Bluetooth low energy based occupancy detection for emergency management

A reliable estimation of an area’s occupancy can be beneficial to a large variety of applications, and especially in relation to emergency management. For example, it can help detect areas of priority and assign emergency personnel in an efficient manner. However, occupancy detection can be a major challenge in indoor environments. A recent technology that can prove very useful in that respect is Bluetooth Low Energy (BLE), which is able to provide the location of a user using information from beacons installed in a building. Here, we evaluate BLE as the primary means of occupancy estimation in an indoor environment, using a prototype system composed of BLE beacons, a mobile application and a server. We employ three machine learning approaches (k-nearest neighbours, logistic regression and support vector machines) to determine the presence of occupants inside specific areas of an office space and we evaluate our approach in two independent experimental settings. Our experimental results indicate that combining BLE with machine learning is certainly promising as the basis for occupancy estimation

Indoor navigation for the visually impaired : enhancements through utilisation of the Internet of Things and deep learning

Wayfinding and navigation are essential aspects of independent living that heavily rely on the sense of vision. Walking in a complex building requires knowing exact location to find a suitable path to the desired destination, avoiding obstacles and monitoring orientation and movement along the route. People who do not have access to sight-dependent information, such as that provided by signage, maps and environmental cues, can encounter challenges in achieving these tasks independently. They can rely on assistance from others or maintain their independence by using assistive technologies and the resources provided by smart environments. Several solutions have adapted technological innovations to combat navigation in an indoor environment over the last few years. However, there remains a significant lack of a complete solution to aid the navigation requirements of visually impaired (VI) people. The use of a single technology cannot provide a solution to fulfil all the navigation difficulties faced. A hybrid solution using Internet of Things (IoT) devices and deep learning techniques to discern the patterns of an indoor environment may help VI people gain confidence to travel independently. This thesis aims to improve the independence and enhance the journey of VI people in an indoor setting with the proposed framework, using a smartphone. The thesis proposes a novel framework, Indoor-Nav, to provide a VI-friendly path to avoid obstacles and predict the user s position. The components include Ortho-PATH, Blue Dot for VI People (BVIP), and a deep learning-based indoor positioning model. The work establishes a novel collision-free pathfinding algorithm, Orth-PATH, to generate a VI-friendly path via sensing a grid-based indoor space. Further, to ensure correct movement, with the use of beacons and a smartphone, BVIP monitors the movements and relative position of the moving user. In dark areas without external devices, the research tests the feasibility of using sensory information from a smartphone with a pre-trained regression-based deep learning model to predict the user s absolute position. The work accomplishes a diverse range of simulations and experiments to confirm the performance and effectiveness of the proposed framework and its components. The results show that Indoor-Nav is the first type of pathfinding algorithm to provide a novel path to reflect the needs of VI people. The approach designs a path alongside walls, avoiding obstacles, and this research benchmarks the approach with other popular pathfinding algorithms. Further, this research develops a smartphone-based application to test the trajectories of a moving user in an indoor environment

iBeacon Applications and Hybrid Wi-Fi Localization

Apple introduced iBeacon to the world in 2013. iBeacon allows you to do indoor geolocation and has essential features to application development. Since 2007, Wi-Fi localization has become the most popular indoor geolocation technology after used with iPhone. The purpose of this project was to develop an iBeacon application and use iBeacon’s signals to improve the accuracy of Wi-Fi’s localization. For the application, we developed an algorithm that allows us to count the number of people that are in a room and broadcast this information based on proximity. While the iBeacons are set up in place for this application, we also integrated it with Wi-Fi to improve the accuracy of Wi-Fi geolocation. We achieved this by developing a new algorithm called path-loss based nearest neighbor

Indoor positioning system survey using BLE beacons

This project provides a survey of indoor positioning systems and reports experimental work with Bluetooth Low Energy (BLE) Beacons. A positioning algorithm based on the Received Signal Strength Index (RSSI) from Bluetooth Low Energy signals is proposed for indoor tracking of the position of a drone. Experimental tests for characterization of beacon signals are presented. The application of a Kalman filter to reduce the effect of fluctuations in beacons signals is described

A Review of pedestrian indoor positioning systems for mass market applications

In the last decade, the interest in Indoor Location Based Services (ILBS) has increased stimulating the development of Indoor Positioning Systems (IPS). In particular, ILBS look for positioning systems that can be applied anywhere in the world for millions of users, that is, there is a need for developing IPS for mass market applications. Those systems must provide accurate position estimations with minimum infrastructure cost and easy scalability to different environments. This survey overviews the current state of the art of IPSs and classifies them in terms of the infrastructure and methodology employed. Finally, each group is reviewed analysing its advantages and disadvantages and its applicability to mass market applications

Bluetooth 5.0 Suitability Assessment for Emergency Response within Fire Environments

Natural disasters, such as wildfires, can cause widespread devastation. Future-proofing infrastructure, such as buildings and bridges, through technological advancements is crucial to minimize their impact. Fires in disasters often stem from damaged fuel lines and electrical equipment, such as the 2018 California wildfire caused by a power line fault. To enhance safety, IoT applications can continuously monitor the health of emergency personnel. Using Bluetooth 5.0 and wearables in mesh networks, these apps can alert others about an individual’s location during emergencies. However, fire can disrupt wireless networks. This study assesses Bluetooth 5.0’s performance in transmitting signals in fire conditions. It examined received signal strength indicator (RSSI) values in a front open-fire chamber using both Peer-to-Peer (P2P) and mesh networks. The experiment considered three transmission heights of 0.61, 1.22, and 1.83 m and two distances of 11.13 m and 1.52 m. The study demonstrated successful signal transmission with a maximum loss of only 2 dB when transmitting through the fire. This research underscores the potential for reliable communication in fire-prone environments, improving safety during natural disasters

Securing Medical Devices and Protecting Patient Privacy in the Technological Age of Healthcare

The healthcare industry has been adopting technology at an astonishing rate. This technology has served to increase the efficiency and decrease the cost of healthcare around the country. While technological adoption has undoubtedly improved the quality of healthcare, it also has brought new security and privacy challenges to the industry that healthcare IT manufacturers are not necessarily fully prepared to address. This dissertation explores some of these challenges in detail and proposes solutions that will make medical devices more secure and medical data more private. Compared to other industries the medical space has some unique challenges that add significant constraints on possible solutions to problems. For example, medical devices must operate reliably even in the face of attack. Similarly, due to the need to access patient records in an emergency, strict enforcement of access controls cannot be used to prevent unauthorized access to patient data. Throughout this work we will explore particular problems in depth and introduce novel technologies to address them. Each chapter in this dissertation explores some aspect of security or privacy in the medical space. We present tools to automatically audit accesses in electronic medical record systems in order to proactively detect privacy violations; to automatically fingerprint network-facing protocols in order to non-invasively determine if particular devices are vulnerable to known attacks; and to authenticate healthcare providers to medical devices without a need for a password in a way that protects against all known attacks present in radio-based authentication technologies. We also present an extension to the widely-used beacon protocol in order to add security in the face of active attackers; and we demonstrate an overhead-free solution to protect embedded medical devices against previously unpreventable attacks that evade existing control- flow integrity enforcement techniques by leveraging insecure built-in features in order to maliciously exploit configuration vulnerabilities in devices

A Mobile Cyber-Physical System Framework for Aiding People with Visual Impairment

It is a challenging problem for researchers and engineers in the assistive technology (AT) community to provide suitable solutions for visually impaired people (VIPs) through AT to meet orientation, navigation and mobility (ONM) needs. Given the spectrum of assistive technologies currently available for the purposes of aiding VIPs with ONM, our literature review and survey have shown that there is a reluctance to adopt these technological solutions in the VIP community. Motivated by these findings, we think it critical to re-examine and rethink the approaches that have been taken. It is our belief that we need to take a different and innovative approach to solving this problem. We propose an integrated mobile cyber-physical system framework (MCPSF) with an \u27agent\u27 and a \u27smart environment\u27 to address VIP\u27s ONM needs in urban settings. For example, one of the essential needs for VIPs is to make street navigation easier and safer for them as pedestrians. In a busy city neighborhood, crossing a street is problematic for VIPs: knowing if it is safe; knowing when to cross; and being sure to remain on path and not collide or interfere with objects and people. These remain issues keeping VIPs from a truly independent lifestyle. In this dissertation, we propose a framework based on mobile cyber-physical systems (MCPS) to address VIP\u27s ONM needs. The concept of mobile cyber-physical systems is intended to bridge the physical space we live in with a cyberspace filled with unique information coming from IoT devices (Internet of Things) which are part of Smart City infrastructure. The devices in the IoT may be embedded in different kinds of physical structures. People with vision loss or other special needs may have difficulties in comprehending or perceiving signals directly in the physical space, but they can make such connections in cyberspace. These cyber connections and real-time information exchanges will enable and enhance their interactions in the physical space and help them better navigate through city streets and street crossings. As part of the dissertation work, we designed and implemented a proof of concept prototype with essential functions to aid VIP’s for their ONM needs. We believe our research and prototype experience opened a new approach to further research areas that might enhance ONM functions beyond our prototype with potential commercial product development