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Cited by 461

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Localization With Mobile Anchor Points in Wireless Sensor Networks

Ssu, K.-F., Ou, C.-H., Jiau, H.C.·2005·IEEE Transactions on Vehicular Technology

Abstract

Localization is one of the substantial issues in wireless sensor networks. Several approaches, including range-based and range-free, have been proposed to calculate positions for randomly deployed sensor nodes. With specific hardware, the range-based schemes typically achieve high accuracy based on either node-to-node distances or angles. On the other hand, the range-free mechanisms support coarse positioning accuracy with the less expense. This paper describes a range-free localization scheme using mobile anchor points. Each anchor point equipped with the GPS moves in the sensing field and broadcasts its current position periodically. The sensor nodes obtaining the information are able to compute their locations. With the scheme, no extra hardware or data communication is needed for the sensor nodes. Moreover, obstacles in the sensing fields can be tolerated. The localization mechanism has been implemented in the network simulator ns-2. The simulation results show that our scheme performed better than other range-free mechanisms.

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Metadata

Authors
Ssu, K.-F., Ou, C.-H., & Jiau, H. C.
Venue
IEEE Transactions on Vehicular Technology
Published
2005-05
Volume
54
Issue
3
Pages
1187-1197
ISSN
0018-9545 (ISSN-L)
OpenAlex
W2113411424
Cited by
461
References
1
FWCI
24.15
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Localization With Mobile Anchor Points in Wireless Sensor Networks. https://app.cochat.ai/papers/10.1109/tvt.2005.844642
The paper
Ssu, K.-F., Ou, C.-H., & Jiau, H. C. (2005). Localization With Mobile Anchor Points in Wireless Sensor Networks. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2005.844642

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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.