Hybrid RF mapping and Kalman filtered spring relaxation for sensor network localization
An accurate and low-cost hybrid solution to the problem of autonomous self-localization in wireless sensor networks (WSN) is presented. The solution is designed to perform robustly under challenging radio propagation conditions in mind, while requiring low deployment efforts, and utilizing only low-...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/96045 http://hdl.handle.net/10220/11365 |
| _version_ | 1826126771380027392 |
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| author | Fong, A. C. M. Seet, Boon-Chong Zhang, Qing Foh, Chuan Heng |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Fong, A. C. M. Seet, Boon-Chong Zhang, Qing Foh, Chuan Heng |
| author_sort | Fong, A. C. M. |
| collection | NTU |
| description | An accurate and low-cost hybrid solution to the problem of autonomous self-localization in wireless sensor networks (WSN) is presented. The solution is designed to perform robustly under challenging radio propagation conditions in mind, while requiring low deployment efforts, and utilizing only low-cost hardware and light-weight distributed algorithms for location computation. Our solution harnesses the strengths of two approaches for environments with complex propagation characteristics: RF mapping to provide an initial estimate of each sensor's position based on a coarse-grain RF map acquired with minimal efforts; and a cooperative light-weight spring relaxation technique for each sensor to refine its estimate using Kalman filtered inter-node distance measurements. Using Kalman filtering to pre-process noisy distance measurements inherent in complex propagation environments, is found to have significant positive impacts on the subsequent accuracy and the convergence of our spring relaxation algorithm. Through extensive simulations using realistic settings and real data set, we show that our approach is a practical localization solution which can achieve sub-meter accuracy and fast convergence under harsh propagation conditions, with no specialized hardware or significant efforts required to deploy. |
| first_indexed | 2024-10-01T06:57:47Z |
| format | Journal Article |
| id | ntu-10356/96045 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:57:47Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/960452020-05-28T07:17:54Z Hybrid RF mapping and Kalman filtered spring relaxation for sensor network localization Fong, A. C. M. Seet, Boon-Chong Zhang, Qing Foh, Chuan Heng School of Computer Engineering DRNTU::Engineering::Computer science and engineering An accurate and low-cost hybrid solution to the problem of autonomous self-localization in wireless sensor networks (WSN) is presented. The solution is designed to perform robustly under challenging radio propagation conditions in mind, while requiring low deployment efforts, and utilizing only low-cost hardware and light-weight distributed algorithms for location computation. Our solution harnesses the strengths of two approaches for environments with complex propagation characteristics: RF mapping to provide an initial estimate of each sensor's position based on a coarse-grain RF map acquired with minimal efforts; and a cooperative light-weight spring relaxation technique for each sensor to refine its estimate using Kalman filtered inter-node distance measurements. Using Kalman filtering to pre-process noisy distance measurements inherent in complex propagation environments, is found to have significant positive impacts on the subsequent accuracy and the convergence of our spring relaxation algorithm. Through extensive simulations using realistic settings and real data set, we show that our approach is a practical localization solution which can achieve sub-meter accuracy and fast convergence under harsh propagation conditions, with no specialized hardware or significant efforts required to deploy. 2013-07-15T02:35:49Z 2019-12-06T19:24:51Z 2013-07-15T02:35:49Z 2019-12-06T19:24:51Z 2011 2011 Journal Article https://hdl.handle.net/10356/96045 http://hdl.handle.net/10220/11365 10.1109/JSEN.2011.2173190 en IEEE sensors journal © 2011 IEEE. |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Fong, A. C. M. Seet, Boon-Chong Zhang, Qing Foh, Chuan Heng Hybrid RF mapping and Kalman filtered spring relaxation for sensor network localization |
| title | Hybrid RF mapping and Kalman filtered spring relaxation for sensor network localization |
| title_full | Hybrid RF mapping and Kalman filtered spring relaxation for sensor network localization |
| title_fullStr | Hybrid RF mapping and Kalman filtered spring relaxation for sensor network localization |
| title_full_unstemmed | Hybrid RF mapping and Kalman filtered spring relaxation for sensor network localization |
| title_short | Hybrid RF mapping and Kalman filtered spring relaxation for sensor network localization |
| title_sort | hybrid rf mapping and kalman filtered spring relaxation for sensor network localization |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/96045 http://hdl.handle.net/10220/11365 |
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