The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Locating the nodes: cooperative localization in wireless sensor networks
Abstract
Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then forms a map of the network. Various application requirements influence the design of sensor localization systems. In this article, the authors describe the measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. The article briefly surveys a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.
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Cites
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
The generalized correlation method for estimation of time delay
Knapp, C., & Carter, G. (1976) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1976.1162830 · Cited by 4,433 · cited 1×
“The generalized cross-correlator (GCC) derived by Knapp and Carter [32] (the maximum likelihood estimator (MLE) for the TOA) extends the SCC by applying prefilters to amplify spectral components of the signal that have little noise and attenuate components with large noise.”
Distributed optimization in sensor networks
Rabbat, M., & Nowak, R. (2004) · Proceedings of the 3rd international symposium on Information processing in sensor networks · doi:10.1145/984622.984626 · Cited by 901
Cited by
Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond
Liu, F., Cui, Y., Masouros, C., Xu, J., Han, T. X., Eldar, Y. C., & Buzzi, S. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3156632 · Cited by 3,857
Sayed, A. H., Tarighat, A., & Khajehnouri, N. (2005) · IEEE Signal Processing Magazine · doi:10.1109/msp.2005.1458275 · Cited by 1,312
Ghaleb, B., Al-Dubai, A. Y., Ekonomou, E., Alsarhan, A., Nasser, Y., Mackenzie, L. M., & Boukerche, A. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2874356 · Cited by 333
Closed-Form Formulae for Time-Difference-of-Arrival Estimation
So, H. C., Chan, Y. T., & Chan, F. K. W. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.914342 · Cited by 134
Device-Free RF Human Body Fall Detection and Localization in Industrial Workplaces
Kianoush, S., Savazzi, S., Vicentini, F., Rampa, V., & Giussani, M. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2624800 · Cited by 113
Metadata
- Authors
- Patwari, N., Ash, J. N., Kyperountas, S., Hero, A. O., Moses, R. L., & Correal, N. S.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2005-07
- Volume
- 22
- Issue
- 4
- Pages
- 54-69
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W1988543519
- Cited by
- 3,031
- References
- 3
- FWCI
- 102.97
- Type
- journal article
- Language
- en
- Open access
- closed
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