RF Cloud for Cyberspace Intelligence

Pahlavan, Kaveh; Ying, Julang; Li, Ziheng; Solovey, Erin Treacy; Loftus, John; Dong, Zaili

doi:10.1109/access.2020.2993548

Cited by 10 publications

(17 citation statements)

References 54 publications

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“…Motion in the environment affects RF propagation in multipath indoor and urban areas and causes fading in the measured RSSI, which seriously challenges the precision of RSSI-based ranging [21]. The channel impulse response for two wireless devices communicating with a range r in a multipath, indoor, or urban area with N -paths, is represented by [23]:…”

Section: Features Of Rssi Short Range Fadingmentioning

confidence: 99%

“…4.a shows the definition of the fade rate and the duration of the fade as well as equations relating them together on a sample of MRAS measured data. Defining the normalized crossing threshold as, ρ = A/A rms , in which A and A rms are the threshold level and rms amplitude of the RSSI, respectively, these relations are given by (6a) and (6b) [23] in the top two rows of the time-domain features in Table This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

Section: B Rssi Features In Time Domainmentioning

confidence: 99%

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Performance Evaluation of COVID-19 Proximity Detection Using Bluetooth LE Signal

Pahlavan

Agu

2021

IEEE Access

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The risk of COVID-19 transmission increases when an uninfected person is less than 6 ft from an infected person for longer than 15 minutes. Infectious disease experts working on the COVID-19 pandemic call this high-risk situation being Too Close for Too Long (TCTL). Consequently, the problem of detecting the TCTL situation in order to maintain appropriate social distance has attracted considerable attention recently. One of the most prominent TCTL detection ideas being explored involves utilizing the Bluetooth Low-Energy (BLE) Received Signal Strength Indicator (RSSI) to determine whether the owners of two smartphones are observing the acceptable social distance of 6 ft. However, using RSSI measurements to detect the TCTL situation is extremely challenging due to the significant signal variance caused by multipath fading in indoor radio channel, carrying the smartphone in different pockets or positions, and differences in smartphone manufacturer and type of the device. In this study we utilize the Mitre Range Angle Structured (MRAS) Private Automated Contact Tracing (PACT) dataset to extensively evaluate the effectiveness of Machine Learning (ML) algorithms in comparison to classical estimation theory techniques to solve the TCTL problem. We provide a comparative performance evaluation of proximity classification accuracy and the corresponding confidence levels using classical estimation theory and a variety of ML algorithms. As the classical estimation method utilizes RSSI characteristics models, it is faster to compute, is more explainable, and drives an analytical solution for the precision bounds proximity estimation. The ML algorithms, Support Vector Machines (SVM), Random Forest, and Gradient Boosted Machines (GBM) utilized thirteen spatial, time-domain, frequency-domain, and statistical features extracted from the BLE RSSI data to generate the same results as classical estimation algorithms. We show that ML algorithms can achieve 3.60%∼19.98% better precision, getting closer to achievable bounds for estimation.

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Section: Features Of Rssi Short Range Fadingmentioning

confidence: 99%

Section: B Rssi Features In Time Domainmentioning

confidence: 99%

Performance Evaluation of COVID-19 Proximity Detection Using Bluetooth LE Signal

Pahlavan

Agu

2021

IEEE Access

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“…Today, the dominant transmission technique in Wi-Fi is MIMO-OFDM. Devices which can use MIMO_OFDM can also measure Direction of Arrival (DOA), channel impulse response (CIR), and the Channel State Information (CSI) of the multipath medium between the transmitter and the received [69]. These features (as well as the RSS feature) vary statistically depending on the multipath characteristics arising from the motion in the environment.…”

Section: Wi-fi and Emerging Cyberspace Applicationsmentioning

confidence: 99%

“…In the past decade, the design of novel "cyberspace intelligence" applications that opportunistically benefit from the "RF cloud" radiated from signals used for wireless communications and short range radars, has been a fertile area of research [69]. These applications take advantage of statistical variations of RF signals propagated from the wireless devices, caused by motion in the environment to design applications that can detect gestures and motion or those used for authentication and security.…”

Section: Emerging Cyberspace Applications Of Wi-fimentioning

confidence: 99%

Evolution and Impact of Wi-Fi Technology and Applications: A Historical Perspective

Pahlavan

Krishnamurthy

2020

Int J Wireless Inf Networks

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The IEEE 802.11 standard for wireless local area networking (WLAN), commercially known as Wi-Fi, has become a necessity in our day-today life. Over a billion Wi-Fi access points connect close to hundred billion of IoT devices, smart phones, tablets, laptops, desktops, smart TVs, video cameras, monitors, printers, and other consumer devices to the Internet to enable millions of applications to reach everyone, everywhere. The evolution of Wi-Fi technology also resulted in the first commercial piloting of spread spectrum, high speed optical communications, OFDM, MIMO and mmWave pulse transmission technologies, which then became more broadly adopted by cellular phone and wireless sensor networking industries. The popularity and widespread Wi-Fi deployment in indoor areas further motivated innovation in opportunistic cyberspace applications that exploit the ubiquitous Wi-Fi signals. The RF signal radiated from Wi-Fi access points creates an "RF cloud" accessible to any Wi-Fi equipped device hosting or supporting these opportunistic applications. Wi-Fi positioning and location intelligence were the first popular opportunistic applications of Wi-Fi's RF cloud. Today, researchers are investigating opportunistic applications of Wi-Fi signals for gesture and motion detection as well as authentication and security. This paper provides a holistic overview of the evolution of Wi-Fi technology and its applications as the authors experienced it in the last few decades.

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“…RF signals in indoor areas fluctuate significantly as a result of human motion in the close vicinity of RF transceivers, these RF propagation characteristics have been used for a variety of hand motion detection applications in recent years. As a result, RF hand motion detection is becoming increasingly popular as an effective HCI method [14].…”

Section: Introductionmentioning

confidence: 99%

A Model-Based RF Hand Motion Detection System for Shadowing Scenarios

Dong

Ying

et al. 2020

IEEE Access

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With the explosive growth of mobile computing, new modes of human-computer interaction (HCI) are emerging and becoming feasible. Compared to vision-based systems that require lighting, radio frequency (RF)-based hand motion detection systems are becoming more popular in HCI applications. In real RF hand motion detection scenarios, the line-of-sight between the transmitter (Tx) and receiver (Rx) is usually blocked. Hence, shadowing significantly affects the detection accuracy. To design better RF hand motion detection systems, we propose a simple diffraction and interference model (DIM) to interpret the received signal strength (RSS) variation caused by hand motions in the shadowing scenario. Based on theories of knife-edge diffraction and mutual radio interference, DIM provides a simple theoretical foundation for analyzing the RSS variation with hand size, signal frequency, and Tx-Rx distance. Furthermore, a modelbased RF hand motion detection system benefiting from DIM is presented. Unlike existing systems that require a large number of motion features to train a motion classifier, the model-based system achieves training-free motion classification, which has potential for hand motion detection on a real-time basis. Empirical data collected from a vector network analyzer validate our system as well as demonstrate a simple diffraction model can help hand motion detection processing for commonly growing HCI applications.

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RF Cloud for Cyberspace Intelligence

Cited by 10 publications

References 54 publications

Performance Evaluation of COVID-19 Proximity Detection Using Bluetooth LE Signal

Performance Evaluation of COVID-19 Proximity Detection Using Bluetooth LE Signal

Evolution and Impact of Wi-Fi Technology and Applications: A Historical Perspective

A Model-Based RF Hand Motion Detection System for Shadowing Scenarios

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