Indoor Positioning Techniques Based on Wireless LAN

As well as delivering high speed internet, Wireless LAN (WLAN) can be used as an effective indoor positioning system. It is competitive in terms of both accuracy and cost compared to similar systems. To date, several signal strength based techniques have been proposed. Researchers at the University of New South Wales (UNSW) have developed several innovative implementations of WLAN positioning systems. This paper describes the techniques used and details the experimental results of the research

Indoor navigation systems based on data mining techniques in internet of things: a survey

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Internet of Things (IoT) is turning into an essential part of daily life, and numerous IoT-based scenarios will be seen in future of modern cities ranging from small indoor situations to huge outdoor environments. In this era, navigation continues to be a crucial element in both outdoor and indoor environments, and many solutions have been provided in both cases. On the other side, recent smart objects have produced a substantial amount of various data which demands sophisticated data mining solutions to cope with them. This paper presents a detailed review of previous studies on using data mining techniques in indoor navigation systems for the loT scenarios. We aim to understand what type of navigation problems exist in different IoT scenarios with a focus on indoor environments and later on we investigate how data mining solutions can provide solutions on those challenges

Evaluating Wi-Fi indoor positioning approaches in a real world environment

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsGlobal positioning system(GPS) does not provide generally a good positioning performance in an indoor location because of many reasons (Henniges, 2012). On the other hand, other alternatives such as the WI-FI technology has become recently in a popular use to provide indoor localization. And that is due to many reasons, such as the wide spread of WI-FI infrastructure in the indoor environments and the low cost of this technology. This study attempts to evaluate different WI-FI indoor positioning approaches in a real world environment. In particular, in retail stores and shopping malls. The pros and cons of each one of these approaches are pointed out. The main purpose of this study from the company perspective is to explore the state of the art methods and the cutting edge technologies of the WI-FI IPS and to come up with an improvement of their indoor localization system. This system forms the core of the company`s retail-analytics product that uses a Wi-Fi positioning technology to provide indoor location based services for the customers and helps retailers to better understanding their businesses

A Location Fingerprint Framework Towards Efficient Wireless Indoor Positioning Systems

Location of mobile computers, potentially indoors, is essential information to enable locationawareapplications in wireless pervasive computing. The popularity of wireless local area networks (WLANs) inside and around buildings makes positioning systems based on readily available received signal strength (RSS) from access points (APs) desirable. The fingerprinting technique associates location-dependent characteristics such as RSS values from multiple APs to a location (namely location fingerprint) and uses these characteristics to infer the location. The collection of RSS fingerprints from different locations are stored in a database called radio map, which is later used to compare to an observed RSS sample vector for estimating the MS's location. An important challenge for the location fingerprinting is how to efficiently collect fingerprintsand construct an effective radio map for different indoor environments. In addition, analytical models to evaluate and predict "precision" performance of indoor positioning systems based on location fingerprinting are lacking. In this dissertation, we provide a location fingerprint framework that will enable a construction of efficient wireless indoor systems. We develop a new analytical model that employs a proximity graph for predicting performance of indoor positioning systems based on location fingerprinting. The model approximatesprobability distribution of error distance given a RSS location fingerprint database and its associated statistics. This model also allows a system designer to perform analysis of the internal structure of location fingerprints. The analytical model is employed to identify and eliminate unnecessary location fingerprints stored in the radio map, thereby saving on computation while performing location estimation. Using the location fingerprint properties such as clustering is also shown to help reduce computational effort and create a more scalable model. Finally, by study actual measurement with the analytical results, a useful guideline for collecting fingerprints is given

Advanced Location-Based Technologies and Services

Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements

Bayesian algorithms for mobile terminal positioning in outdoor wireless environments

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A Survey of 3D Indoor Localization Systems and Technologies

Indoor localization has recently and significantly attracted the interest of the research community mainly due to the fact that Global Navigation Satellite Systems (GNSSs) typically fail in indoor environments. In the last couple of decades, there have been several works reported in the literature that attempt to tackle the indoor localization problem. However, most of this work is focused solely on two-dimensional (2D) localization, while very few papers consider three dimensions (3D). There is also a noticeable lack of survey papers focusing on 3D indoor localization; hence, in this paper, we aim to carry out a survey and provide a detailed critical review of the current state of the art concerning 3D indoor localization including geometric approaches such as angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), fingerprinting approaches based on Received Signal Strength (RSS), Channel State Information (CSI), Magnetic Field (MF) and Fine Time Measurement (FTM), as well as fusion-based and hybrid-positioning techniques. We provide a variety of technologies, with a focus on wireless technologies that may be utilized for 3D indoor localization such as WiFi, Bluetooth, UWB, mmWave, visible light and sound-based technologies. We critically analyze the advantages and disadvantages of each approach/technology in 3D localization

Sistema de localização automática de dispositivos móveis com recurso à sequências perfeitas de rádio frequência

Recent advancements in the area of nanotechnology have brought us into a new age of pervasive computing devices. These computing devices grow ever smaller and are being used in ways which were unimaginable before. Recent interest in developing a precise indoor positioning system, as opposed to existing outdoor systems, has given way to much research heading into the area. The use of these small computing devices offers many conveniences for usage in indoor positioning systems. This thesis will deal with using small computing devices Raspberry Pi’s to enable and improve position estimation of mobile devices within closed spaces. The newly patented Orthogonal Perfect DFT Golay coding sequences will be used inside this scenario, and their positioning properties will be tested. After that, testing and comparisons with other coding sequences will be done

Development of a WiFi and RFID based indoor location and mobility tracking system

Ubiquitous positioning and people mobility tracking has become one of the critical parts of our daily life. As a core element of the Location Based Services (LBS), the ubiquitous positioning capability necessitates seamless positioning across both indoor and outdoor environments. Nowadays, tracking outdoor with a relatively high accuracy and reliability can be achieved using matured technologies such as Global Navigation Satellite Systems (GNSS). However, it is still challenging for tracking in indoor environments such as airports, shopping malls and museums. The demand for indoor tracking has driven the fast development of indoor positioning and tracking technologies, especially Wi-Fi, RFID and smartphone etc. All these technologies have significantly enhanced the convenience of people’s daily life and the competitiveness of business firms. With the rapidly increased ubiquity of Wi-Fi enabled mobile phones and tablets, developing a robust location and mobility tracking system utilising such technologies will have a great potential for industry innovation and applications. This research is part of an Australian Research Council (ARC) project that involves two universities and one industry partner who is a large global shopping mall management company located in Australia. The project aims to develop a smart system for robust modelling and analysing the shopping behaviours of customers so that value-added services can be effectively provided. A number of field tests have been conducted and a large amount of data has been acquired both in the shopping mall of interest and the RMIT Indoor Positioning Laboratory. A large cohort of real users in the shopping mall were recorded where only one Wi-Fi access point (AP) connection at a time for each mobile device user was provided for our research. This makes most of the conventional tracking and positioning methods inapplicable. To overcome this constraint, a new hybrid system for positioning and mobility tracking — called single AP-connection location tracking system (SCLTS) was developed, which utilised Wi-Fi, RFID and mobile device technologies and took advantage of both the cell of origin (CoO) and fingerprinting positioning methods. Three new algorithms for Wi-Fi based indoor positioning were developed during this research. They are the common handover point determination (CHOPD) algorithm for determining the boundary of the cell; the algorithm for positioning with the case of same-line-dual-connection (SLDC) in a long narrow space (e.g., a long corridor) and the algorithm for positioning with the case of perpendicular-dual-connection of APs in a T-shape corridor for improving the positioning accuracy. The architecture of the SCLTS system was also developed as part of the implementation of the SCLTS system. Various experiments were conducted in a simulated large shopping-mall-like environment (i.e., the RMIT Indoor Positioning Lab) and the results showed that the performance of the SCLTS developed was very promising and the original goal of the project has been achieved. In addition, the two most popular indoor positioning methods — trilateration and fingerprinting were also optimised and implemented in a real industrial product and promising results have been achieved