The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
목표 위치를 추정하는 기능은 무선 센서 네트워크의 많은 응용 분야에서 필수적입니다. 무선 센서 네트워크에서 RSSI(Received Signal Strength Indicator) 기반의 ML(Maximum Likelihood) 방식은 일반적으로 감지 영역 내 RSSI 변화에 대해 미리 정해진 통계 모델이 필요하며 이를 ML 함수로 사용하여 위치를 추정할 수 있다. 감지 영역의 대상. 그러나 타겟의 위치를 추정할 때 여러 가지 이유로 인해 통계 모델을 따르지 않는, 즉 통계 모델에서 이상치인 RSSI를 측정하는 경우가 많습니다. 결과적으로 이상치 RSSI 데이터의 영향으로 인해 추정 정확도가 악화됩니다. 무선 센서 네트워크에 센서 노드가 많으면 이러한 이상치 RSSI를 의도적으로 거부하여 추정 정확도를 향상시킬 수 있습니다. 본 논문에서는 ML 위치 추정을 위한 간단한 이상치 RSSI 데이터 거부 알고리즘을 제안합니다. 제안된 알고리즘은 이상치 RSSI를 측정하는 앵커 노드를 반복적으로 제거합니다. 이전에 제안된 이상치 RSSI 데이터 거부 알고리즘을 사용한 위치 추정 방법과 비교하여 제안한 방법은 훨씬 적은 계산 복잡도로 더 나은 성능을 발휘합니다.
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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
부
@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},}
부
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 -