Device-free indoor localisation with non-wireless sensing techniques : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Electronics and Computer Engineering, Massey University, Albany, New Zealand

Global Navigation Satellite Systems provide accurate and reliable outdoor positioning to support a large number of applications across many sectors. Unfortunately, such systems do not operate reliably inside buildings due to the signal degradation caused by the absence of a clear line of sight with the satellites. The past two decades have therefore seen intensive research into the development of Indoor Positioning System (IPS). While considerable progress has been made in the indoor localisation discipline, there is still no widely adopted solution. The proliferation of Internet of Things (IoT) devices within the modern built environment provides an opportunity to localise human subjects by utilising such ubiquitous networked devices. This thesis presents the development, implementation and evaluation of several passive indoor positioning systems using ambient Visible Light Positioning (VLP), capacitive-flooring, and thermopile sensors (low-resolution thermal cameras). These systems position the human subject in a device-free manner (i.e., the subject is not required to be instrumented). The developed systems improve upon the state-of-the-art solutions by offering superior position accuracy whilst also using more robust and generalised test setups. The developed passive VLP system is one of the first reported solutions making use of ambient light to position a moving human subject. The capacitive-floor based system improves upon the accuracy of existing flooring solutions as well as demonstrates the potential for automated fall detection. The system also requires very little calibration, i.e., variations of the environment or subject have very little impact upon it. The thermopile positioning system is also shown to be robust to changes in the environment and subjects. Improvements are made over the current literature by testing across multiple environments and subjects whilst using a robust ground truth system. Finally, advanced machine learning methods were implemented and benchmarked against a thermopile dataset which has been made available for other researchers to use

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Capacitor Mismatch Calibration Technique to Improve the SFDR of 14-Bit SAR ADC

This paper presents mismatch calibration technique to improve the SFDR in a 14-bit successive approximation register (SAR) analog-to-digital converter (ADC) for wearable electronics application. Behavioral Monte-Carlo simulations are applied to demonstrate the effect of the proposed method where no complex digital calibration algorithm or auxiliary calibration DAC needed. Simulation results show that with a mismatch error typical of modern technology, the SFDR is enhanced by more than 20 dB with the proposed technique for a 14-bit SAR ADC

Distributed physical sensors network for the protection of critical infrastractures against physical attacks

The SCOUT project is based on the use of multiple innovative and low impact technologies for the protection of space control ground stations and the satellite links against physical and cyber-attacks, and for intelligent reconfiguration of the ground station network (including the ground node of the satellite link) in the case that one or more nodes fail. The SCOUT sub-system devoted to physical attacks protection, SENSNET, is presented. It is designed as a network of sensor networks that combines DAB and DVB-T based passive radar, noise radar, Ku-band radar, infrared cameras, and RFID technologies. The problem of data link architecture is addressed and the proposed solution described

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Privacy-preserving human behaviour monitoring through thermal vision

Despite the abundance of human-centred research to support domestic human behaviour monitoring in various vital applications, there are still notable limitations to deploying such systems on a broader scale. The main challenge is the trade-off between privacy, performance, and cost of assistive technologies to support older adults to live independently in their own homes. For example, the traditional vision-based sensing approach provides excellent performance while violating human privacy in domestic environments. In contrast, the ambient sensing approach, e.g., employing Passive Infra-Red (PIR) sensors, maintains human privacy but suffers significant performance hindrances in realistic scenarios such as multi-occupancy environments. This research proposes to utilise the Thermal Sensor Array (TSA) to adjust the trade-off between privacy and performance in domestic environment applications. The rationale behind proposing this sensor for human behaviour monitoring applications is its claimed advantages to perform well while maintaining human privacy, low-cost, and noncontact capabilities. Nevertheless, there has not been sufficient related work to empirically validate the hypothesis of using this low-resolution imager in domestic monitoring. Furthermore, most published works that use the TSA have not yet reached the deployment stage due to the TSA sensing constraints. In particular, TSA is sensitive to environmental thermal noise, and its Field of View (FoV) is not wide enough to cover a large inspection area. Intelligent algorithms should be employed in order to avoid these limitations. The focus of this thesis is to investigate the human physiological and behavioural thermal patterns for privacy-preserving human behaviour monitoring to support the independent living of older adults in a multi-occupancy environment by using TSA. This will be achieved through signal processing and machine learning techniques. To achieve this aim, the research methodology is drawn into two main directions. First, human physiological processing of the human thermal signal. Second, human behavioural processing of the human motion signal. This drawn methodology resulted in four main novel contributions. The first novel contribution of this research is to propose an adaptive segmentation of the human physiological presence and count the number of people from different sensor placements, indoor environments, and human-to-sensor distance. The second contribution is to extract localisation knowledge of the human physiological appearance in terms of human-to-sensor distance and human-to-human distance. Extracting human localisation knowledge is also applicable in other applications such as caregivers and care time monitoring. The third contribution is to fuse multiple TSAs to cover a wide inspection area, e.g., private or care homes. Hence, objects that appear in the low-resolution thermal images acquired from TSA have low intra-class variations and high inter-class similarities, making the identification of the overlapping regions through matching a comparable template image in multiple images very difficult. This research proposes a motion-based approach to fuse multiple TSAs and learn the domestic environment layout with a privacy improvement of utilising TSA in potential monitoring applications running in the cloud. Inspired by the results from this stage of the research, the fourth contribution of the research presented in this thesis is a human-in-the-loop fall detection approach in the Activities of Daily Living (ADLs) that reduces the false-positive alerts while keeping the false-negative fall predictions as low as possible. The novel solutions and the results presented in this thesis demonstrate a significant contribution toward enabling privacy-preserving human behaviour monitoring

Proactive extraction of IoT device capabilities for security applications

2020 Spring.Includes bibliographical references.Internet of Things (IoT) device adoption is on the rise. Such devices are mostly self-operated and require minimum user interventions. This is achieved by abstracting away their design complexities and functionalities from users. However, this abstraction significantly limits a user's insights on evaluating the true capabilities (i.e., what actions a device can perform) of a device and hence, its potential security and privacy threats. Most existing works evaluate the security of those devices by analyzing the environment data (e.g., network traffic, sensor data, etc.). However, such approaches entail collecting data from encrypted traffic, relying on the quality of the collected data for their accuracy, and facing difficulties in preserving both utility and privacy of the data. We overcome the above-mentioned challenges and propose a proactive approach to extract IoT device capabilities from their informational specifications to verify their potential threats, even before a device is installed. More specifically, we first introduce a model for device capabilities in the context of IoT. Second, we devise a technique to parse the vendor-provided materials of IoT devices and enumerate device capabilities from them. Finally, we apply the obtained capability model and extraction technique in a proactive access control model to demonstrate the applicability of our proposed solution. We evaluate our capability extraction approach in terms of its efficiency and enumeration accuracy on devices from three different vendors

Visible Light Communication Cyber Security Vulnerabilities For Indoor And Outdoor Vehicle-To-Vehicle Communication

Light fidelity (Li-Fi), developed from the approach of Visible Light Communication (VLC), is a great replacement or complement to existing radio frequency-based (RF) networks. Li-Fi is expected to be deployed in various environments were, due to Wi-Fi congestion and health limitations, RF should not be used. Moreover, VLC can provide the future fifth generation (5G) wireless technology with higher data rates for device connectivity which will alleviate the traffic demand. 5G is playing a vital role in encouraging the modern applications. In 2023, the deployment of all the cellular networks will reach more than 5 billion users globally. As a result, the security and privacy of 5G wireless networks is an essential problem as those modern applications are in people\u27s life everywhere. VLC security is as one of the core physical-layer security (PLS) solutions for 5G networks. Due to the fact that light does not penetrate through solid objects or walls, VLC naturally has higher security and privacy for indoor wireless networks compared to RF networks. However, the broadcasting nature of VLC caused concerns, e.g., eavesdropping, have created serious attention as it is a crucial step to validate the success of VLC in wild. The aim of this thesis is to properly address the security issues of VLC and further enhance the VLC nature security. We analyzed the secrecy performance of a VLC model by studying the characteristics of the transmitter, receiver and the visible light channel. Moreover, we mitigated the security threats in the VLC model for the legitimate user, by 1) implementing more access points (APs) in a multiuser VLC network that are cooperated, 2) reducing the semi-angle of LED to help improve the directivity and secrecy and, 3) using the protected zone strategy around the AP where eavesdroppers are restricted. According to the model\u27s parameters, the results showed that the secrecy performance in the proposed indoor VLC model and the vehicle-to-vehicle (V2V) VLC outdoor model using a combination of multiple PLS techniques as beamforming, secure communication zones, and friendly jamming is enhanced. The proposed model security performance was measured with respect to the signal to noise ratio (SNR), received optical power, and bit error rate (BER) Matlab simulation results