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.
Citations
More filters
01 Dec 2019
TL;DR: This paper categorizes the WiFi-based IPSs into two types, namely Received signal strength (RSS)-based and Channel state information (CSI)-based, and compares the practical performances of several WiFi- based IPSs, and chooses the RSS-based and CSI-based methods.
Abstract: With the popularity of smart phones and wireless network, location-based services (LBSs) attract extensive attention. The demands on indoor positioning systems (IPS) for the WiFi deployed environments are especially high. In this paper, we categorize the WiFi-based IPSs into two types, namely Received signal strength (RSS)-based and Channel state information (CSI)-based, and compare the practical performances of several WiFi-based IPSs. For the RSS-based methods, we select fingerprinting, trilateration method, sequence-based localization (SBL), and multidimensional scaling (MDS)-based methods for comparison. For the CSI-based method, we choose the time-reversal (TR) algorithm and propose a new experimental scheme to measure its performance. The pros and cons of each algorithm are also discussed.
5 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...In our experiment, we adopt an open source CSI tool [36] to sample CSI from CFR....
[...]
09 Aug 2019
TL;DR: Rover is presented, an indoor localization system that simultaneously localizes multiple backscatter tags with zero start-up cost using a robot equipped with inertial sensors, to empower universal localization service.
Abstract: Recent years have witnessed the rapid proliferation of low- power backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Localizing such low-power backscatter tags is crucial for IoT-based smart services. However, current backscatter localization systems require prior knowledge of the site, either a map or landmarks with known positions, increasing the deployment cost. To empower universal localization service, this paper presents Rover, an indoor localization system that simultaneously localizes multiple backscatter tags with zero start-up cost using a robot equipped with inertial sensors. Rover runs in a joint optimization framework, fusing WiFi-based positioning measurements with inertial measurements to simultaneously estimate the locations of both the robot and the connected tags. Our design addresses practical issues such as the interference among multiple tags and the real- time processing for solving the SLAM problem. We prototype Rover using off-the-shelf WiFi chips and customized backscatter tags. Our experiments show that Rover achieves localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.
5 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...11n Channel State Information (CSI) tool [10] to obtain wireless channel information for AoA estimation....
[...]
...11 CSI tool [10] to obtain the wireless channel information for each packet....
[...]
09 Apr 2021
TL;DR: This work implements their sleep monitoring system based on COTS WiFi devices with a motion detection capability enhancement method based on Rice-K theory and Fresnel theory and a sleep motion positioning algorithm based on regularity detection.
Abstract: High-quality sleep is essential to our daily lives, and real-time monitoring of vital signs during sleep is beneficial. Current sleep monitoring solutions are mostly based on wearable sensors or cameras, the former is worse for sleep quality, the latter is worse for privacy, dissimilar to such methods, we implement our sleep monitoring system based on COTS WiFi devices. There are two challenges need to be overcome in the system implementation process: First, the torso deformation caused by breathing/heartbeat is weak, how to effectively capture this deformation? Second, movements such as turning over will affect the accuracy of vital signs monitoring, how to quickly distinguish such movements? For the former, we propose a motion detection capability enhancement method based on Rice-K theory and Fresnel theory. For the latter, we propose a sleep motion positioning algorithm based on regularity detection. The experimental results indicated the performance of our method.
5 citations
TL;DR: In this paper , a stacking ensemble broad learning localization system using channel state information as a fingerprint is proposed, which enables the EnsemLoca system to build the base learner in parallel by using bagging.
Abstract: Indoor positioning technology based on Wi-Fi fingerprint recognition has been widely studied owing to the pervasiveness of hardware facilities and the ease of implementation of software technology. However, the similarity-based method is not sufficiently accurate, whereas the offline training of the neural network-based method is overly time consuming. An efficient model with high positioning accuracy is therefore not yet available. We propose a stacking ensemble broad learning localization system using channel state information as a fingerprint, which is termed EnsemLoca. A bootstrapping method is used to build the training set, which enables the EnsemLoca system to build the base learner in parallel by using bagging. The broad learning system (BLS), which is a novel neural network model, as a base learner, not only has the advantage of time complexity but also offers a sparse representation in which the features are filtered. A unique base learner is constructed by randomly selecting the samples and features, and they are combined by stack generalization. The experimental results show that the EnsemLoca system achieves higher accuracy than several machine-learning algorithms in both line-of-sight (LOS) and non-LOS environments, and is even stronger than deep neural networks characterized by accuracy. At the same time, it has the same theoretical complexity as BLS, which greatly reduces the offline training time.
5 citations
TL;DR: RFree-ID is the first unobtrusive RFID-based human identification system irrespective of walking cofactors, and is implemented on COTS RFID devices, and extensive experimental evaluation under various conditions validates the high reliability and robustness of the system.
5 citations
References
More filters
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....
[...]
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
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