Survey on Wireless Indoor Positioning Systems

Indoor positioning has finally testified a rise in interest, thanks to the big selection of services it is provided, and ubiquitous connectivity. There are currently many systems that can locate a person, be it wireless or by mobile phone and the most common systems in outdoor environments is the GPS, the most common in indoor environments is Wi-Fi positioning technique positioning. The improvement of positioning systems in indoor environments is desirable in many areas as it provides important facilities and services, such as airports, universities, factories, hospitals, and shopping malls. This paper provides an overview of the existing methods based on wireless indoor positioning technique. We focus in this survey on the strengths of these systems mentioned in the literature discordant with the present surveys; we also assess to additionally measure various systems from the scene of energy efficiency, price, and following accuracy instead of comparing the technologies, we also to additionally discuss residual challenges to correct indoor positioning

Filters for Wi-Fi Generated Crowd Movement Data

Cities represent large groups of people that share a common infrastructure, common social groups and/or common interests. With the development of new technologies current cities aim to become what is known as smart cities, in which all the small details of these large constructs are controlled to better improve the quality of life of its inhabitants. One of the important gears that powers a city is given by traffic, be it vehicular or pedestrian. As such traffic is closely related to all other activities that take place inside of a city. Understanding traffic is still a difficult process as we have to be able to not only measure it in the sense of how many people are using a particular path but also in analyzing where people are going and when, while still maintaining individual privacy. And all this has to be done at a scale that would cover most if not all individuals in a city. With the high increase in smartphones adoption we can reliably assume that a large part of the population in cities are carrying with them, at all times, at least one Wi-Fi enabled device. Because Wi-Fi devices are regularly transmitting signals we can rely on these devices to detect individual's movements unobtrusively without identifying or tracking any particular individual. Special sensors that monitor Wi-Fi frequencies can be placed around a city to gather data that can later be used to identify patterns in the traffic flows. We present a set of filters that can be used to minimize the amount of data needed for processing and without negatively impacting the result or the information that can be extracted from this data. Part of the filters we present can be deployed at the sensor level, making the entire system more scalable, while a different part can be executed before data processing thus enabling real time information extraction and a broader temporal and spatial range for data analysis. Some of these filters are particular to Wi-Fi but some of them can be applied to any detection system

Enhanced indoor positioning utilising wi-fi fingerprint and QR calibration techniques

The growing interest in location-based services (LBS), due to the demand for its application in personal navigation, billing and information enquiries, has expedited the research development for indoor positioning techniques. The widely used global positioning system (GPS) is a proven technology for positioning, navigation, but it performs poorly indoors. Hence, researchers seek alternative solutions, including the concept of signal of opportunity (SoOP) for indoor positioning. This research planned to use cheap solutions by utilizing available communication system infrastructure without the need to deploy new transmitters or beacons for positioning purposes. Wi-Fi fingerprinting has been identified for potential indoor positioning due to its availability in most buildings. In unplanned building conditions where the available number of APs is limited and the locations of APs are predesignated, certain positioning algorithms do not perform well consistently. In addition, there are several other factors that influence positioning accuracy, such as different path movements of users and different Wi-Fi chipset manufacturers. To overcome these challenges, many techniques have been proposed, such as collaborative positioning techniques, data fusion of radio-based positioning and mobile-based positioning that uses sensors to sense the physical movement activity of users. A few researchers have proposed combining radio-based positioning with vision-based positioning while utilizing image sensors. This work proposed integrated layers of positioning techniques, which is based on enhanced deterministic method; Bayesian estimation and Kalman filter utilising dynamic localisation region. Here, accumulated accuracy is proposed with distribution of error location by estimation at each test point on path movement. The error distribution and accumulated accuracy have been presented in graphs and tables for each result. The proposed algorithm has been enhanced by location based calibration with additional QR calibration. It allows not only correction of the actual position but the control of the errors from being accumulated by utilizing the Bayesian technique and dynamic localisation region. The position of calibration point is determined by analysing the error distribution region. In the last part, modification on Kalman filter step for calibration algorithm did further improve the location error compared to other deterministic algorithms with calibration point. The CDF plots have shown all developed techniques that provide accuracy improvement for indoor positioning based on Wi-Fi fingerprinting and QR calibration

Wireless Positioning and Tracking for Internet of Things in GPS-denied Environments

Wireless positioning and tracking have long been a critical technology for various applications such as indoor/outdoor navigation, surveillance, tracking of assets and employees, and guided tours, among others. Proliferation of Internet of Things (IoT) devices, the evolution of smart cities, and vulnerabilities of traditional localization technologies to cyber-attacks such as jamming and spoofing of GPS necessitate development of novel radio frequency (RF) localization and tracking technologies that are accurate, energy-efficient, robust, scalable, non-invasive and secure. The main challenges that are considered in this research work are obtaining fundamental limits of localization accuracy using received signal strength (RSS) information with directional antennas, and use of burst and intermittent measurements for localization. In this dissertation, we consider various RSS-based techniques that rely on existing wireless infrastructures to obtain location information of corresponding IoT devices. In the first approach, we present a detailed study on localization accuracy of UHF RF IDentification (RFID) systems considering realistic radiation pattern of directional antennas. Radiation patterns of antennas and antenna arrays may significantly affect RSS in wireless networks. The sensitivity of tag antennas and receiver antennas play a crucial role. In this research, we obtain the fundamental limits of localization accuracy considering radiation patterns and sensitivity of the antennas by deriving Cramer-Rao Lower Bounds (CRLBs) using estimation theory techniques. In the second approach, we consider a millimeter Wave (mmWave) system with linear antenna array using beamforming radiation patterns to localize user equipment in an indoor environment. In the third approach, we introduce a tracking and occupancy monitoring system that uses ambient, bursty, and intermittent WiFi probe requests radiated from mobile devices. Burst and intermittent signals are prominent characteristics of IoT devices; using these features, we propose a tracking technique that uses interacting multiple models (IMM) with Kalman filtering. Finally, we tackle the problem of indoor UAV navigation to a wireless source using its Rayleigh fading RSS measurements. We propose a UAV navigation technique based on Q-learning that is a model-free reinforcement learning technique to tackle the variation in the RSS caused by Rayleigh fading

Investigation of indoor positioning based on WLAN 802.11

The need for location based services has dramatically increased within the past few years, especially with the popularity and capability of mobile device such as smart phones and tablets. The limitation of GPS for indoor positioning has seen an increase of indoor positioning based on Wireless Local Area Network 802.11.\ud This thesis reviews the various different techniques used by applications to determine one’s location through the measurement of Wi-Fi signals. It particularly focuses on the Cisco Context-Aware Mobility which provides a Real Time Location System solution based on Wi-Fi. It details the implementation of an Android application, developed to communicate with the Cisco Context-Aware Mobility to visually display the location of the mobile device. The application was tested in a production environment. Limitations in the production environment along with the diagnostic capabilities of the Context-Aware Mobility were identified

Indoor Localization Using Wi-Fi Signals

RÉSUMÉ Plusieurs approches ont été développées pour localiser des appareils mobiles à l'intérieur de bâtiments d’une façon précise. Certaines donnent une précision de moins d'un mètre, mais elles nécessitent des infrastructures et du matériel spécifiques. D'autres utilisent une infrastructure qui est déjà déployée, mais donnent une position avec une précision inférieure. Dans ce mémoire, nous proposons plusieurs méthodes de positionnement basées sur les mesures de l'intensité du signal reçu d'une infrastructure Wi-Fi existant. Le but de ces méthodes de positionnement est de localiser le plus précisément possible l'emplacement du dispositif mobile utilisé. La première méthode de positionnement que nous proposons transforme la puissance du signal reçue en une entité appelée signature. Cette entité caractérise chaque emplacement de l'environnement où la localisation doit être effectuée. Pour localiser l'appareil mobile, la signature calculée est jumelée avec les signatures de référence les plus représentatives et qui sont déjà enregistrées dans une base de données. Dans ce mémoire, nous proposons deux approches pour produire les signatures de référence: une empirique et une théorique. La deuxième méthode de positionnement que nous proposons dans ce mémoire est de localiser les appareils mobiles en utilisant la différence entre les mesures de puissance de signaux reçus. On a appelé cette méthode la différence de puissances des signaux reçues (RSSD). Cette méthode consiste à convertir la différence de puissances des signaux reçues en des distances et d’utiliser ces distances pour estimer la position des appareils mobiles. Ensuite, nous décrivons les expériences qui nous ont conduits à développer la méthode de traitement du signal et les algorithmes de localisation. Les algorithmes et les méthodes proposés ont conduit à un système de localisation précis qui atteint 2 mètres de précision dans 90% des cas. Les résultats actuels des systèmes proposés montrent que les emplacements estimés sont précis (moins de 2 mètres) dans un environnement fermé en utilisant la méthode des signatures et une localisation précise dans les espaces ouverts en utilisant la méthode de la RSSD. Certains endroits critiques ont besoin de plus de collecte de données et plus d'informations sur l'environnement pour atteindre le même niveau de précision. Les résultats obtenus sont décrits et discutés à l’aide de cartes et de statistiques.----------ABSTRACT Several approaches have been developed to provide an accurate estimation of the position of mobile devices inside buildings. Some of them give a precision of less than one meter but they require special infrastructure and materials. Some others use an infrastructure that is already deployed but gives a position with lower precision. In this thesis, we propose several positioning methods based on the received signal strength (RSS) measurements of an existing Wi-Fi infrastructure. The aim of these positioning methods is to locate a mobile device as accurately as possible. The first method that we propose transforms the RSS to an entity called signature. This entity characterises each location of the environment where the localization should be performed. This computed signature is matched with the most representative reference signatures already recorded in a database in order to locate the mobile device. In this thesis, we propose two approaches to produce the reference signatures: an empirical and a theoretical one. The second method that we propose in this thesis is about locating the mobile devices using the difference between the received signals strength measurements. We call this method the received signal strength difference (RSSD) method. We then describe the experiments that led us to develop the signal processing method and the localization algorithms. The algorithm proposed led to an accurate localization system that reaches 2 meters of accuracy in 90% of the cases. Current results of the proposed systems show that the estimated locations are accurate (less than 2 meters) in closed environments when using the fingerprinting method and in open spaces when using the RSSD method. Some critical locations need more collected data and more information about the environment to reach the same level of accuracy. The results obtained are described and discussed using maps and statistics

MEMS Accelerometers

Micro-electro-mechanical system (MEMS) devices are widely used for inertia, pressure, and ultrasound sensing applications. Research on integrated MEMS technology has undergone extensive development driven by the requirements of a compact footprint, low cost, and increased functionality. Accelerometers are among the most widely used sensors implemented in MEMS technology. MEMS accelerometers are showing a growing presence in almost all industries ranging from automotive to medical. A traditional MEMS accelerometer employs a proof mass suspended to springs, which displaces in response to an external acceleration. A single proof mass can be used for one- or multi-axis sensing. A variety of transduction mechanisms have been used to detect the displacement. They include capacitive, piezoelectric, thermal, tunneling, and optical mechanisms. Capacitive accelerometers are widely used due to their DC measurement interface, thermal stability, reliability, and low cost. However, they are sensitive to electromagnetic field interferences and have poor performance for high-end applications (e.g., precise attitude control for the satellite). Over the past three decades, steady progress has been made in the area of optical accelerometers for high-performance and high-sensitivity applications but several challenges are still to be tackled by researchers and engineers to fully realize opto-mechanical accelerometers, such as chip-scale integration, scaling, low bandwidth, etc