Received Signal Strength Difference Research Articles

Three-Dimensional Node Localization Algorithm for WSN Based on Differential RSS Irregular Transmission Model

The localization accuracy of the conventional three- dimensional (3D) node localization algorithm based on received signal strength (RSS) is restricted by the random signal strength fluctuation caused by the irregular propagation environments. In this paper, we propose an improved localization algorithm based on a differential RSS distance estimation algorithm to minimize the influence of the path loss exponent error. Furthermore, considering the influence of degree of irregularity (DOI), we propose a new radio model and obtain numerically the relation between DOI and the variation of signal transmission ranges. A spherical shell with certain finite thickness is then used to characterize the transmission range irregularity. The simulation results show the path loss exponent has no effect on localization error in the proposed algorithm. The localization errors for various DOIs are significantly lower than the conventional 3D algorithm for different densities of anchor nodes.

Robust Device-Free Wireless Localization Based on Differential RSS Measurements

As an emerging technique with a promising application prospect, the device-free localization (DFL) technique has drawn considerable attention due to its ability of realizing wireless localization without the need of equipping the target with any device. The DFL technique detects the shadowed links and realizes localization with the received signal strength (RSS) measurements of these links. However, one major disadvantage of the DFL technique is that the RSS signal is too sensitive, and a slight variation of the environment will cause the variation of RSS measurements, which incurs the misjudgment of shadowed links and degradation of localization performance. To solve this problem, a robust DFL scheme based on differential RSS is proposed. The scheme utilizes the novel differential RSS to judge whether a link is shadowed, which not only eliminates the need of acquiring reference RSS measurements but also overcomes the negative effect incurred by the environment. Meanwhile, an outlier detection scheme is presented to filter out the outlier links that are far away from the target. We present the observation model of the shadowed links and incorporate it into the particle filter framework to realize location estimation robustly. Experimental results demonstrate the outstanding performance of the proposed scheme.

Accurate and simple source localization using differential received signal strength

Locating an unknown-position source using received signal strength (RSS) measurements in an accurate and low-complexity manner is addressed in this paper. Given that the source transmit power is unknown, we employ the differential RSS information to devise two computationally attractive localization methods based on the weighted least squares (WLS) approach. The main ingredients in the first algorithm development are to obtain the unbiased estimates of the squared ranges and introduce an extra variable. The second method improves the first version by implicitly exploiting the relationship between the extra variable and source location through a second WLS step. The performance of the two estimators is analyzed in the presence of zero-mean white Gaussian disturbances. Numerical examples are also included to evaluate their localization accuracy by comparing with the maximum likelihood approach and Cramer-Rao lower bound.

Modeling and Mitigating Noise and Nuisance Parameters in Received Signal Strength Positioning

Localization via received signal strength (RSS) is often employed in cases where the received signal is fairly weak, either due to distance or due to deliberate covert operation or interference avoidance. However, most research on source localization via RSS implicitly assumes that the background noise is negligible, and that parameters of the transmitter and environment are known. Many commercial chipsets provide per-frame RSS measurements obtained when demodulating the signal, which do not include background noise; however, noise can still cause signal outages. In law enforcement, surveillance, and emergency situations, RSS may be obtained more crudely, such as by energy detection, in which case the RSS will include contributions from the background noise as well. This paper proposes new probabilistic RSS models that account for background noise in both types of RSS measurements. We also derive and evaluate maximum likelihood estimators (MLEs) for these new models, as well as for differential RSS, which has hitherto not been rigorously analyzed in the literature. Several of these MLEs are extended to estimate the transmit power and/or path loss if they are unknown. The new models are justified by extensive measured data.

Improved Localization Algorithm Based on Proportion of Differential RSSI

How to enhance the accuracy of sensor node self-localization for limited energy resource networks is an important problem in the study of wireless sensor networks (WSNs). Concerning the advantages and disadvantages of some main algorithms for senor node self-localization, an easy and simple algorithm is proposed to locate the unknown node itself. The algorithm is to improve weight centroid localization (WCL), by the way of determining weight through using the proportion of differential received signal strength indicator (RSSI) that are derived from unknown node and criterion nodes. In contrast to WCL, the algorithm has the strengths of less computation and better determination of weight, and the determination of weight shows more distinguished distinction in the effect on the localization of unknown node, which is caused by various beacon nodes. Simulations demonstrate that the algorithm has a higher localization accuracy than WCL

실환경 CR 시스템에서 오경보 감소를 위한 신호 검출 알고리즘

FCC의 TV white space 개방과 관련하여 스펙트럼 센싱에 대한 관심이 높아지면서 다양한 센싱 기법들이 제안되고는 있으나 대부분 실환경을 고려하지 않아 실제 모듈 적용시 원하는 성능을 얻기가 어렵다. 본 논문에서는 센싱 모듈의 실환경 적용시 발생할 수 있는 문제점과 이를 제어할 수 있는 방법을 제안하고 전산 모의 실험을 통해 성능을 검증하였다. 우선 시스템 내부 스퓨리어스 성분으로 인한 오경보 가능성을 줄이기 위해 시스템 인식을 통한 채널 상태 정보를 이용하여 수산 신호와 구별할 수 있는 방법을 제안하였고, 다음으로 수신 신호에 여러 개의 협대역 신호가 공존하는 경우에 각 신호의 크기 차이로 발생할 수 있는 미검출 가능성을 줄일 수 있도록 서브밴드 정규화 방식을 제안하였다. 전산 모의 실험 결과 시스템 인식 과정을 통해 스퓨리어스 성분으로 인한 오경보는 발생하지 않았으며, 서브밴드 정규화를 통해 기존 채널 단위의 정규화 방식으로 인해 신호간의 크기차로 발생했던 검출 성능의 차이가 신호간의 크기 차이에 상관없이 신호 자체 크기에 따라 검출 성능이 나타나는 것을 확인할 수 있었다. After permission for utilization of TV white space by FCC, a lot of attentions are focused on spectrum sensing, and various spectrum sensing methods have been proposed. However, they do not consider real environment, thus they are hard to achieve the required performance. In this paper, we propose resolutions for the problem which could be occurred in implementation of spectrum sensing module and verify performance of the proposed methods with computer simulation. The first proposed method utilizes channel status information to separate received signal and spurious for reducing false alarm probability caused by system internal spurious. The another proposed scheme is subband normalization method to prevent miss detection caused by multiple narrow band signals with different received signal strength. The simulation results verify that we can prevent false alarm cause by spurious components with the proposed system internal spurious cognition. Moreover, the proposed subband normalization method shows that it could overcome performance degradation caused by received signal strength difference.

Securing Wi-Fi networks with position verification: extended version

In this paper we investigate position-based enhancements to Wi-Fi network security. Specifically, we investigate whether Received Signal Strength (RSS) measurements can identify attempts at network access by malicious nodes exterior to an authorised network perimeter. We assume the malicious nodes will spoof their received or transmitted power-levels in attempts to circumvent standard position-based security techniques. We outline why residual analysis of the RSS measurements cannot robustly identify illegal network access requests. However, we show that by referring the residual RSS analysis to a 'claimed position', interior to the authorised perimeter, a robust position-based verification system for secure network access can be developed. Indoor systems based on RSS fingerprints and differential RSS fingerprints are studied. Outdoor systems under the assumption of log-normal shadowing are also investigated.