Robust Device-Free Wireless Localization Based on Differential RSS Measurements

Wang, Jie; Gao, Qinghua; Yu, Yan; Cheng, Peng; Wu, Lifei; Wang, Hongyu

doi:10.1109/tie.2012.2228145

Cited by 85 publications

(51 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In RASID system [21], Kosba et al improve the detection accuracy by adopting standard deviation of RSS as the feature approximates its distribution with a kernel function and leverages a nonparametric technique for adapting to environment changes. Since RSS signal is too sensitive to the slight variation of the environment, a robust device-free localization (DFL) scheme based on differential RSS is proposed [22], 2 Journal of Sensors which can eliminate the need of acquiring reference RSS measurements as well as overcome the negative effect environment induced. Since RSS schemes suffer from coarse granularity of 1 dB and limited accuracy, researchers begin to pay more attention to CSI-based schemes owing to its frequency diversity [11].…”

Section: Related Workmentioning

confidence: 99%

A Robust Passive Intrusion Detection System with Commodity WiFi Devices

Ding

Zhao

et al. 2018

Journal of Sensors

View full text Add to dashboard Cite

In recent years, due to the rapidly growing capacities of physical layer, device-free passive detection holds great importance for a broad range of application. Most recent works focus on motion detection, intrusion detection, and vital sign with commodity WiFi devices in the indoor environment. Conventional device-free motion detection techniques, which utilize received signal strength (RSS), may suffer from coarse granularity and high variability problems. In resorting to the finer-grained channel state information (CSI), we propose PhaseMode, a novel approach for device-free motion detection leveraging CSI phase difference data between adjacent antenna pairs. We implement our approach on commercial WiFi devices and validate its performance. We conduct experiments in different test periods of three indoor environments; the results show that the proposed scheme achieves an average accuracy over 99.4% of motion detection in different scenarios.

show abstract

Section: Related Workmentioning

confidence: 99%

A Robust Passive Intrusion Detection System with Commodity WiFi Devices

Ding

Zhao

et al. 2018

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…WSNs with various indoor localization algorithms such as RSS, time-of-arrival (ToA), time difference of arrival (TDoA) [21], and angle of arrival (AoA) [22] have been widely implemented. Recently, RSS-based indoor localization has been widely adopted because of its simple calculations and the fact that it effectively estimates the mobile node (MN) position without requiring any additional hardware [23]. Akeila et al [24] proposed an RSSI approach for indoor localization using Bluetooth nodes with a reduced accuracy error.…”

Section: Related Workmentioning

confidence: 99%

An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study

Khan

2017

Applied Sciences

View full text Add to dashboard Cite

Fatal accidents associated with underground coal mines require the implementation of high-level gas monitoring and miner's localization approaches to promote underground safety and health. This study introduces a real-time monitoring, event-reporting and early-warning platform, based on cluster analysis for outlier detection, spatiotemporal statistical analysis, and an RSS range-based weighted centroid localization algorithm for improving safety management and preventing accidents in underground coal mines. The proposed platform seamlessly integrates monitoring, analyzing, and localization approaches using the Internet of Things (IoT), cloud computing, a real-time operational database, application gateways, and application program interfaces. The prototype has been validated and verified at the operating underground Hassan Kishore coal mine. Sensors for air quality parameters including temperature, humidity, CH 4 , CO 2 , and CO demonstrated an excellent performance, with regression constants always greater than 0.97 for each parameter when compared to their commercial equivalent. This framework enables real-time monitoring, identification of abnormal events (>90%), and verification of a miner's localization (with <1.8 m of error) in the harsh environment of underground mines. The main contribution of this study is the development of an open source, customizable, and cost-effective platform for effectively promoting underground coal mine safety. This system is helpful for solving the problems of accessibility, serviceability, interoperability, and flexibility associated with safety in coal mines.

show abstract

“…Wilson and Patwari [3] proposed the RTI system, which exploits the redundancy introduced by sensor arrays surrounding the monitored area to visualize the target induced RSS fluctuations. Since the RSS signal is sensitive and a slight variation of the environment will lead to a large localization error, Wang et al [2] proposed a robust DFL scheme based on the differential RSS, which overcomes the negative effect incurred by the environment. In order to reduce the number of measurements required by DFL systems while maintaining the high localization accuracy, the work in [4] and [5] formulated DFL as a sparse signal reconstruction problem by taking advantage of CS in sparse recovery to handle the sparsity property of the localization problem.…”

Section: Related Workmentioning

confidence: 99%

“…1 (b) shows that the distributions of the RSS changes distorted by different targets differ significantly from each other even when residing at the same location. This observation implies that the traditional localization models (e.g., trained database [1], [2], [6], shadowing loss [3], or sensing matrix [4], [5]) are bound to fail, if we use one category of targets for building the localization model and another category of targets for testing. For example, Fig.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Transferring Compressive-Sensing-Based Device-Free Localization Across Target Diversity

Liu

Fang

et al. 2015

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

Device-free localization (DFL) plays an important role in many applications, such as wildlife population and migration tracking. Most of current DFL systems leverage the distorted received signal strength (RSS) changes to localize the target(s). However, they assume a fixed distribution of the RSS change measurements, although they are distorted by different types of targets. It inevitably causes the localization to fail if the targets for modeling and testing belong to different categories. This paper presents TLCS-a transferring compressive sensing based DFL approach-which employs a rigorously designed transferring function to transfer the distorted RSS changes across different categories of targets into a latent feature space, where the distributions of the distorted RSS change measurements from different categories of targets are unified. A benefit of this approach is that the same transferred sensing matrix can be shared by different categories of targets, leading to a substantial reduction in the human efforts. The results of experiments illustrate the efficacy of the TLCS.

show abstract

Robust Device-Free Wireless Localization Based on Differential RSS Measurements

Cited by 85 publications

References 37 publications

A Robust Passive Intrusion Detection System with Commodity WiFi Devices

A Robust Passive Intrusion Detection System with Commodity WiFi Devices

An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study

Transferring Compressive-Sensing-Based Device-Free Localization Across Target Diversity

Contact Info

Product

Resources

About