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The ability to estimate a target location is essential in many applications of wireless sensor networks. Received signal strength indicator (RSSI)-based maximum likelihood (ML) method in a wireless sensor network usually requires a pre-determined statistical model on the variation of RSSI in a sensing area and uses it as an ML function when estimating the location of a target in the sensing area. However, when estimating the location of a target, due to several reasons, we often measure the RSSIs which do not follow the statistical model, in other words, which are outlier on the statistical model. As the result, the effect of the outlier RSSI data worsens the estimation accuracy. If the wireless sensor network has a lot of sensor nodes, we can improve the estimation accuracy intentionally rejecting such outlier RSSIs. In this paper, we propose a simple outlier RSSI data rejection algorithm for an ML location estimation. The proposed algorithm iteratively eliminates the anchor nodes which measure outlier RSSIs. As compared with the location estimation methods with previously proposed outlier RSSI data rejection algorithms, our proposed method performs better with much less computational complexity.

- Publication
- IEICE TRANSACTIONS on Communications Vol.E91-B No.11 pp.3442-3449

- Publication Date
- 2008/11/01

- Publicized

- Online ISSN
- 1745-1345

- DOI
- 10.1093/ietcom/e91-b.11.3442

- Type of Manuscript
- Special Section PAPER (Special Section on Emerging Technologies for Practical Ubiquitous and Sensor Networks)

- Category

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Daisuke ANZAI, Shinsuke HARA, "Experimental Evaluation of a Simple Outlier RSSI Data Rejection Algorithm for Location Estimation in Wireless Sensor Networks" in IEICE TRANSACTIONS on Communications,
vol. E91-B, no. 11, pp. 3442-3449, November 2008, doi: 10.1093/ietcom/e91-b.11.3442.

Abstract: The ability to estimate a target location is essential in many applications of wireless sensor networks. Received signal strength indicator (RSSI)-based maximum likelihood (ML) method in a wireless sensor network usually requires a pre-determined statistical model on the variation of RSSI in a sensing area and uses it as an ML function when estimating the location of a target in the sensing area. However, when estimating the location of a target, due to several reasons, we often measure the RSSIs which do not follow the statistical model, in other words, which are outlier on the statistical model. As the result, the effect of the outlier RSSI data worsens the estimation accuracy. If the wireless sensor network has a lot of sensor nodes, we can improve the estimation accuracy intentionally rejecting such outlier RSSIs. In this paper, we propose a simple outlier RSSI data rejection algorithm for an ML location estimation. The proposed algorithm iteratively eliminates the anchor nodes which measure outlier RSSIs. As compared with the location estimation methods with previously proposed outlier RSSI data rejection algorithms, our proposed method performs better with much less computational complexity.

URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.11.3442/_p

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@ARTICLE{e91-b_11_3442,

author={Daisuke ANZAI, Shinsuke HARA, },

journal={IEICE TRANSACTIONS on Communications},

title={Experimental Evaluation of a Simple Outlier RSSI Data Rejection Algorithm for Location Estimation in Wireless Sensor Networks},

year={2008},

volume={E91-B},

number={11},

pages={3442-3449},

abstract={The ability to estimate a target location is essential in many applications of wireless sensor networks. Received signal strength indicator (RSSI)-based maximum likelihood (ML) method in a wireless sensor network usually requires a pre-determined statistical model on the variation of RSSI in a sensing area and uses it as an ML function when estimating the location of a target in the sensing area. However, when estimating the location of a target, due to several reasons, we often measure the RSSIs which do not follow the statistical model, in other words, which are outlier on the statistical model. As the result, the effect of the outlier RSSI data worsens the estimation accuracy. If the wireless sensor network has a lot of sensor nodes, we can improve the estimation accuracy intentionally rejecting such outlier RSSIs. In this paper, we propose a simple outlier RSSI data rejection algorithm for an ML location estimation. The proposed algorithm iteratively eliminates the anchor nodes which measure outlier RSSIs. As compared with the location estimation methods with previously proposed outlier RSSI data rejection algorithms, our proposed method performs better with much less computational complexity.},

keywords={},

doi={10.1093/ietcom/e91-b.11.3442},

ISSN={1745-1345},

month={November},}

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TY - JOUR

TI - Experimental Evaluation of a Simple Outlier RSSI Data Rejection Algorithm for Location Estimation in Wireless Sensor Networks

T2 - IEICE TRANSACTIONS on Communications

SP - 3442

EP - 3449

AU - Daisuke ANZAI

AU - Shinsuke HARA

PY - 2008

DO - 10.1093/ietcom/e91-b.11.3442

JO - IEICE TRANSACTIONS on Communications

SN - 1745-1345

VL - E91-B

IS - 11

JA - IEICE TRANSACTIONS on Communications

Y1 - November 2008

AB - The ability to estimate a target location is essential in many applications of wireless sensor networks. Received signal strength indicator (RSSI)-based maximum likelihood (ML) method in a wireless sensor network usually requires a pre-determined statistical model on the variation of RSSI in a sensing area and uses it as an ML function when estimating the location of a target in the sensing area. However, when estimating the location of a target, due to several reasons, we often measure the RSSIs which do not follow the statistical model, in other words, which are outlier on the statistical model. As the result, the effect of the outlier RSSI data worsens the estimation accuracy. If the wireless sensor network has a lot of sensor nodes, we can improve the estimation accuracy intentionally rejecting such outlier RSSIs. In this paper, we propose a simple outlier RSSI data rejection algorithm for an ML location estimation. The proposed algorithm iteratively eliminates the anchor nodes which measure outlier RSSIs. As compared with the location estimation methods with previously proposed outlier RSSI data rejection algorithms, our proposed method performs better with much less computational complexity.

ER -