In modern life, there are many applications where positioning plays an important role. People have developed the global positioning system (GPS) to locate world wide position with error in decameter scales, which brings people much convenience. However, the accuracy of GPS is too low for indoor localization. The signals will drop down due to the signal attenuation caused by construction materials. With the well-developed GPS being indispensable for outdoor activities, many researchers have been also devoted to seeking an indoor positioning system to realize indoor localization with acceptable error. Indoor localization can be very useful in different situations, like locating, tracking, navigation and identification. For example, in the mall, locating the exact goods for customers can provide much convenience and benefits. Locating and tracking in the airport can greatly help passengers save their time and energy in reaching the destination. In another general scenario of identification, the population of observed targets is usually larger than just one. Hence, only with small error, indoor localization system (ILS) can be able to identify the targets despite the neighbors. Due to the emerging and urging demands of increasing the accuracy of indoor localization, we propose a novel design of three dimensional (3-D). optical receiver for visible light communication (VLC) indoor positioning system. First, we model the optical wireless channel. Then we utilize modified triangulation method to obtain more robust receiver position by using at least two light-emitting diodes (LEDs) and one receiver consisting of nine photodetectors (PDs). Finally, the improved algorithm is implemented and the results are shown under our three dimensional multiple photodetectors (multi-PDs) structure receiver. In the simulation, we take the parameters of Lambertian radiation pattern, LEDs and PDs as those shown in [1] . To be noticed, our design of multi-PDs receiver is fully expanded into three dimensions compared with the pyramid receiver (PR), which allows indoor positioning with our receiver structure to be more robust to the higher or corner positions. The details will be explained in the following sections. Based on Multiple-Photodiodebased Indoor Positioning algorithm [1], the indoor positioning algorithm is improved by redefining the optimization problem of obtaining the direction from receiver to LED and using weighted triangulation method to locate receiver position. We admit the solution under the redefined problem is not optimal to the actual problem. Yet, our given solution is better to that in [1] due to the existence of noise, which is reasonable and has been verified.

Low-infrastructure techniques for indoor localization attempt to provide indoor positioning information for users, without requiring the installation of specialized transmitting or receiving hardware. Such an approach should encourage further adoption of indoor positioning systems by reducing the installation burden on individual building owners. If fully adopted, indoor positioning could prove to be a valuable addition to the existing outdoor localization system based on GPS. In this work, a particle filter is used to combine motion and light data in order to provide positioning information for a user in an indoor environment. A simple lighting model is used to predict light measurements, while an orientation tracking algorithm provides information about user motion. The system is shown to work with the existing lighting infrastructure of a building, though the addition of visible light communication (VLC) enabled light fixtures is shown to further improve performance. An experimental demonstration of the proposed system is provided, which indicates that tracking accuracy on the order of ten’s of centimetres is possible with very low infrastructure requirements. / Thesis / Master of Applied Science (MASc)

Internet of Things (iot) is a growing field enabled by many different technologies. One of these technologies is  Bluetooth Low Energy (ble). It is of interest to investigate the potential of ble and one interesting, currently unsupported, feature is mesh networking. This thesis work aims to investigate whether it is possible to design and implement a mesh network protocol using ble. To verify the implemented mesh network protocols functionality an indoor localization system has been implemented upon the BLE mesh network protocol. Furthermore this thesis work investigates if an indoor localization system can benefit from using a mesh network. The results of the investigation is a proof of concept of a functional ble mesh network protocol implemented on hardware and tested in a real environment. Tests show that the implemented localization system has similar accuracy as other rssi based indoor localization systems. The largest advantage found for a mesh based indoor localization is the ability to localize objects outside of the radio propagation range of the user. This feature is enabled by multi-hop messaging in the mesh network.

WiFi-based indoor localization is emerging as a new positioning technology. In this work, we present our efforts to find the best recommender system based on the indoor location tracks collected from the Bow Valley shopping mall for one week. The time a user spends in a shop is considered as an implicit preference and different mapping algorithms are proposed to map the time to a more realistic rating value. A new distribution error metric is proposed to examine the mapping algorithms. Eleven different recommender systems are built and evaluated in terms of accuracy and execution time. The Slope-One recommender system with a logarithmic mapping algorithm is finally selected with a score of 1.292, distribution error of 0.178 and execution time of 0.39 seconds for ten runs.

WiFi-based indoor localization is emerging as a new positioning technology. In this work, we present our efforts to find the best recommender system based on the indoor location tracks collected from the Bow Valley shopping mall for one week. The time a user spends in a shop is considered as an implicit preference and different mapping algorithms are proposed to map the time to a more realistic rating value. A new distribution error metric is proposed to examine the mapping algorithms. Eleven different recommender systems are built and evaluated in terms of accuracy and execution time. The Slope-One recommender system with a logarithmic mapping algorithm is finally selected with a score of 1.292, distribution error of 0.178 and execution time of 0.39 seconds for ten runs.

Location tracking on mobile devices like smartphones has already begun to revolutionize personal navigation. Unfortunately, these services perform poorly indoors when GPS signals are no longer available. Highly accurate indoor location tracking would enhance a wide variety of applications including: building navigation (malls, factories, airports), augmented reality, location-aware pervasive computing, targeted advertising, social networking, participatory sensing and could even support next generation beam forming MIMO wireless networks. Current indoor localization systems for smartphones often use RF signal strength from WiFi access points or Bluetooth Low Energy (BLE) beacons to fingerprint indoor locations. Such systems are sensitive to environmental changes and obstructions, require extensive training procedures and are limited in both absolute as well as semantic localization accuracy. We propose using audio signals in the ultrasound spectrum, just above the human hearing range, to provide ranging and localization for many off-the-shelf mobile devices that are equipped with microphones. Ultrasonic ranging provides several advantages over RF-based ranging and fingerprinting approaches, which make it attractive for indoor localization. A relatively low propagation speed and carrier frequency allow for precise propagation time measurements in software using commodity hardware. Acoustic signals also have a low penetration depth, which confines them to target areas for accurate semantic localization. In this dissertation we address several challenges related to acoustic localization, including system scalability, ranging and localization accuracy, energy efficiency, robustness to noise, elimination of human perceivable audio artifacts, efficient use of limited acoustic bandwidth and rapid deployment strategies.

This thesis is a part of a research project performed by two MS students Yang Yang and the author. The overall objective of the project is the design, implementation, and performance evaluation of algorithms for newborn localization and tracking in hospitals using Apple iBeacon technology. In the research project, I lead the path-loss modeling of iBeacon, design of algorithms for in-room presence detection system, and analysis of the accelerometer sensor. My partner, Yang Yang, leads the performance evaluation of the localization system using Cramer Rao Lower Bound (CRLB). This manuscript describes the project with a focus on my contributions in modeling the behavior of sensors and presence detection algorithms. Today, RFID detection is the most popular indoor detection technique. It provides high precision detection rate to distinguish the number of people in certain rooms of a building. However, special scanners and manual operations are required. This increases the cost and operation complexity. With the recent introduction of iBeacon by Apple, possibility of more efficient in-room presence detection has emerged for specific applications. An example of these applicatons is recording the number of visitors and newborns in a nursery room inside a hospital. The iBeacon uses Bluetooth Low Energy (BLE) technology for proximity broadcasting. Additionally, iBeacon carries a motion detection sensor, which can be utilized for counting the number of people and newborns entering and leaving a room. In this thesis we introduce a novel intelligent in-room presence detection system using iBeacon for the newborns in hospitals to determine the number of visitors and newborns' location in the nursery room. We first develop a software application on iPhone to receive and extract the necessary data from iBeacon for further analysis. We build the path-loss model for the iBeacon based on the received signal strength (RSS) of the iBeacon, which is used for performance evaluation using CRLB in Yang Yang's project. We also utilize the accelerometer in the smart phones to improve the performance of our detection system.

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While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, indoor localization with no pre-deployment effort in an indoor space, such as an office building corridor, with WiFi coverage but no apriori knowledge of the placement of the access points(APs) is implemented for mobile devices. WiFi Received Signal Strength(RSS) in the considered environment is used to build radio maps using WiFi fingerprinting approach. Two architectures are developed based on this localization algorithm. The first one involves a client-server approach where the localization algorithm runs on the server whereas the second one is a standalone architecture and the algorithm runs on the SD card of the mobile device.

Indoor occupant localization is a key component of location-based smart-space applications. Such applications are expected to save energy and provide productivity gains and health benefits. Many traditional camera-based indoor localization systems use visual information to detect and analyze the states of room occupants. These systems, however, may not be acceptable in privacy-sensitive scenarios since high-resolution images may reveal room and occupant details to eavesdroppers. To address visual privacy concerns, approaches have been developed using extremely-low-resolution light sensors, which provide limited visual information and preserve privacy even if hacked. These systems preserve visual privacy and are reasonably accurate, but they fail in the presence of noise and ambient light changes. This dissertation focuses on two-dimensional localization of an occupant on the floor plane, where three goals are considered in the development of an indoor localization system: accuracy, robustness and visual privacy preservation. Unlike techniques that preserve user privacy by degrading full-resolution data, this dissertation focuses on an array of single-pixel light sensors. Furthermore, to make the system robust to noise, ambient light changes and sensor failures, the scene is actively illuminated by modulating an array of LED light sources, which allows algorithms to use light transported from sources to sensors (described as light transport matrix) instead of raw sensor readings. Finally, to assure accurate localization, both principled model-based algorithms and learning-based approaches via active scene illumination are proposed. In the proposed model-based algorithm, the appearance of an object is modeled as a change in floor reflectivity in some area. A ridge regression algorithm is developed to estimate the change of floor reflectivity from change in the light transport matrix caused by appearance of the object. The region of largest reflectivity change identifies object location. Experimental validation demonstrates that the proposed algorithm can accurately localize both flat objects and human occupants, and is robust to noise, illumination changes and sensor failures. In addition, a sensor design using aperture grids is proposed which further improves localization accuracy. As for learning-based approaches, this dissertation proposes a convolutional neural network, which reshapes the input light transport matrix to take advantage of spatial correlations between sensors. As a result, the proposed network can accurately localize human occupants in both simulations and the real testbed with a small number of training samples. Moreover, unlike model-based approaches, the proposed network does not require modeling assumptions or knowledge of room, sources and sensors.