The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Localization for mobile sensor networks
Abstract
Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.
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Cites
RADAR: an in-building RF-based user location and tracking system
Bahl, P., & Padmanabhan, V. N. (2002) · Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064) · doi:10.1109/infcom.2000.832252 · Cited by 8,380
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993) · IEE Proceedings F Radar and Signal Processing · doi:10.1049/ip-f-2.1993.0015 · Cited by 7,550
Sequential Monte Carlo Methods in Practice
(2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,366
A survey of mobility models for ad hoc network research
Camp, T., Boleng, J., & Davies, V. (2002) · Wireless Communications and Mobile Computing · doi:10.1002/wcm.72 · Cited by 4,621
On sequential Monte Carlo sampling methods for Bayesian filtering
Doucet, A., Godsill, S., & Andrieu, C. (2000) · Statistics and Computing · doi:10.1023/a:1008935410038 · Cited by 4,411
(2014) · Developing Nonprofit and Human Service Leaders: Essential Knowledge and Skills · doi:10.4135/9781483388007.n5 · Cited by 3,811
GPS-less low-cost outdoor localization for very small devices
Bulusu, N., Heidemann, J., & Estrin, D. (2000) · IEEE Personal Communications · doi:10.1109/98.878533 · Cited by 3,592
A key-management scheme for distributed sensor networks
Eschenauer, L., & Gligor, V. D. (2002) · Proceedings of the 9th ACM conference on Computer and communications security · doi:10.1145/586110.586117 · Cited by 3,541
Stochastic models, estimation, and control
(1982) · Mathematics in Science and Engineering · doi:10.1016/s0076-5392(08)x6186-7 · Cited by 3,229
Location systems for ubiquitous computing
Hightower, J., & Borriello, G. (2001) · Computer · doi:10.1109/2.940014 · Cited by 2,987
Metadata
- Authors
- Hu, L., & Evans, D.
- Venue
- Proceedings of the 10th annual international conference on Mobile computing and networking
- Publisher
- ACM
- Published
- 2004-09-26
- Pages
- 45-57
- ISSN
- 1543-5679 (ISSN-L)
- OpenAlex
- W2131152141
- Cited by
- 1,050
- References
- 40
- FWCI
- 83.65
- Type
- conference paper
- Language
- en
- Open access
- closed
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