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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TL;DR: This paper proposes a hybrid electronic geofence approach that combines self-updating RSS fingerprints based localization and Channel State Information (CSI) motion detection and Multidimensional matching and filtering principle achieves fingerprints self- updating and improves the localization accuracy.
Abstract: Indoor location is definitively a key feature with immense value especial for geofencing. The received signal strength (RSS) fingerprinting based methodology is widely adopted to determine his/her proximity to that particular region. Its dynamic nature and maintain overhead remain a primary challenge. In this paper, we propose a hybrid electronic geofence approach that combines self-updating RSS fingerprints based localization and Channel State Information (CSI) motion detection. Multidimensional matching and filtering principle achieves fingerprints self-updating and improves the localization accuracy. CSI-based speed estimation reduces localization frequency and overhead. Our extensive real-world experiment results show that the proposed indoor geofencing method works well for more than 30 days without manual Wi-Fi fingerprints updating.
3 citations
TL;DR: Wi-Dog is presented, a noninvasive physical assault monitoring scheme that enables privacy-preserving monitoring in ubiquitous circumstances and consistently outperforms the advanced abnormal detection methods with a higher true detection rate and a lower false alarm rate.
Abstract: Monitoring physical assault is critical for the prevention of juvenile delinquency and promotion of school harmony. A large portion of assault events, particularly school violence among teenagers, usually happen at indoor secluded places. Pioneering approaches employ always-on-body sensors or cameras in the limited surveillance area, which are privacy-invasive and cannot provide ubiquitous assault monitoring. In this paper, we present Wi-Dog, a noninvasive physical assault monitoring scheme that enables privacy-preserving monitoring in ubiquitous circumstances. Wi-Dog is based on widely deployed commodity Wi-Fi infrastructures. The key intuition is that Wi-Fi signals are easily distorted by human motions, and motion-induced signals could convey informative characteristics, such as intensity, regularity, and continuity. Specifically, to explicitly reveal the substantive properties of physical assault, we innovatively propose a set of signal processing methods for informative components extraction by selecting sensitive antenna pairs and subcarriers. Then a novel signal-complexity-based segmentation method is developed as a location-independent indicator to monitor targeted movement transitions. Finally, holistic analysis is employed based on domain knowledge, and we distinguish the violence process from both local and global perspective using time-frequency features. We implement Wi-Dog on commercial Wi-Fi devices and evaluate it in real indoor environments. Experimental results demonstrate the effectiveness of Wi-Dog which consistently outperforms the advanced abnormal detection methods with a higher true detection rate of 94% and a lower false alarm rate of 8%.
3 citations
TL;DR: The authors propose an efficient construction scheme leveraging the matrix completion theory to improve the calibration efficiency, and employ a Bayes rule-based fingerprint matching method to implement location estimation.
Abstract: The popularisation of fingerprinting localisation technology has been hindered because of two major hurdles: (i) the accuracy bottleneck caused by unreliable location fingerprints and (ii) the huge effort required to construct a fingerprints database (or radio map) for the targeted area. To tackle the two problems, the authors propose an effective solution in this work. First, they exploit channel state information, which is a parameter depicting the frequency response of each subchannel, to design the location fingerprint, striving to eliminate the interferences of the complex indoor environment. Second, they propose an efficient construction scheme leveraging the matrix completion theory to improve the calibration efficiency, and employ a Bayes rule-based fingerprint matching method to implement location estimation. Finally, they evaluate the authors’ localisation system in two typical scenarios, and the numerical results show that the proposal ensures superior performance while reducing the workload significantly.
3 citations
TL;DR: This tutorial takes Wi-Fi sensing as an example and introduces both the theoretical principles and the code implementation 1 of data collection, signal processing, features extraction, and model design, and highlights state-of-the-art deep learning models and their applications in wireless sensing systems.
Abstract: With the rapid development of wireless communication technology, wireless access points (AP) and internet of things (IoT) devices have been widely deployed in our surroundings. Various types of wireless signals (e.g., Wi-Fi, LoRa, LTE) are filling out our living and working spaces. Previous researches reveal the fact that radio waves are modulated by the spatial structure during the propagation process (e.g., reflection, diffraction, and scattering) and superimposed on the receiver. This observation allows us to reconstruct the surrounding environment based on received wireless signals, called “wireless sensing”. Wireless sensing is an emerging technology that enables a wide range of applications, such as gesture recognition for human-computer interaction, vital signs monitoring for health care, and intrusion detection for security management. Compared with other sensing paradigms, such as vision-based and IMU-based sensing, wireless sensing solutions have unique advantages such as high coverage, pervasiveness, low cost, and robustness under adverse light and texture scenarios. Besides, wireless sensing solutions are generally lightweight in terms of both computation overhead and device size. This tutorial takes Wi-Fi sensing as an example. It introduces both the theoretical principles and the code implementation 1 of data collection, signal processing, features extraction, and model design. In addition, this tutorial highlights state-of-the-art deep learning models (e.g., CNN, RNN, and adversarial learning models) and their applications in wireless sensing systems. We hope this tutorial will help people in other research fields to break into wireless sensing research and learn more about its theories, designs, and implementation skills, promoting prosperity in the wireless sensing research field.
3 citations
TL;DR: This paper presents a new Wi-Fi protocol called QoS-Fi, that provides QoS for the mobile users in the frequency awareWi-Fi network, and is the first work that employs QoS at the frequency domain for Wi-fi networks.
Abstract: Modern Wi-Fi networks are trending towards using a wider channel bandwidth to achieve high physical layer data rate. The wide channel band experiences fluctuations across the different frequencies, causing diversity in the frequency domain. Frequency-aware Wi-Fi protocols exploit this frequency diversity and consequently achieve high wireless capacity. However, most of the existing works have not considered quality-of-service (QoS) issues. In this paper, we present a new Wi-Fi protocol called QoS-Fi, that provides QoS for the mobile users in the frequency aware Wi-Fi network. QoS-Fi dynamically assigns orthogonal frequency division multiplexing (OFDM) subchannels for heterogeneous mobile users to meet the QoS demands. To achieve this goal, we apply an OFDM-based variable-length Bloom filter (VBF) that synergistically integrates the channel quality estimation and QoS channel coordination. To the best of our knowledge, this is the first work that employs QoS at the frequency domain for Wi-Fi networks. We study the impact of variable-length signatures in the aspect of throughput maximization and meeting the QoS requirements and further develop a decentralized QoS-aware channel-allocation algorithm that achieves sub-optimal performance. Our USRP/GNURadio-based experiments and trace-driven simulations show that QoS-Fi provides up to 1.39 × and 1.29 × throughput improvements compared to the legacy EDCA and well-known Knopp and Humblet’s and round robin (K&H/RR) scheduling, respectively in the QoS-regimes.
3 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...The traces are obtained from commodity Intel Wi-Fi Link 5300 NIC and its modified driver [35]....
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References
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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.
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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....
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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.
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89 citations