The DOI matches its Crossref record; 176 of the 176 references it cites were found in a registry or an index.
Information fusion for wireless sensor networks: Methods, models, and classifications
Abstract
Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and discuss their applicability in the context of wireless sensor networks.
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Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
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
(1988) · Cited by 16,582
Kohonen, T. (2001) · Springer Series in Information Sciences · doi:10.1007/978-3-642-56927-2 · Cited by 16,019
Energy-efficient communication protocol for wireless microsensor networks
Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2005) · Proceedings of the 33rd Annual Hawaii International Conference on System Sciences · doi:10.1109/hicss.2000.926982 · Cited by 14,140
A Mathematical Theory of Evidence
Shafer, G. (2020) · Princeton University Press eBooks · doi:10.2307/j.ctv10vm1qb · Cited by 11,711
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,502
Pattern Recognition with Fuzzy Objective Function Algorithms
Bezdek, J. C. (1981) · doi:10.1007/978-1-4757-0450-1 · Cited by 9,881
Ahlswede, R., Cai, N., Li, S.-Y. R., & Yeung, R. W. (2000) · IEEE Transactions on Information Theory · doi:10.1109/18.850663 · Cited by 7,814
Markov Chain Monte Carlo in Practice
(1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,757
Cited by
Context Aware Computing for The Internet of Things: A Survey
Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.042313.00197 · Cited by 2,861
“Data fusion methods, models, and classification techniques in the wireless sensor networks domain are comprehensively surveyed in [190].”
MethodAdvances in Crowd Analysis for Urban Applications Through Urban Event Detection
Kaiser, M. S., Lwin, K. T., Mahmud, M., Hajializadeh, D., Chaipimonplin, T., Sarhan, A., & Hossain, M. A. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2771746 · Cited by 98
Metadata
- Authors
- Nakamura, E. F., Loureiro, A. A. F., & Frery, A. C.
- Venue
- ACM Computing Surveys
- Published
- 2007-09-03
- Volume
- 39
- Issue
- 3
- Pages
- 9
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2048619613
- Cited by
- 537
- References
- 176
- FWCI
- 40.20
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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