WiFi Sensing at the Edge Towards Scalable On-Device Wireless Sensing Systems

WiFi sensing offers a powerful method for tracking physical activities using the radio-frequency signals already found throughout our homes and offices. This novel sensing modality offers continuous and non-intrusive activity tracking since sensing can be performed (i) without requiring wearable sensors, (ii) outside the line-of-sight, and even (iii) through the wall. Furthermore, WiFi has become a ubiquitous technology in our computers, our smartphones, and even in low-cost Internet of Things devices. In this work, we consider how the ubiquity of these low-cost WiFi devices offer an unparalleled opportunity for improving the scalability of wireless sensing systems. Thus far, WiFi sensing research assumes costly offline computing resources and hardware for training machine learning models and for performing model inference. To improve the scalability of WiFi sensing systems, this dissertation introduces techniques for improving machine learning at the edge by thoroughly surveying and evaluating signal preprocessing and edge machine learning techniques. Additionally, we introduce the use of federated learning for collaboratively training machine learning models with WiFi data only available on edge devices. We then consider privacy and security concerns of WiFi sensing by demonstrating possible adversarial surveillance attacks. To combat these attacks, we propose a method for leveraging spatially distributed antennas to prevent eavesdroppers from performing adversarial surveillance while still enabling and even improving the sensing capabilities of allowed WiFi sensing devices within our environments. The overall goal throughout this work is to demonstrate that WiFi sensing can become a ubiquitous and secure sensing option through the use of on-device computation on low-cost edge devices

Towards a Robust WiFi-based Fall Detection with Adversarial Data Augmentation

Recent WiFi-based fall detection systems have drawn much attention due to their advantages over other sensory systems. Various implementations have achieved impressive progress in performance, thanks to machine learning and deep learning techniques. However, many of such high accuracy systems have low reliability as they fail to achieve robustness in unseen environments. To address that, this paper investigates a method of generalization through adversarial data augmentation. Our results show a slight improvement in deep learning-systems in unseen domains, though the performance is not significant.Comment: Will appear in Proceedings of the 54th Annual Conference on Information Sciences and Systems (CISS2020

Channel State Information from pure communication to sense and track human motion: A survey

Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology

An Approach to Finding Parking Space Using the CSI-based WiFi Technology

With ever-increasing number of vehicles and shortages of parking spaces, parking has always been a very important issue in transportation. It is necessary to use advanced intelligent technologies to help drivers find parking spaces, quickly. In this thesis, an approach to finding empty spaces in parking lots using the CSI-based WiFi technology is presented. First, the channel state information (CSI) of received WiFi signals is analyzed. The features of CSI data that are strongly correlated with the number of empty slots in parking lots are identified and extracted. A machine learning technique to perform multi-class classification that categorizes the input data into classes representing the number of empty slots is employed. A prototype system of the proposed approach is developed. Experiments are performed and it is shown that the system is feasible. Compared with traditional approaches based on magnetic sensors deployed on individual parking slots, the proposed approach is non-intrusive as it does not require to install specialized devices in a parking lot, and is cost-effective since it utilizes either existing WiFi infrastructure or only a pair of WiFi devices. As a result, the average classification accuracy of system is 80.8%, and the accuracy is improved to 93.8% with a tolerance of one empty slot

Secure Data Collection and Analysis in Smart Health Monitoring

Smart health monitoring uses real-time monitored data to support diagnosis, treatment, and health decision-making in modern smart healthcare systems and benefit our daily life. The accurate health monitoring and prompt transmission of health data are facilitated by the ever-evolving on-body sensors, wireless communication technologies, and wireless sensing techniques. Although the users have witnessed the convenience of smart health monitoring, severe privacy and security concerns on the valuable and sensitive collected data come along with the merit. The data collection, transmission, and analysis are vulnerable to various attacks, e.g., eavesdropping, due to the open nature of wireless media, the resource constraints of sensing devices, and the lack of security protocols. These deficiencies not only make conventional cryptographic methods not applicable in smart health monitoring but also put many obstacles in the path of designing privacy protection mechanisms. In this dissertation, we design dedicated schemes to achieve secure data collection and analysis in smart health monitoring. The first two works propose two robust and secure authentication schemes based on Electrocardiogram (ECG), which outperform traditional user identity authentication schemes in health monitoring, to restrict the access to collected data to legitimate users. To improve the practicality of ECG-based authentication, we address the nonuniformity and sensitivity of ECG signals, as well as the noise contamination issue. The next work investigates an extended authentication goal, denoted as wearable-user pair authentication. It simultaneously authenticates the user identity and device identity to provide further protection. We exploit the uniqueness of the interference between different wireless protocols, which is common in health monitoring due to devices\u27 varying sensing and transmission demands, and design a wearable-user pair authentication scheme based on the interference. However, the harm of this interference is also outstanding. Thus, in the fourth work, we use wireless human activity recognition in health monitoring as an example and analyze how this interference may jeopardize it. We identify a new attack that can produce false recognition result and discuss potential countermeasures against this attack. In the end, we move to a broader scenario and protect the statistics of distributed data reported in mobile crowd sensing, a common practice used in public health monitoring for data collection. We deploy differential privacy to enable the indistinguishability of workers\u27 locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks

AI enabled RF sensing of Diversified Human-Centric Monitoring

This thesis delves into the application of various RF signaling techniques in HumanCentric Monitoring (HCM), specifically focusing on WiFi, LoRa, Ultra-wideband (UWB) radars, and Frequency Modulated Continuous Wave (FMCW) radars. Each of these technologies has unique properties suitable for different aspects of HCM. For instance, 77GHz FMCW radar signals demonstrate high sensitivity in detecting subtle human movements, such as heartbeat, contrasting with the capabilities of 2.4GHz/5GHz WiFi signals. The research extends to both large-scale and small-scale Human Activity Recognition (HAR), examining how ubiquitous communication signals like WiFi and LoRa can be utilized for large-scale HAR, while radar signals with higher central frequencies are more effective for small-scale motions, including heartbeat and mouth movements. The thesis also identifies several unresolved challenges in the field. These include the underutilization of spatial spectral information in existing WiFi sensing technologies, the untapped potential of LoRa technology in identity recognition, the sensitivity of millimeterwave radar in detecting breathing and heartbeat against minor movements, and the lack of comprehensive datasets for mouth motion detection in silent speech recognition. Addressing these challenges, the paper proposes several innovative solutions: • A comprehensive analysis of methodologies for RF-based HCM applications, discussing challenges and proposing potential solutions for broader healthcare applications using wireless sensing. • Exploration of communication signals in HCM systems, especially focusing on WiFi and LoRa sensing. It introduces the continuous AoA-ToF maps method to enhance HCM system performance and the LoGait system, which uses LoRa signals for human gait identification, extending the sensing range to 20 meters. • Development of a FMCW radar-based structure for respiration detection, incorporating an ellipse normalization method to adjust distorted IQ signals, reducing the root mean square error by 30% compared to baseline methods. • Collection and analysis of a large-scale multimodal dataset for silent speech recognition and speech enhancement, including designing experiments to validate the dataset’s utility in a multimodal-based speech recognition system

Integrated Sensing and Communications: Recent Advances and Ten Open Challenges

It is anticipated that integrated sensing and communications (ISAC) would be one of the key enablers of next-generation wireless networks (such as beyond 5G (B5G) and 6G) for supporting a variety of emerging applications. In this paper, we provide a comprehensive review of the recent advances in ISAC systems, with a particular focus on their foundations, system design, networking aspects and ISAC applications. Furthermore, we discuss the corresponding open questions of the above that emerged in each issue. Hence, we commence with the information theory of sensing and communications (S$\&$C), followed by the information-theoretic limits of ISAC systems by shedding light on the fundamental performance metrics. Next, we discuss their clock synchronization and phase offset problems, the associated Pareto-optimal signaling strategies, as well as the associated super-resolution ISAC system design. Moreover, we envision that ISAC ushers in a paradigm shift for the future cellular networks relying on network sensing, transforming the classic cellular architecture, cross-layer resource management methods, and transmission protocols. In ISAC applications, we further highlight the security and privacy issues of wireless sensing. Finally, we close by studying the recent advances in a representative ISAC use case, namely the multi-object multi-task (MOMT) recognition problem using wireless signals.Comment: 26 pages, 22 figures, resubmitted to IEEE Journal. Appreciation for the outstanding contributions of coauthors in the paper

Improving Wifi Sensing And Networking With Channel State Information

In recent years, WiFi has a very rapid growth due to its high throughput, high efficiency, and low costs. Multiple-Input Multiple-Output (MIMO) and Orthogonal Frequency-Division Multiplexing (OFDM) are two key technologies for providing high throughput and efficiency for WiFi systems. MIMO-OFDM provides Channel State Information (CSI) which represents the amplitude attenuation and phase shift of each transmit-receiver antenna pair of each carrier frequency. CSI helps WiFi achieve high throughput to meet the growing demands of wireless data traffic. CSI captures how wireless signals travel through the surrounding environment, so it can also be used for wireless sensing purposes. This dissertation presents how to improve WiFi sensing and networking with CSI. More specifically, this dissertation proposes deep learning models to improve the performance and capability of WiFi sensing and presents network protocols to reduce CSI feedback overhead for high efficiency WiFi networking. For WiFi sensing, there are many wireless sensing applications using CSI as the input in recent years. To get a better understanding of existing WiFi sensing technologies and future WiFi sensing trends, this dissertation presents a survey of signal processing techniques, algorithms, applications, performance results, challenges, and future trends of CSI-based WiFi sensing. CSI is widely used for gesture recognition and sign language recognition. Existing methods for WiFi-based sign language recognition have low accuracy and high costs when there are more than 200 sign gestures. The dissertation presents SignFi for sign language recognition using CSI and Convolutional Neural Networks (CNNs). SignFi provides high accuracy and low costs for run-time testing for 276 sign gestures in the lab and home environments. For WiFi networking, although CSI provides high throughput for WiFi networks, it also introduces high overhead. WiFi transmitters need CSI feedback for transmit beamforming and rate adaptation. The size of CSI packets is very large and it grows very fast with respect to the number of antennas and channel width. CSI feedback introduces high overhead which reduces the performance and efficiency of WiFi systems, especially mobile and hand-held WiFi devices. This dissertation presents RoFi to reduce CSI feedback overhead based on the mobility status of WiFi receivers. CSI feedback compression reduces overhead, but WiFi receivers still need to send CSI feedback to the WiFi transmitter. The dissertation presents EliMO for eliminating CSI feedback without sacrificing beamforming gains

D21.3 Analysis of initial results at EuWIN@CTTC

Deliverable D21.3 del projecte europeu NEWCOM#The nature of this Deliverable of WP2.1 (“Radio interfaces for next-generation wireless systems”) is mainly descriptive and its purpose is to provide a report on the status of the different Joint Research Activities (JRAs) currently ongoing, some of them being performed on the facilities that are available at EuWInPeer ReviewedPreprin

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things