The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Distributed regression: an efficient framework for modeling sensor network data
Abstract
We present distributed regression, an efficient and general framework for in-network modeling of sensor data. In this framework, the nodes of the sensor network collaborate to optimally fit a global function to each of their local measurements. The algorithm is based upon kernel linear regression, where the model takes the form of a weighted sum of local basis functions; this provides an expressive yet tractable class of models for sensor network data. Rather than transmitting data to one another or outside the network, nodes communicate constraints on the model parameters, drastically reducing the communication required. After the algorithm is run, each node can answer queries for its local region, or the nodes can efficiently transmit the parameters of the model to a user outside the network. We present an evaluation of the algorithm based upon data from a 48-node sensor network deployment at the Intel Research - Berkeley Lab, demonstrating that our distributed algorithm converges to the optimal solution at a fast rate and is very robust to packet losses.
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Directed diffusion: a scalable and robust communication paradigm for sensor networks
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Wireless integrated network sensors
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System architecture directions for networked sensors
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TOSSIM: accurate and scalable simulation of entire TinyOS applications
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Sandryhaila, A., & Moura, J. M. F. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2238935 · Cited by 1,531
Machine learning in wireless sensor networks: Algorithms, strategies, and applications
Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2320099 · Cited by 965
A new class of distributed optimization algorithms: Application to regression of distributed data
Sundhar Ram, S., Nedić, A., & Veeravalli, V. V. (2012) · Optimization Methods and Software · doi:10.1080/10556788.2010.511669 · Cited by 123
Deb Majumder, B., Roy, J. K., & Padhee, S. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2882239 · Cited by 104
Predicting link quality using supervised learning in wireless sensor networks
Wang, Y., Martonosi, M., & Peh, L.-S. (2007) · ACM SIGMOBILE Mobile Computing and Communications Review · doi:10.1145/1317425.1317434 · Cited by 101
Metadata
- Authors
- Guestrin, C., Bodik, P., Thibaux, R., Paskin, M., & Madden, S.
- Venue
- Proceedings of the 3rd international symposium on Information processing in sensor networks
- Publisher
- ACM
- Published
- 2004-04-26
- Pages
- 1-10
- OpenAlex
- W2165004589
- Cited by
- 429
- References
- 16
- FWCI
- 16.02
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Guestrin, C., Bodik, P., Thibaux, R., Paskin, M., & Madden, S. (2004). Distributed regression: an efficient framework for modeling sensor network data. Proceedings of the 3rd international symposium on Information processing in sensor networks. https://doi.org/10.1145/984622.984624
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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.
