Tool release

Halperin, Daniel; Hu, Wenjun; Sheth, Anmol; Wetherall, David

doi:10.1145/1925861.1925870

Cited by 1,453 publications

(200 citation statements)

References 2 publications

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“…In the experiments, the Intel5300 (Intel Corporation, Santa Clara, CA, USA) Wi-Fi NIC (Network Interface Card) equipped with three antennas (see Figure 7) is selected as the receiver and the CSI is collected by using the Linux CSI tool, as discussed in [32]. The NIC supports 30 subcarriers for each antenna.…”

Section: Resultsmentioning

confidence: 99%

WIPP: Wi-Fi Compass for Indoor Passive Positioning with Decimeter Accuracy

et al. 2016

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Abstract:In recent decades, the proliferation of smart phones, tablets, and wireless networks has fostered a growing interest in indoor passive positioning. The Wi-Fi-based passive positioning systems can provide Location-Based Services (LBSs) to the third party such as market and security departments. Most of the existing systems are based on the Receive Signal Strength Indication (RSSI) information, which are generally time-consuming and susceptible to environmental change. To overcome this problem, we propose the Wi-Fi compass for indoor passive positioning system (WIPP), an Angle of Arrival (AOA) based passive positioning system using the existing commodity Wi-Fi network. In this paper, we first propose a new algorithm for the joint estimation of AOA and Time of Arrival (TOA) measurements based on the fine-grained Channel State Information (CSI), which is collected by an off-the-shelf Wi-Fi device equipped with only three antennas. Second, we use the affinity propagation clustering algorithm to identify the direct signal path from the target to each Wi-Fi Access Point (AP). Finally, we deploy the WIPP in an actual indoor environment to conduct the performance comparison with the well-known radio-frequency (RF) based system for locating and tracking users inside buildings (RADAR), as well as the conventional passive positioning system using the AOA solely. The experimental results show that the WIPP is able to achieve the median positioning error 0.7 m, which is much lower than the ones by the RADAR system and the conventional system using the AOA solely.

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Section: Resultsmentioning

confidence: 99%

WIPP: Wi-Fi Compass for Indoor Passive Positioning with Decimeter Accuracy

et al. 2016

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“…The laptop is installed with Ubuntu version 14.04 and run the Open Source CSI-Tools [21]. As is known in the OFDM system, we can get 2 × 3 the Multiple-Input and Multiple-Output (MIMO) system.…”

Section: Preprocessingmentioning

confidence: 99%

“…Thus, we get six streams. In each stream, there are 30 subcarriers because 5300 NIC reports CSI 30 groups of subcarriers for each stream [21]. Thus, the CSI we get can be represented as Equation (1):…”

Section: Preprocessingmentioning

confidence: 99%

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Device-Free Indoor Activity Recognition System

Al-qaness

et al. 2016

Applied Sciences

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Abstract:In this paper, we explore the properties of the Channel State Information (CSI) of WiFi signals and present a device-free indoor activity recognition system. Our proposed system uses only one ubiquitous router access point and a laptop as a detection point, while the user is free and neither needs to wear sensors nor carry devices. The proposed system recognizes six daily activities, such as walk, crawl, fall, stand, sit, and lie. We have built the prototype with an effective feature extraction method and a fast classification algorithm. The proposed system has been evaluated in a real and complex environment in both line-of-sight (LOS) and none-line-of-sight (NLOS) scenarios, and the results validate the performance of the proposed system.

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“…An antenna which is in the range of S-Band (2 GHz to 4 GHz) was deployed as the transmitter and a 2.4 GHz omni-directional antenna working as a receiver. The wireless channel information extraction tool introduced by [16] was installed. The host computer receive 10 WCI packets per second [17].…”

Section: Introductionmentioning

confidence: 99%

Respiration Symptoms Monitoring in Body Area Networks

et al. 2018

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This work presents a framework that monitors particular symptoms such as respiratory conditions (abnormal breathing pattern) experienced by hyperthyreosis, sleep apnea, and sudden infant death syndrome (SIDS) patients. The proposed framework detects and monitors respiratory condition using S-Band sensing technique that leverages the wireless devices such as antenna, card, omni-directional antenna operating in 2 GHz to 4 GHz frequency range, and wireless channel information extraction tool. The rhythmic patterns extracted using S-Band sensing present the periodic and non-periodic waveforms that correspond to normal and abnormal respiratory conditions, respectively. The fine-grained amplitude information obtained using aforementioned devices is used to examine the breathing pattern over a period of time and accurately identifies the particular condition.

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Tool release

Cited by 1,453 publications

References 2 publications

WIPP: Wi-Fi Compass for Indoor Passive Positioning with Decimeter Accuracy

WIPP: Wi-Fi Compass for Indoor Passive Positioning with Decimeter Accuracy

Device-Free Indoor Activity Recognition System

Respiration Symptoms Monitoring in Body Area Networks

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