Fall Detection Using Channel State Information from WiFi Devices

Falls among the independently living elderly population are a major public health worry, leading to injuries, loss of confidence to live independently and even to death. Each year, one in three people aged 65 and older falls and one in five of them suffers fatal or non fatal injuries. Therefore, detecting a fall early and alerting caregivers can potentially save lives and increase the standard of living. Existing solutions, e.g. push-button, wearables, cameras, radar, pressure and vibration sensors, have limited public adoption either due to the requirement for wearing the device at all times or installing specialized and expensive infrastructure. In this thesis, a device-free, low cost indoor fall detection system using commodity WiFi devices is presented. The system uses physical layer Channel State Information (CSI) to detect falls. Commercial WiFi hardware is cheap and ubiquitous and CSI provides a wealth of information which helps in maintaining good fall detection accuracy even in challenging environments. The goals of the research in this thesis are the design, implementation and experimentation of a device-free fall detection system using CSI extracted from commercial WiFi devices. To achieve these objectives, the following contributions are made herein. A novel time domain human presence detection scheme is developed as a precursor to detecting falls. As the next contribution, a novel fall detection system is designed and developed. Finally, two main enhancements to the fall detection system are proposed to improve the resilience to changes in operating environment. Experiments were performed to validate system performance in diverse environments. It can be argued that through collection of real world CSI traces, understanding the behavior of CSI during human motion, the development of a signal processing tool-set to facilitate the recognition of falls and validation of the system using real world experiments significantly advances the state of the art by providing a more robust fall detection scheme

Using Commercial Ray Tracing Software to Drive an Attenuator-Based Mobile WIreless Testbed

We propose and build a prototype architecture for a laboratory-based mobile wireless testbed that uses highly detailed, site-specific channel models to dynamically configure a many-to-many analog channel emulator. Unlike similar systems that have used abstract channel models with few details from the physical environment, we take advantage of commercial ray tracing software and high-performance hardware to make realistic signal power and characteristics predictions in a highly detailed environment. The ray tracing results are used to program a many-to-many analog channel emulator. Using this system, we can conveniently, repeatedly, and realistically subject real wireless nodes to the effects of mobility. We use our prototype system and a detailed CAD model of the University of Maryland campus to compare field test measurements to measurements made from the same devices in the same physical scenario in the testbed. This thesis presents the design, implementation, and validation phases of the proposed mobile wireless testbed

Uncover the Power of Multipath : Detecting NLOS Drones Using Low-Cost WiFi Devices

In recent years, consumer UAV technology has seen considerable advances. Consumer UAVs have become an ideal vector for privacy invasions due to their affordability, size, maneuverability, and their ability to stream live high-quality video. There is considerable proliferation of drones in both civil and military domains. Hence it is critical to detect invading unmanned aerial vehicles (UAVs) or drones in a timely manner for both security and safeguarding privacy. Currently available solutions like active radar, video or acoustic sensors are very expensive (especially for individuals) and have considerable constraints (e.g., requiring visual line of sight). Recent research on drone detection with passive RF signals provides an opportunity for low-cost deployment of drone detectors on commodity wireless devices. The state of the arts in this direction mainly focus on detecting drones using line-of-sight (LOS) RF signals which are less noisy as compared to their non-LOS (NLOS) counterparts. To the best of our knowledge, there is no existing cost-effective solution for the general public to enable non-LOS(NLOS) detection for drone privacy invasion, which is the most common condition and it still remains an open challenge. This thesis research provides a low-cost UAV detection system for privacy invasion caused by customer drone. Our model supports NLOS detection with low-cost hardware under $50, and hence it is affordable for the general public to deploy in their house, apartments, and office. Our work utilizes inherent drone motions (i.e., body shifting and vibrations) as unique signatures for drone detection. Firstly, we validated the relationship between drone motions and RF signal under the NLOS condition using extensive experiments. This is motivated by the fact that under NLOS conditions slight changes to the position or motion of a drone could lead to dramatic change in multi-path components in received RF signals. The NLOS condition “amplifies the RF signatures introduced by drone motions. We designed a deep learning model to capture the complex features from NLOS RF signals. In particular, we designed and trained a long short-term memory (LSTM) neural network [15, 27], a generative model which can effectively extract features of inputs for NLOS drone detection. Moreover, without knowing the presence of drones, our system starts with classifying any detected RF signals into LOS signals and NLOS signals before the NLOS drone learner is used. Classification of LOS and NLOS signals is feasible because they exhibit different combined features such as strength, variance, and distribution due to their differences in multipath effects. We used the supervised support vector machine (S-SVM) [17] as the learning model, which is effective for binary classification. This design is validated via extensive experiments using commodity drones in resident areas with other Wi-Fi enabled mobile devices

Taming and Leveraging Directionality and Blockage in Millimeter Wave Communications

To cope with the challenge for high-rate data transmission, Millimeter Wave(mmWave) is one potential solution. The short wavelength unlatched the era of directional mobile communication. The semi-optical communication requires revolutionary thinking. To assist the research and evaluate various algorithms, we build a motion-sensitive mmWave testbed with two degrees of freedom for environmental sensing and general wireless communication.The first part of this thesis contains two approaches to maintain the connection in mmWave mobile communication. The first one seeks to solve the beam tracking problem using motion sensor within the mobile device. A tracking algorithm is given and integrated into the tracking protocol. Detailed experiments and numerical simulations compared several compensation schemes with optical benchmark and demonstrated the efficiency of overhead reduction. The second strategy attempts to mitigate intermittent connections during roaming is multi-connectivity. Taking advantage of properties of rateless erasure code, a fountain code type multi-connectivity mechanism is proposed to increase the link reliability with simplified backhaul mechanism. The simulation demonstrates the efficiency and robustness of our system design with a multi-link channel record.The second topic in this thesis explores various techniques in blockage mitigation. A fast hear-beat like channel with heavy blockage loss is identified in the mmWave Unmanned Aerial Vehicle (UAV) communication experiment due to the propeller blockage. These blockage patterns are detected through Holm\u27s procedure as a problem of multi-time series edge detection. To reduce the blockage effect, an adaptive modulation and coding scheme is designed. The simulation results show that it could greatly improve the throughput given appropriately predicted patterns. The last but not the least, the blockage of directional communication also appears as a blessing because the geometrical information and blockage event of ancillary signal paths can be utilized to predict the blockage timing for the current transmission path. A geometrical model and prediction algorithm are derived to resolve the blockage time and initiate active handovers. An experiment provides solid proof of multi-paths properties and the numeral simulation demonstrates the efficiency of the proposed algorithm

Integrated Sensing and Communication based Outdoor Multi-Target Detection, Tracking and Localization in Practical 5G Networks

The 6th generation (6G) wireless networks will likely to support a variety of capabilities beyond communication, such as sensing and localization, through the use of communication networks empowered by advanced technologies. Integrated sensing and communication (ISAC) has been recognized as a critical technology as well as an usage scenario for 6G, as widely agreed by leading global standardization bodies. ISAC utilizes communication infrastructure and devices to provide the capability of sensing the environment with high resolution, as well as tracking and localizing moving objects nearby. Meeting both the requirements for communication and sensing simultaneously, ISAC based approaches celebrate the advantages of higher spectral and energy efficiency compared to two separate systems to serve two purposes, and potentially lower costs and easy deployment. A key step towards the standardization and commercialization of ISAC is to carry out comprehensive field trials in practical networks, such as the 5th generation (5G) network, to demonstrate its true capacities in practical scenarios. In this paper, an ISAC based outdoor multi-target detection, tracking and localization approach is proposed and validated in 5G networks. The proposed system comprises of 5G base stations (BSs) which serve nearby mobile users normally, while accomplishing the task of detecting, tracking and localizing drones, vehicles and pedestrians simultaneously. Comprehensive trial results demonstrate the relatively high accuracy of the proposed method in practical outdoor environment when tracking and localizing single targets and multiple targets.Comment: Accepted by an open access journal (appearing on IEEEXplore soon

Doctor of Philosophy

dissertationThe wireless radio channel is typically thought of as a means to move information from transmitter to receiver, but the radio channel can also be used to detect changes in the environment of the radio link. This dissertation is focused on the measurements we can make at the physical layer of wireless networks, and how we can use those measurements to obtain information about the locations of transceivers and people. The first contribution of this work is the development and testing of an open source, 802.11b sounder and receiver, which is capable of decoding packets and using them to estimate the channel impulse response (CIR) of a radio link at a fraction of the cost of traditional channel sounders. This receiver improves on previous implementations by performing optimized matched filtering on the field-programmable gate array (FPGA) of the Universal Software Radio Peripheral (USRP), allowing it to operate at full bandwidth. The second contribution of this work is an extensive experimental evaluation of a technology called location distinction, i.e., the ability to identify changes in radio transceiver position, via CIR measurements. Previous location distinction work has focused on single-input single-output (SISO) radio links. We extend this work to the context of multiple-input multiple-output (MIMO) radio links, and study system design trade-offs which affect the performance of MIMO location distinction. The third contribution of this work introduces the "exploiting radio windows" (ERW) attack, in which an attacker outside of a building surreptitiously uses the transmissions of an otherwise secure wireless network inside of the building to infer location information about people inside the building. This is possible because of the relative transparency of external walls to radio transmissions. The final contribution of this dissertation is a feasibility study for building a rapidly deployable radio tomographic (RTI) imaging system for special operations forces (SOF). We show that it is possible to obtain valuable tracking information using as few as 10 radios over a single floor of a typical suburban home, even without precise radio location measurements

Radar Imaging with a Network of Digital Noise Radar Systems

Today\u27s battlefield consists of a blend of humans and machines working together to locate and monitor the enemy. Due to the threat of terrorism, today\u27s enemy can be anyone and they can exist anywhere even in populated cities. Monitoring human activities in an urban environment is a difficult problem due to walls, clutter, and other obstructions. This thesis focused on developing a network of digital noise radar sensors that could operate simultaneously to track humans and non-human targets inside rooms and through walls. The theory, application, and results are discussed throughout this thesis. A noise radar works by cross correlating the received signal with a time delayed replica of the transmit signal. A high correlation indicates a target. A digital noise radar digitizes the transmit and receive signals and accomplishes the correlation in software. A network of three digital noise radars was constructed to triangulate the (x, y) position of a target within a room. The results were presented in two-dimensional graphs. In nine out of ten cases the stationary targets were clearly identified. In eight out of ten cases the stationary targets were located within the range solution of the system, 0.375 m. In the one miss case, the results image indicated the presence of the human target, but the detection was faint and possible to miss. Tests were also accomplished with moving human targets. In these tests the network of radar systems tracked the human target in an empty and cluttered room until the target was out of range. The test results prove that a network of simultaneously operating noise radars can locate and track human and non-human targets within rooms