The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Incremental Adaptive Strategies Over Distributed Networks
Open accessAbstract
An adaptive distributed strategy is developed based on incremental techniques. The proposed scheme addresses the problem of linear estimation in a cooperative fashion, in which nodes equipped with local computing abilities derive local estimates and share them with their predefined neighbors. The resulting algorithm is distributed, cooperative, and able to respond in real time to changes in the environment. Each node is allowed to communicate with its immediate neighbor in order to exploit the spatial dimension while limiting the communications burden at the same time. A spatial-temporal energy conservation argument is used to evaluate the steady-state performance of the individual nodes across the entire network. Computer simulations illustrate the results.
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
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002) · IEEE Communications Magazine · doi:10.1109/mcom.2002.1024422 · Cited by 13,644
Consensus Problems in Networks of Agents With Switching Topology and Time-Delays
Olfati-Saber, R., & Murray, R. M. (2004) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2004.834113 · Cited by 12,788
Fast linear iterations for distributed averaging
Xiao, L., & Boyd, S. (2004) · Systems & Control Letters · doi:10.1016/j.sysconle.2004.02.022 · Cited by 2,706
Distributed asynchronous deterministic and stochastic gradient optimization algorithms
Tsitsiklis, J., Bertsekas, D., & Athans, M. (1986) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1986.1104412 · Cited by 1,967
Fundamentals of Adaptive Filtering
Ramirez, P. S. (2002) · The Kluwer International Series in Engineering and Computer Science · doi:10.1007/978-1-4757-3637-3_2 · Cited by 1,958
Fundamentals of adaptive filtering
Sayed, A. H. (2003) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1,673
Self-citationGuest Editors' Introduction: Overview of Sensor Networks
Culler, D., Estrin, D., & Srivastava, M. (2004) · Computer · doi:10.1109/mc.2004.93 · Cited by 1,451
Cruller, D., Estrin, D. L., & Srivastava, M. B. (2004) · Computer · Cited by 1,263
A scheme for robust distributed sensor fusion based on average consensus
Xiao, L., Boyd, S., & Lall, S. (2005) · doi:10.5555/1147685.1147698 · Cited by 1,170
Instrumenting the world with wireless sensor networks
Estrin, D., Girod, L., Pottie, G., & Srivastava, M. (2002) · 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221) · doi:10.1109/icassp.2001.940390 · Cited by 1,164
Cited by
Distributed Optimization Over Time-Varying Directed Graphs
Nedic, A., & Olshevsky, A. (2015) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2014.2364096 · Cited by 1,187
Diffusion recursive least-squares for distributed estimation over adaptive networks
Cattivelli, F. S., Lopes, C. G., & Sayed, A. H. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.913164 · Cited by 623
Self-citationSayed, A. H. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2306253 · Cited by 587
Self-citationOn Analog Gradient Descent Learning Over Multiple Access Fading Channels
Sery, T., & Cohen, K. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.2989580 · Cited by 172
Distributed optimization over time-varying directed graphs
Nedic, A., & Olshevsky, A. (2013) · 52nd IEEE Conference on Decision and Control · doi:10.1109/cdc.2013.6760975 · Cited by 72
Metadata
- Authors
- Lopes, C. G., & Sayed, A. H.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2007-08
- Volume
- 55
- Issue
- 8
- Pages
- 4064-4077
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2130442323
- Cited by
- 732
- References
- 25
- FWCI
- 28.06
- Type
- journal article
- Language
- en
- Open access
- green
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: Incremental Adaptive Strategies Over Distributed Networks. https://app.cochat.ai/papers/10.1109/tsp.2007.896034
- The paper
- Lopes, C. G., & Sayed, A. H. (2007). Incremental Adaptive Strategies Over Distributed Networks. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2007.896034
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.