LoRaWAN communication implementation platforms

A key role in the development of smart Internet of Things (IoT) solutions is played by wireless communication technologies, especially LPWAN (Low-Power Wide-Area Network), which are becoming increasingly popular due to their advantages: long range, low power consumption and the ability to connect multiple edge devices. However, in addition to the advantages of communication and low power consumption, the security of transmitted data is also important. End devices very often have a small amount of memory, which makes it impossible to implement advanced cryptographic algorithms on them. The article analyzes the advantages and disadvantages of solutions based on LPWAN communication and reviews platforms for IoT device communication in the LoRaWAN (LoRa Wide Area Network) standard in terms of configuration complexity. It describes how to configure an experimental LPWAN system being built at the Department of Computer Science and Telecommunications at Poznan University of Technology for research related to smart buildings

Wi-Fi Sensing: Applications and Challenges

Wi-Fi technology has strong potentials in indoor and outdoor sensing applications, it has several important features which makes it an appealing option compared to other sensing technologies. This paper presents a survey on different applications of Wi-Fi based sensing systems such as elderly people monitoring, activity classification, gesture recognition, people counting, through the wall sensing, behind the corner sensing, and many other applications. The challenges and interesting future directions are also highlighted

Comprehending the Safety Paradox and Privacy Concerns with Medical Device Remote Patient Monitoring

Medical literature identifies a number of technology-driven improvements in disease management such as implantable medical devices (IMDs) that are a standard treatment for candidates with specific diseases. Among patients using implantable cardiac defibrillators (ICD), for example, problems and issues are being discovered faster compared to patients without monitoring, improving safety. What is not known is why patients report not feeling safer, creating a safety paradox, and why patients identify privacy concerns in ICD monitoring. There is a major gap in the literature regarding the factors that contribute to perceived safety and privacy in remote patient monitoring (RPM). To address this gap, the research goal of this study was to provide an interpretive account of the experience of RPM patients. This study investigated two research questions: 1) How did RPM recipients perceive safety concerns?, and 2) How did RPM recipients perceive privacy concerns? To address the research questions, in-depth, semi-structured interviews were conducted with six participants to explore individual perceptions in rich detail using interpretative phenomenological analysis (IPA). Four themes were identified and described based on the analysis of the interviews that include — comfort with perceived risk, control over information, education, and security — emerged from the iterative review and data analysis. Participants expressed comfort with perceived risk, however being scared and anxious were recurrent subordinate themes. The majority of participants expressed negative feelings as a result of an initial traumatic event related to their devices and lived in fear of being shocked in inopportune moments. Most of these concerns stem from lack of information and inadequate education. Uncertainties concerning treatment tends to be common, due to lack of feedback from ICD RPM status. Those who knew others with ICD RPM became worrisome after hearing about incidences of sudden cardiac death (SCD) when the device either failed or did not work adequately to save their friend’s life. Participants also expressed cybersecurity concerns that their ICD might be hacked, maladjusted, manipulated with magnets, or turned off. They believed ICD RPM security was in place but inadequate as well as reported feeling a lack of control over information. Participants expressed wanting the right to be left alone and in most cases wanted to limit others’ access to their information, which in turn, created conflict within families and loved ones. Geolocation was a contentious node in this study, with most of participants reporting they did not want to be tracked under any circumstances. This research was needed because few researchers have explored how people live and interact with these newer and more advanced devices. These findings have implications for practice relating to RPM safety and privacy such as identifying a gap between device companies, practitioners, and participants and provided directions for future research to discover better ways to live with ICD RPM and ICD shock

Wi-Fi Sensing: Applications and Challenges

Wi-Fi technology has strong potentials in indoor and outdoor sensing applications, it has several important features which makes it an appealing option compared to other sensing technologies. This paper presents a survey on different applications of Wi-Fi-based sensing systems such as elderly people monitoring, activity classification, gesture recognition, people counting, through the wall sensing, behind the corner sensing, and many other applications. The challenges and interesting future directions are also highlighted

Harnessing the Power of Generative Models for Mobile Continuous and Implicit Authentication

Authenticating a user's identity lies at the heart of securing any information system. A trade off exists currently between user experience and the level of security the system abides by. Using Continuous and Implicit Authentication a user's identity can be verified without any active participation, hence increasing the level of security, given the continuous verification aspect, as well as the user experience, given its implicit nature. This thesis studies using mobile devices inertial sensors data to identify unique movements and patterns that identify the owner of the device at all times. We implement, and evaluate approaches proposed in related works as well as novel approaches based on a variety of machine learning models, specifically a new kind of Auto Encoder (AE) named Variational Auto Encoder (VAE), relating to the generative models family. We evaluate numerous machine learning models for the anomaly detection or outlier detection case of spotting a malicious user, or an unauthorised entity currently using the smartphone system. We evaluate the results under conditions similar to other works as well as under conditions typically observed in real-world applications. We find that the shallow VAE is the best performer semi-supervised anomaly detector in our evaluations and hence the most suitable for the design proposed. The thesis concludes with recommendations for the enhancement of the system and the research body dedicated to the domain of Continuous and Implicit Authentication for mobile security

RSS Models for Respiration Rate Monitoring

Received signal strength based respiration rate monitoring is emerging as an alternative non-contact technology. These systems make use of the radio measurements of short-range commodity wireless devices, which vary due to the inhalation and exhalation motion of a person. The success of respiration rate estimation using such measurements depends on the signal-to-noise ratio, which alters with properties of the person and with the measurement system. To date, no model has been presented that allows evaluation of different deployments or system configurations for successful breathing rate estimation. In this paper, a received signal strength model for respiration rate monitoring is introduced. It is shown that measurements in linear and logarithmic scale have the same functional form, and the same estimation techniques can be used in both cases. The model is numerically and empirically evaluated, and its properties are discussed in depth. The most important model implications are validated under varying signal-to-noise ratio conditions using the performances of three estimators: batch frequency estimator, recursive Bayesian estimator, and model-based estimator. The results are in coherence with the findings, and they imply that different estimators are advantageous in different signal-to-noise ratio regimes.Peer reviewe