A., Luo, Z.-Q., & Giannakis, G. B.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2006-07
- Volume
- 23
- Issue
- 4
- Pages
- 27-41
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W2052179119
- Cited by
- 347
- References
- 31
- FWCI
- 35.04
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Distributed compression-estimation using wireless sensor networks. https://app.cochat.ai/papers/10.1109/msp.2006.1657815
- The paper
- Xiao, J.-J., Ribeiro, A., Luo, Z.-Q., & Giannakis, G. B. (2006). Distributed compression-estimation using wireless sensor networks. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2006.1657815
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
