• 디지털융복합연구
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• 제11권11호
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• pp.321-326
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• 2013

GPS는 실내에서 수신률이 떨어지는 특성을 가지고 있다. 이러한 단점을 극복하기 위해 AP의 RSS를 이용한 측위기술에 대한 연구와 개발이 이루어지고 있다. 측위기술과 더불어 신호 세기만을 가지고 장애물까지 판단할 수 있다면 활용도와 효율성 측면에서 이를 응용한 서비스를 하는 입장에서도 별다른 구축비용이 들지 않는다는 장점이 있다. 본 논문에서는 RSS(Received Signal Strength)를 이용하여 장애물을 판단하는 방법을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권1호
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• pp.45-61
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• 2010

In the communication channel, the received signal is affected by many factors that can cause errors. These effects mean that received signal strength (RSS) based methods incur more errors in measuring distance and consequently result in low precision in the location detection process. As one of the approaches to overcome these problems, we propose using direction calibration to improve the performance of the RSS-based method for distance measurement, and sequentially a weighted least squares (WLS) method using reliability factors in conjunction with a conventional RSS weighting matrix is proposed to solve an over-determined localization process. The proposed scheme focuses on the features of the RSS method to improve the performance, and these effects are proved by the simulation results.

• 정보처리학회논문지C
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• 제14C권3호
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• pp.305-312
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• 2007

무선 LAN 시스템에서 고속 핸드오프를 위한 계층 2 트리거는 정확한 핸드오프 예측 모델을 요구한다. 이에 따라 본 논문은 계층 2 트리거 발생기 설계를 위한 단말의 이동성 모델로 수신 신호 강도 (received signal strength: RSS) 예측 모델을 제안한다. 제안 모델은 짧은 시간 동안 사용자 단말과 억세스 포인트 (AP) 사이의 거리 변화양은 물리적으로 제한된다는 사실을 이용하여 일정 시간 동안 측정된 RSS 값들에 대해 적응성 있게 동작한다. 제안 모델 설계를 위해 우선 ns 2 모의 실험을 통해 측정된 RSS 데이터를 통계적으로 분석하여 일정 시간 측정된 RSS 데이터는 차수 1인 자기 회기 (autoregressive: AR(1)) 프로세스로 모델링 할 수 있다는 것을 검증하였다. 이후 AR(1) 프로세스를 이용하여 향후 RSS 값을 예측하는 방법을 제시하고 예측 오류를 확률적으로 정량화 하였으며 모의 실험을 통해 현재까지 측정된 RSS 값들을 이용하여 적어도 1-step 이후의 RSS 값을 예측할 수 있다는 것을 검증하였다.

• Journal of Electrical Engineering and Technology
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• 제7권5호
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• pp.789-796
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• 2012

Fingerprinting is a widely used positioning technology for received signal strength (RSS) based wireless local area network (WLAN) positioning system. Though spatial RSS variation is the key factor of the positioning technology, temporal RSS variation needs to be considered for more accuracy. To deal with the spatial and temporal RSS characteristics within a unified framework, this paper proposes an extended signal propagation mode (ESPM) and a fingerprint generation method. The proposed spatiotemporal fingerprint generation method consists of two algorithms running in parallel; Kalman filtering at several measurement-sampling locations and Kriging to generate location fingerprints at dense reference locations. The two different algorithms are connected by the extended signal propagation model which describes the spatial and temporal measurement characteristics in one frame. An experiment demonstrates that the proposed method provides an improved positioning accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권8호
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• pp.2663-2674
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• 2014

For cognitive radio sensor networks (CRSNs) that use underlay-based spectrum access, the location of the primary user (PU) plays an important role in the power control of the secondary users (SUs), because the SUs must keep the minimum interference level required by the PU. Received signal strength (RSS)-based localization schemes provide low-cost implementation and low complexity, thus it is suitable for the PU localization in CRSNs. However, the RSS-based localization schemes have a high localization error because they use an inexact path loss exponent (PLE). Thus, applying a RSS-based localization scheme into the PU localization would cause a high interference to the PU. In order to reduce the localization error and improve the channel reuse rate, we propose a RSS-based PU localization scheme that uses distance calibration for CRSNs using underlay model-based spectrum access. Through the simulation results, it is shown that the proposed scheme can provide less localization error as well as more spectrum utilization than the RSS-based PU localization using the mean and the maximum likelihood calibration.

• Journal of Positioning, Navigation, and Timing
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• 제11권4호
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• pp.361-368
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• 2022

An indoor navigation system that utilizes long-term evolution (LTE) signals has the benefit of no additional infrastructure installation expenses and low base station database management costs. Among the LTE signal measurements, received signal strength (RSS) is particularly appealing because it can be easily obtained with mobile devices. Propagation channel models can be used to estimate the position of mobile devices with RSS. However, conventional channel models have a shortcoming in that they do not discriminate between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions of the received signal. Accordingly, a previous study has suggested separated LOS and NLOS channel models. However, a method for determining LOS and NLOS conditions was not devised. In this study, a machine learning-based LOS/NLOS classification method using RSS measurements is developed. We suggest several machine-learning features and evaluate various machine-learning algorithms. As an indoor experimental result, up to 87.5% classification accuracy was achieved with an ensemble algorithm. Furthermore, the range estimation accuracy with an average error of 13.54 m was demonstrated, which is a 25.3% improvement over the conventional channel model.

• 대한임베디드공학회논문지
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• 제5권3호
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• pp.152-156
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• 2010

Non-line-of-sight (NLOS) propagation is one of the challenges in radio positioning. Distinguishing the transmission status of the communication as line-of-sight (LOS) or NLOS is of great importance for the wireless communication systems. This paper focuses on the identification of NLOS based on time-of-arrival (TOA) distance estimates and the received signal strength (RSS) measurements. We set a path loss threshold based on the joint TOA and RSS based NLOS detection method to determine LOS or NLOS. Simulation results show that the proposed method ensures the correct of detection for the LOS condition and can improve the NLOS identification for the weak noise and long distance.

• 한국정보과학회논문지:정보통신
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• 제37권6호
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• pp.489-493
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• 2010

래터레이션 기반 실내 위치 측위 기법은 단말기와 AP(Anchor Point) 사이의 거리 예측을 위하여 RSS(Received Signal Strength)를 주로 사용한다. 그러나 무선 신호는 시간과 공간에 따라 무작위로 변화하는 특성을 가지므로 RSS를 이용한 거리 예측에서 오류의 발생은 불가피하다. 단말기와 AP사이의 거리 예측 정확도는 단말기 위치 예측 정확도에 많은 영향을 미치게 되므로 기존 기법들은 이를 해결하기 위하여 다수의 AP를 사용하였다. 그러나 많은 실험 결과들은 다수의 AP 사용보다는 경로 손실 모델에 잘 부합하는 RSS 측정 값을 가진 AP 즉 기준 AP 만을 선택하여 이용하는 것이 위치 예측 정확도를 향상시킬 수 있는 방법임을 보였다. 따라서 본 논문에서는 실내 환경에서 단말기의 측위 정확도활 향상시키기 위한 기준 AP 선택 기법과 선택된 기준 AP들을 이용한 적응적 거리 예측 기법을 제안한다. 또한 실내 위치 측위 시스템을 구현하여 다양한 실험 환경에서의 실험함으로써 제안 기법의 타당성을 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4418-4437
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• 2017

In this paper a three-phase secure compressive sensing (CS) and received signal strength (RSS) based target localization approach is proposed to mitigate the effect of malicious node attack. RSS measurements are first arranged into a group of subsets where the same measurement can be included in multiple subsets. Intermediate target position estimates are then produced using individual subsets of RSS measurements and the CS technique. From the intermediate position estimates, the residual error vector and residual error square vector are formed. The least median of residual error square is utilized to define a verifier parameter. The selected residual error vector is utilized along with a threshold to determine whether a node or measurement is under attack. The final target positions are estimated by using only the attack-free measurements and the CS technique. Further, theoretical analysis is performed for parameter selection and computational complexity evaluation. Extensive simulation studies are carried out to demonstrate the advantage of the proposed CS-based secure localization approach over the existing algorithms.

• International Journal of Internet, Broadcasting and Communication
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• 제12권4호
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• pp.226-231
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• 2020

In order to maximize the production of landscape plants in optimal condition while coexisting with the environment in terms of precision agriculture, quick and accurate information gathering of the internal environmental elements of the growing container is necessary. This may depend on the accuracy of the positioning of numerous sensors connected to landscape plants cultivating system (LPCS) in containers. Thus, this paper presents a method for estimating the location of the sensors related to cultivation environment connected to LPCS by measuring the received signal strength (RSS) or time of arrival TOA received between oneself and adjacent sensors. The Small sensors connected to the LPCS of container are known for their locations, but the remaining locations must be estimated. For this in the paper, Rao-Cramer limits and maximum likelihood estimators are derived from Gaussian models and lognormal models for TOA and RSS measurements, respectively. As a result, this study suggests that both RSS and TOA range measurements can produce estimates of the exact locations of the cultivation environment sensors within the wireless sensor network related to the LPCS.