The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including pattern-based, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions.

/pdf/a-survey-on-human-behavior-recognition-using-channel-state-17xs5detky.pdf

A Survey on Human Behavior Recognition Using Channel State Information

In the Internet-of-Things (IoT) era, user authentication is essential to ensure the security of connected devices and the customization of passive services However, conventional knowledge-based and physiological biometric-based authentication systems (eg, password, face recognition, and fingerprints) are susceptible to shoulder surfing attacks, smudge attacks, and heat attacks The powerful sensing capabilities of IoT devices, including smartphones, wearables, robots, and autonomous vehicles enable continuous authentication (CA) based on behavioral biometrics The artificial intelligence (AI) approaches hold significant promise in sifting through large volumes of heterogeneous biometrics data to offer unprecedented user authentication and user identification capabilities In this survey article, we outline the nature of CA in IoT applications, highlight the key behavioral signals, and summarize the extant solutions from an AI perspective Based on our systematic and comprehensive analysis, we discuss the challenges and promising future directions to guide the next generation of AI-based CA research

Behavioral Biometrics for Continuous Authentication in the Internet-of-Things Era: An Artificial Intelligence Perspective

Quarantine and isolation.

The COVID-19 outbreak spread like a forest fire in the first quarter of 2020. Until September, more than 26 million people were affected by this epidemic. It has been 9 months since the first case was seen. However, a curative treatment method or vaccine has not been developed yet. Today, the only approach that can prevent the outbreak is classical epidemic control approaches such as hygiene, case isolation, contact tracing and quarantine. Contact tracing aims to reduce the spread of the epidemic by trying to analyze the potential transmission routes of the infection at the individual level. In addition, it will be possible to prevent official measures such as the curfew taken to prevent the epidemic from spreading. However, when considering ways of communication between people, the epidemic knows no boundaries. Mobile applications and artificial intelligence can be successful in analyzing this contact chain. Even if protecting human life is the highest degree moral duty, these methods contain many ethical problems. The most violated ethical values because of these practices are privacy, confidentiality of information, civil freedom and autonomy. In this study, we will try to analyze the ethical problems that arise with contact tracing mobile applications in the context of COVID-19. © 2021, DOC Design and Informatics Co. Ltd.. All rights reserved.

Contact tracing in the context of covid-19: Ethical issues and assessment

As a result of the worldwide transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has evolved into an unprecedented pandemic. Currently, with unavailable pharmaceutical treatments and low vaccination rates, this novel coronavirus results in a great impact on public health, human society, and global economy, which is likely to last for many years. One of the lessons learned from the COVID-19 pandemic is that a long-term system with non-pharmaceutical interventions for preventing and controlling new infectious diseases is desirable to be implemented. Internet of things (IoT) platform is preferred to be utilized to achieve this goal, due to its ubiquitous sensing ability and seamless connectivity. IoT technology is changing our lives through smart healthcare, smart home, and smart city, which aims to build a more convenient and intelligent community. This paper presents how the IoT could be incorporated into the epidemic prevention and control system. Specifically, we demonstrate a potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art literatures of these five interventions to present the capabilities of IoT in countering against the current COVID-19 pandemic or future infectious disease epidemics.

IoT Platform for COVID-19 Prevention and Control: A Survey

Voice assistant devices function as interaction gateways in the Internet-of-Things (IoT) smart home. By using voice assistants, users are able to control smart homes via speech commands. However, voice assistants introduce potential security risks and privacy disclosures. For example, malicious actors could impersonate genuine users to send smart home speech commands. Speaker/user verification thus becomes a critical issue for smart home security. This article proposes a secure method for speaker verification in IoT smart homes using millimeter-wave (mmWave) radar. Specifically, we utilize the radar to capture both vocal cord vibration (VCV) and lip motion (LM) as multimodal biometrics for identifying speakers. Traditional voice-based speaker verification methods are vulnerable to impostor attacks, such as replay attacks and voice synthesis attacks, that use recorded or imitated voice audio to spoof the system. Our approach is able to protect IoT smart homes from these attacks by continuously detecting the liveness of the user using mmWave sensing and deep learning techniques. Extensive experiments show that the proposed approach can achieve high verification accuracy and be more robust against imposter attacks.

Secure mmWave-Radar-Based Speaker Verification for IoT Smart Home

Channel estimation is a challenging task, especially in a massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADC). Traditional deep learning (DL) methods, that learn the mapping from inputs to real channels, have significant difficulties in estimating accurate channels because their loss functions are not well designed and investigated. In this letter, a conditional generative adversarial networks (cGAN) is developed to predict more realistic channels by adversarially training two DL networks. cGANs not only learn the mapping from quantized observations to real channels but also learn an adaptive loss function to correctly train the networks. Numerical results show that the proposed cGAN based approach outperforms existing DL methods and achieves high robustness in massive MIMO systems.

Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN

The ever-growing security issues in various mobile applications and smart devices create an urgent demand for a reliable and convenient user verification method. Traditional verification methods request users to provide their secrets (e.g., entering passwords and collecting fingerprints). We envision that the essential trend of user verification is to free users from active participation in the verification process. Toward this end, we propose a continuous user verification system, which re-uses the widely deployed WiFi infrastructure to capture the unique physiological characteristics rooted in user’s respiratory motions. Different from the existing continuous verification approaches, posing dependency on restricted scenarios/user behaviors (e.g., keystrokes and gaits), our system can be easily integrated into any WiFi infrastructure to provide non-intrusive continuous verification. Specifically, we extract the respiration-related signals from the channel state information (CSI) of WiFi. We then derive the user-specific respiratory features based on the waveform morphology analysis and fuzzy wavelet transformation of the respiration signals. Additionally, a deep learning based user verification scheme is developed to identify legitimate users accurately and detect the existence of spoofing attacks. Extensive experiments involving 20 participants demonstrate that the proposed system can robustly verify/identify users and detect spoofers under various types of attacks.

Continuous User Verification via Respiratory Biometrics

This work proposes a continuous user verification system based on unique human respiratory-biometric characteristics extracted from the off-the-shelf WiFi signals. Our system innovatively re-uses widely available WiFi signals to capture the unique physiological characteristics rooted in respiratory motions for continuous authentication. Different from existing continuous authentication approaches having limited applicable scenarios due to their dependence on restricted user behaviors (e.g., keystrokes and gaits) or dedicated sensing infrastructures, our approach can be easily integrated into any existing WiFi infrastructure to provide non-invasive continuous authentication independent of user behaviors. Specifically, we extract representative features leveraging waveform morphology analysis and fuzzy wavelet transformation of respiration signals derived from the readily available channel state information (CSI) of WiFi. A respiration-based user authentication scheme is developed to accurately identify users and reject spoofers. Extensive experiments involving 20 subjects demonstrate that the proposed system can achieve a high authentication success rate of over 93% and robustly defend against various types of attacks.

https://dl.acm.org/doi/pdf/10.1145/3241539.3267743

Yudi Dong

Papers

IoT Platform for COVID-19 Prevention and Control: A Survey

Secure mmWave-Radar-Based Speaker Verification for IoT Smart Home

Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN

Continuous User Verification via Respiratory Biometrics

Leveraging Breathing for Continuous User Authentication