π-Splicer: Perceiving Accurate CSI Phases with Commodity WiFi Devices

Zhu, Hongzi; Zhuo, Yiwei; Liu, Qinghao; Chang, Shan

doi:10.1109/tmc.2018.2793222

Cited by 72 publications

(32 citation statements)

References 25 publications

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“…As we know from (2), the CSI data consist of amplitude and phase information. The amplitude of the CSI data is affected by the burst noise and high amplitude impulse that are caused by the adaptive change in the transmission power and transmission rate [23], [44], [45]. On the other hand, the phase of the CSI data is distorted by errors such as carrier frequency offset (CFO) and sampling frequency offset (SFO) [44], [46].…”

Section: Experimental Setup and Data Collectionmentioning

confidence: 99%

WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities

et al. 2020

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Robust and accurate human activity recognition (HAR) systems are essential to many humancentric services within active assisted living and healthcare facilities. Traditional HAR systems mostly leverage a single sensing modality (e.g., either wearable, vision, or radio frequency sensing) combined with machine learning techniques to recognize human activities. Such unimodal HAR systems do not cope well with real-time changes in the environment. To overcome this limitation, new HAR systems that incorporate multiple sensing modalities are needed. Multiple diverse sensors can provide more accurate and complete information resulting in better recognition of the performed activities. This article presents WiWeHAR-a multimodal HAR system that uses combined Wi-Fi and wearable sensing modalities to simultaneously sense the performed activities. WiWeHAR makes use of standard Wi-Fi network interface cards to collect the channel state information (CSI) and a wearable inertial measurement unit (IMU) consisting of accelerometer, gyroscope, magnetometer sensors to collect the user's local body movements. We compute the time-variant mean Doppler shift (MDS) from the processed CSI data and magnitude from the inertial data for each sensor of the IMU. Thereafter, we separately extract various time-and frequencydomain features from the magnitude data and the MDS. We apply feature-level fusion to combine the extracted features, and finally supervised learning techniques are used to recognize the performed activities. We evaluate the performance of WiWeHAR by using a multimodal human activity data set, which was obtained from 9 participants. Each participant carried out four activities, such as walking, falling, sitting, and picking up an object from the floor. Our results indicate that the proposed multimodal WiWeHAR system outperforms the unimodal CSI, accelerometer, gyroscope, and magnetometer HAR systems and achieves an overall recognition accuracy of 99.6%-100%.

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Section: Experimental Setup and Data Collectionmentioning

confidence: 99%

WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities

et al. 2020

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“…Secondly, the phases of all the channel paths, namely φ uv l in (2), are distributed uniformly across [0, 2π], regardless of the power. Yet, the phase is vulnerable to noise, carrier frequency offset and asynchronous clocks/clock drift at the receiver, hence it is less suitable for practical applications [176]. The study in [133] is the only one on a practical phase-based key generation system, which is implemented on the USRP [177].…”

Section: ) Csimentioning

confidence: 99%

A New Frontier for IoT Security Emerging From Three Decades of Key Generation Relying on Wireless Channels

Zhang

Marshall

et al. 2020

IEEE Access

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The Internet of Things (IoT) is a transformative technology, which is revolutionizing our everyday life by connecting everyone and everything together. The massive number of devices are preferably connected wirelessly because of the easy installment and flexible deployment. However, the broadcast nature of the wireless medium makes the information accessible to everyone including malicious users, which should hence be protected by encryption. Unfortunately, the secure and efficient provision of cryptographic keys for low-cost IoT devices is challenging; weak keys have resulted in severe security breaches, as evidenced by numerous notorious cyberattacks. This paper provides a comprehensive survey of lightweight security solutions conceived for IoT, relying on key generation from wireless channels. We first introduce the key generation fundamentals and protocols. We then examine how to apply this emerging technique to secure IoT and demonstrate that key generation relying on the randomness of wireless channels is eminently suitable for IoT. This paper reviews the extensive research efforts in the areas of theoretical modelling, simulation based validation and experimental exploration. We finally discuss the hurdles and challenges that key generation is facing and suggest future work to make key generation a reliable and secure solution to safeguard the IoT.

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“…In actual measurement process, the lack of hardware accuracy makes the true phase difficult to obtain. Previous studies [36] explicitly point that phase measurement error comes from two major reasons-Carrier Frequency Offset (CFO) and Sampling Frequency Offset (SFO). The former is caused by the fact that the center frequency of the transmitting and receiving pairs cannot be completely synchronized, resulting in phase rotation error [37].…”

Section: Modified Phase Ls Calibrationmentioning

confidence: 99%

Indoor NLOS Positioning System Based on Enhanced CSI Feature with Intrusion Adaptability

Han

Shi

Deng

et al. 2020

Sensors

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With the wide deployment of commercial WiFi devices, the fine-grained channel state information (CSI) has received widespread attention with broad application domain including indoor localization and intrusion detection. From the perspective of practicality, dynamic intrusion may be confused under non-line-of-sight (NLOS) conditions and the continuous operation of passive positioning system will bring much unnecessary computation. In this paper, we propose an enhanced CSI-based indoor positioning system with pre-intrusion detection suitable for NLOS scenarios (C-InP). It mainly consists of two modules: intrusion detection and positioning estimation. The introduction of detection module is a prerequisite for positioning module. In order to improve the discrimination of features under NLOS conditions, we propose a modified calibration method for phase transformation while the amplitude outliers are filtered by the variance distribution with the median sequence. In addition, binary and improved multiple support vector classification (SVC) models are established to realize NLOS intrusion detection and high-discrimination fingerprint localization, respectively. Comprehensive experimental verification is carried out in typical indoor scenarios. Experimental results show that C-InP outperforms the existing system in NLOS environments, where the mean distance error (MDE) reached 0.49 m in the integrated room and 0.81 m in the complex garage, respectively.

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π-Splicer: Perceiving Accurate CSI Phases with Commodity WiFi Devices

Cited by 72 publications

References 25 publications

WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities

WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities

A New Frontier for IoT Security Emerging From Three Decades of Key Generation Relying on Wireless Channels

Indoor NLOS Positioning System Based on Enhanced CSI Feature with Intrusion Adaptability

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