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Journal ArticleDOI

Tool release: gathering 802.11n traces with channel state information

22 Jan 2011-Vol. 41, Iss: 1, pp 53-53
TL;DR: The measurement setup comprises the customized versions of Intel's close-source firmware and open-source iwlwifi wireless driver, userspace tools to enable these measurements, access point functionality for controlling both ends of the link, and Matlab scripts for data analysis.
Abstract: We are pleased to announce the release of a tool that records detailed measurements of the wireless channel along with received 802.11 packet traces. It runs on a commodity 802.11n NIC, and records Channel State Information (CSI) based on the 802.11 standard. Unlike Receive Signal Strength Indicator (RSSI) values, which merely capture the total power received at the listener, the CSI contains information about the channel between sender and receiver at the level of individual data subcarriers, for each pair of transmit and receive antennas.Our toolkit uses the Intel WiFi Link 5300 wireless NIC with 3 antennas. It works on up-to-date Linux operating systems: in our testbed we use Ubuntu 10.04 LTS with the 2.6.36 kernel. The measurement setup comprises our customized versions of Intel's close-source firmware and open-source iwlwifi wireless driver, userspace tools to enable these measurements, access point functionality for controlling both ends of the link, and Matlab (or Octave) scripts for data analysis. We are releasing the binary of the modified firmware, and the source code to all the other components.

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Citations
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Proceedings ArticleDOI
01 Dec 2019
TL;DR: This work approaches the problem by analyzing the Wi-Fi packets for counting people and estimating their position within a pre-defined accuracy, and proposes improvised counting techniques that results in people counts close to 75% of the ground truth.
Abstract: Many IoT applications require the knowledge of crowd distribution, particularly in indoor scenarios. To this end, we leverage the lighting grid infrastructure in buildings by using smart light bulbs, which can include variety of sensors, in these grids. The exponentially increasing adoption of smartphones and the Wi-Fi infrastructure has motivated us to tackle this problem using Wi-Fi. As we seek a solution that works in many buildings, relying on active user participation or installing apps is not an option. Therefore, we need a Wi-Fi based crowd distribution estimation technique that is non-participatory and non-intrusive, and works with very few Wi-Fi packets generated by users' smartphones sporadically. We approach the problem by analyzing the Wi-Fi packets for counting people (smartphones) and estimating their position within a pre-defined accuracy. To this end, extensive experiments are conducted in a real-world testbed with controlled settings as well as in test setups in office spaces with no control. We propose improvised counting techniques that results in people counts close to 75% of the ground truth. We further propose improvements to range-free localization techniques to refine the position estimation accuracy and reduce the execution time. Our algorithm estimates the location with an accuracy of 2m 74% of the time, when Wi-Fi sniffers are placed in bulbs every 4m in the grid.

3 citations


Cites methods from "Tool release: gathering 802.11n tra..."

  • ...These chips require modification of the underlying software stack [12]....

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Book ChapterDOI
10 Oct 2016
TL;DR: WiCare is a non-intrusive and device-free healthcare service system based on ubiquitous WiFi signals that extracts Channel State Information (CSI) from the physical layer and detects the unique variations of CSI values caused by human activities.
Abstract: To help the independent-living elders or patients nursed in a single isolated ward, we propose a proof-of-concept prototype named WiCare, a non-intrusive and device-free healthcare service system based on ubiquitous WiFi signals. It extracts Channel State Information (CSI) from the physical layer and detects the unique variations of CSI values caused by human activities. We implement WiCare on two laptops equipped with the commercial 802.11n network interface cards. Two potential application scenarios are considered: a living room and a bedroom. The results demonstrate that the proposed scheme achieves overall recognition accuracies of 92.3 % in living room and 87.6 % in bedroom with low false positive rates. Moreover, WiCare can send alarm messages when the server recognizes the occurrences of emergency activities, which assist the users in getting help as quickly as possible.

3 citations


Cites methods from "Tool release: gathering 802.11n tra..."

  • ...The receiver is equipped with Intel 5300 network interface card (NIC), which is modified by the tools in [4], to obtain CSI measurements from the physical layer....

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  • ...Each MP has 3 omni-directional antennas and its firmware is modified as [4] to report the CSI values, and the APs are also installed with 3 antennas....

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Journal ArticleDOI
TL;DR: The design and implementation of DARMS is presented, a Device-free human Activity Recognition and Monitoring System that can be deployed with low-cost commodity WiFi devices that can accurately distinguish various daily activities without the user wearing any sensor.
Abstract: Human activity recognition plays a significant role in smart building applications, healthcare services, and security monitoring. In particular, WiFi-based indoor wireless sensing system becomes increasingly popular due to its noninvasiveness. This work presents the design and implementation of DARMS, a Device-free human Activity Recognition and Monitoring System that can be deployed with low-cost commodity WiFi devices. DARMS is a passive wireless sensing system, and it can accurately distinguish various daily activities without the user wearing any sensor. DARMS makes two key technical contributions. First, an effective signal processing methodology is designed to extract the CSI features both in the time domain and frequency domain. Second, a dual-channel neural network that combines temporal and frequency information is proposed to achieve fine-grained activity recognition. In our experiments, DARMS shows outstanding performance in different indoor environments, with an average accuracy of 96.9% for fall detection and 93.3% for human activity recognition.

3 citations

Patent
31 Aug 2016
TL;DR: In this article, a moving object-reflected wireless signal identifying method is proposed, which can be implemented in common commercial wireless equipment, rapid and convenient implementation can be achieved, and low cost and high benefits can be realized.
Abstract: The invention discloses a moving object-reflected wireless signal identifying method. An emitting end in a wireless transmission environment is a wireless signal emitting device, and a receiving end in the wireless transmission environment is a wireless signal receiver corresponding to the emitting end. The moving object-reflected wireless signal identifying method is characterized in that devices at the wireless signal emitting end and the wireless signal receiving end are not required, signals can be received via an antenna array of the wireless signal receiving end, a direct path signal and a static object reflection path signal are combined into a static path signal via a signal processing method, an arrival angle of the static path signal and an arrival angle of a moving object reflection path signal can be automatically identified in signal identifying processes, and therefore a moving object-reflected wireless signal can be identified. A technical solution of the moving object-reflected wireless signal identifying method is low in equipment requirements, hardware change is not required, the method can be implemented in common commercial wireless equipment, rapid and convenient implementation can be achieved, and low cost and high benefits can be realized.

3 citations

References
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Proceedings ArticleDOI
30 Aug 2010
TL;DR: It is shown that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide, and the rate prediction is as good as the best rate adaptation algorithms for 802.
Abstract: RSSI is known to be a fickle indicator of whether a wireless link will work, for many reasons. This greatly complicates operation because it requires testing and adaptation to find the best rate, transmit power or other parameter that is tuned to boost performance. We show that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide. Our model uses 802.11n Channel State Information measurements as input to an OFDM receiver model we develop by using the concept of effective SNR. It is simple, easy to deploy, broadly useful, and accurate. It makes packet delivery predictions for 802.11a/g SISO rates and 802.11n MIMO rates, plus choices of transmit power and antennas. We report testbed experiments that show narrow transition regions (

697 citations


"Tool release: gathering 802.11n tra..." refers methods in this paper

  • ...It works on up-to-date Linux operating systems: in our testbed we use Ubuntu 10.04 LTS with the 2.6.36 kernel....

    [...]

Journal ArticleDOI
01 Oct 2001
TL;DR: The Internet is going mobile and wireless, perhaps quite soon, with a number of diverse technologies leading the charge, including, 3G cellular networks based on CDMA technology, a wide variety of what is deemed 2.5G cellular technologies (e.g., EDGE, GPRS and HDR), and IEEE 802.11 wireless local area networks (WLANs).
Abstract: At some point in the future, how far out we do not exactly know, wireless access to the Internet will outstrip all other forms of access bringing the freedom of mobility to the way we access the we...

615 citations

Journal ArticleDOI
07 Jan 2010
TL;DR: This tutorial provides a brief introduction to multiple antenna techniques, and describes the two main classes of those techniques, spatial diversity and spatial multiplexing.
Abstract: The use of multiple antennas and MIMO techniques based on them is the key feature of 802.11n equipment that sets it apart from earlier 802.11a/g equipment. It is responsible for superior performance, reliability and range. In this tutorial, we provide a brief introduction to multiple antenna techniques. We describe the two main classes of those techniques, spatial diversity and spatial multiplexing. To ground our discussion, we explain how they work in 802.11n NICs in practice.

89 citations