No description available.

The purpose of this thesis is to devise a system based on RFID (Radio Frequency IDentification) that can be used for smart healthcare applications. Location estimation, eloping prevention and occupancy detection are monitoring applications of smart healthcare which can provide very useful information for the nursing and administration staff of the nursing-home/hospital. The introduction of ubiquitous networking along with the concepts such as Internet of Things (IoT) can certainly help achieve the goals of smart healthcare. RFID technology has features, such as low power and small size, which makes this technology suitable for researching solutions for smart healthcare. Today several nursing-home/hospital monitoring solutions exist in the market and academia alike. The solutions marketed commercially are very expensive whereas the solutions from academia provides solutions to isolated problems but a comprehensive all in one solution that can meet the need of smart healthcare monitoring applications is missing. In this thesis we present a system that is low cost and suitable for accommodating a number of the smart healthcare applications including occupancy detection, location estimation, eloping prevention and access control. The solution is implemented on a customized Openbeacon Active RFID System (OARS). Active RFID based proximity detection is the core of our system. Practical experiments based on novel Proximity Detection based Weighted Centroid Localization (PD-WCL) method were done to analyze the performance of the system with different applications to highlight the applicability of the system.

Vision-based positioning systems are founded primarily on a simple image processing technique of identifying various visually significant key-points in an image and relating them to a known coordinate system in a scene. Fiducial markers are used as a means of providing the scene with a number of specific key-points, or features, such that computer vision algorithms can quickly identify them within a captured image. This thesis proposes a reliable vision-based positioning system which utilizes a unique pseudo-random fiducial marker. The marker itself offers 49 distinct feature points to be used in position estimation. Detection of the designed marker occurs after an integrated process of adaptive thresholding, k-means clustering, color classification, and data verification. The ultimate goal behind such a system would be for indoor localization implementation in low cost autonomous mobile platforms.

Vision-based positioning systems are founded primarily on a simple image processing technique of identifying various visually significant key-points in an image and relating them to a known coordinate system in a scene. Fiducial markers are used as a means of providing the scene with a number of specific key-points, or features, such that computer vision algorithms can quickly identify them within a captured image. This thesis proposes a reliable vision-based positioning system which utilizes a unique pseudo-random fiducial marker. The marker itself offers 49 distinct feature points to be used in position estimation. Detection of the designed marker occurs after an integrated process of adaptive thresholding, k-means clustering, color classification, and data verification. The ultimate goal behind such a system would be for indoor localization implementation in low cost autonomous mobile platforms.

Indoor localization is the process of localizing people or objects inside a building in the same way GPS does in an outside environment. In recent years, researchers have successfully achieved improvement in indoor localization accuracy. Still there are many limitations to overcome in performing and achieving good accuracy in indoor localization. The interest in estimating the location of something inside a building with good accuracy is very strong. In this thesis we first propose an indoor localization technique relative to Wi-Fi access points along with a novel heuristic search based algorithm, named MuSLoc. Through simulation and comparative studies, we have shown that MuSLoc outperforms other indoor localization models without the help of fingerprinting or crowdsourcing about the environment. MuSLoc provides almost the same accuracy in LOS (Line of Sight) and NLOS (Non-Line of Sight) environments with regular infrastructure that has recently been provided by smart phones. This model doesn't require any additional hardware support in order to perform well. Further, we propose another indoor localization based Wi-Fi device tracker model, named MSTracker, which is able to track both moving and non-moving devices inside a building. This model is also free from specialized infrastructure and can perform well without any training data information. Through real time simulation and analysis we have shown that it performs more accurately than other available models. Through extensive simulations in a real time environment and analysis of performance comparatives with other available models, we have shown that both MuSLoc and MSTracker perform more accurately with COTS than any other method of indoor localization and tracking of objects inside a building. The complete package of MuSLoc and MSTracker can perform perfectly with recently available Wi-Fi modules and smartphones.

Indoor localization with proximity information in ultra-high-frequency (UHF) radio-frequency-identification (RFID) is widely considered as a potential candidate of locating items in Internet-of-Things (IoT) paradigm. First, the proximity-based methods are less affected by multi-path distortion and dynamic changes of the indoor environment compared to the traditional range-based localization methods. The objective of this dissertation is to use tag-to-tag backscattering communication link in augmented UHF RFID system (AURIS) for proximity-based indoor localization solution. Tag-to-tag backscattering communication in AURIS has an obvious advantage over the conventional reader-to-tag link for proximity-based indoor localization by keeping both landmark and mobile tags simple and inexpensive. This work is the very first thesis evaluating proximity-based localization solution using tag-to-tag backscattering communication.Our research makes the contributions in terms of phase cancellation effect, the improved mathematical models and localization algorithm. First, we investigate the phase cancellation effect in the tag-to-tag backscattering communication, which has a significant effect on proximity-based localization. We then present a solution to counter such destructive effect by exploiting the spatial diversity of dual antennas. Second, a novel and realistic detection probability model of ST-to-tag detection is proposed. In AURIS, a large set of passive tags are placed at known locations as landmarks, and STs are attached mobile targets of interest. We identify two technical roadblocks of AURIS and existing localization algorithms as false synchronous detection assumption and state evolution model constraints. With the new and more realistic detection probability model we explore the use of particle filtering methodology for localizing ST, which overcomes the aforementioned roadblocks. Last, we propose a landmark-based sequential localization and mapping framework (SQLAM) for AURIS to locate STs and passive tags with unknown locations, which leverages a set of passive landmark tags to localize ST, and sequentially constructs a geographical map of passive tags with unknown locations while ST is moving in the environment. Mapping passive tags with unknown locations accurately leads to practical advantages. First, the localization capability of AURIS is not confined to the objects carrying STs. Second, the problem of failed landmark tags is addressed by including passive tags with resolved locations into landmark set. Each of the contributions is supported by extensive computer simulation to demonstrate the performance of enhancements.

The purpose of this thesis is to devise a system based on RFID (Radio Frequency IDentification) that can be used for smart healthcare applications. Location estimation, eloping prevention and occupancy detection are monitoring applications of smart healthcare which can provide very useful information for the nursing and administration staff of the nursing-home/hospital. The introduction of ubiquitous networking along with the concepts such as Internet of Things (IoT) can certainly help achieve the goals of smart healthcare. RFID technology has features, such as low power and small size, which makes this technology suitable for researching solutions for smart healthcare. Today several nursing-home/hospital monitoring solutions exist in the market and academia alike. The solutions marketed commercially are very expensive whereas the solutions from academia provides solutions to isolated problems but a comprehensive all in one solution that can meet the need of smart healthcare monitoring applications is missing. In this thesis we present a system that is low cost and suitable for accommodating a number of the smart healthcare applications including occupancy detection, location estimation, eloping prevention and access control. The solution is implemented on a customized Openbeacon Active RFID System (OARS). Active RFID based proximity detection is the core of our system. Practical experiments based on novel Proximity Detection based Weighted Centroid Localization (PD-WCL) method were done to analyze the performance of the system with different applications to highlight the applicability of the system.

Public transportation provides people with access to education, employment, health and community activities. However, navigating inside public hubs for people with disabilities such as cognitive or mobility impairments can be very challenging and dangerous. With the rapid development of digital technology such as Smartphones and sensors, there are unprecedented opportunities to assist people with disabilities to conquer these challenges. In this research, we aim to create a two-step indoor navigation solution for users with different mobility and orientation abilities. In the first step, we developed a virtual reality-based pre-travel training module that enables users to familiarize themselves with the virtual environment which represents the physical environment. After users feel confident and familiar enough with the environment, they proceed to the second step in which they visit the physical environment and use our real-time navigation assistance module. The pre-travel training module is developed using a Unity-based 3D game and includes a virtual indoor environment that represents the physical environment. The game provides a navigation function that highlights the path between the user location and the chosen destination. Considering the unique needs of cognitive impaired users, we designed action training modules in the game environment which train the user to use the ticket machine, fare gate and call boxes. Such training modules help cognitive impaired users familiarize themselves with the environment as well as gain confidence to experience the physical environment. When the users are ready to visit the physical environment, they use our real-time navigation assistance module which includes the same 3D virtual environment developed for the pre-travel training module. This approach is particularly important for people with cognitive impairment since they cannot organize navigation cues effectively. Using the Bluetooth Low Energy (BLE) infrastructure in the environment, our localization algorithm can track the user location in real-time. Subsequently, the user’s location will be integrated into the game environment so that the navigation path between the user’s current location and the selected destination can be generated and visualized by the user on the fly.

No description available.

A disaster usually severely harms human health and property. After a disaster, great amount of information of a disaster area is needed urgently. The information not only indicates the severity of the disaster, but also is crucial for an efficient search and rescue process. In order to quickly and accurately collect real time information in a disaster scenario, a mobile platform is developed for an outdoor scenario and a localization and navigation system for responders is introduced for an indoor scenario. The mobile platform has been integrated to the DIORAMA system. It is built with a 6-wheel robot chassis along with an Arduino microcontroller. Controlled by a mounted Android smartphone, the mobile platform can receive commands from incident commanders and quickly respond to the commands. While patrolling in a disaster area, a constant RFID signal is collected to improve the localization accuracy of victims. Pictures and videos are also captured in order to enhance the situational awareness of rescuers. The design of the indoor information collection is focused on the responder side. During a disaster scenario, it is hard to track responders’ locations in an indoor environment. In this thesis, an indoor localization and navigation system based on Bluetooth low energy and Android is developed for helping responders report current location and quickly find the right path in the environment. Different localization algorithms are investigated and implemented. A navigation system based on A­* is also proposed.