A New Set of Wi-Fi Dynamic Line-Based Localization Algorithms for Indoor Environments

Localization is of great importance for several fields such as healthcare and security. To achieve localization, GPS technologies are common for outdoor localization but are insufficient for indoor localization. This is because the accuracy and precision of the users’ indoor locations are influenced by many factors (e.g., multipath signal propagations). As a result, the methodologies and technologies for indoor localization services need to remain continuously under development. A related challenge is the time complexity of the methodologies which impacts the performance of the mobile phones’ limited resources. To address these challenges, a new set of fingerprinting algorithms called Fingerprinting Line-Based Nearest Neighbor (FLBNN) is proposed. Furthermore, the new set is compared to other existing Nearest Neighbor-based algorithms. When the deployment of four access points is considered, the FLBNN algorithms outperform several algorithms in terms of accuracy such as Nearest Neighbor version 2, Nearest Neighbor version 4, and Soft-Range-Limited KNN by approximately 17.1%, 7.8%, and 24.1%; respectively. With regards to precision, the new set of algorithms outperforms Path-Loss-Based Fingerprint Localization (PFL) and Dual-Scanned Fingerprint Localization (DFL) by approximately 7.0% and 60.9%; respectively. Moreover, the FLBNN algorithms have a time complexity of O(t * p) where the term t is the number of deployed centroids and the term p is the number of Path Loss exponents. In addition, the new set of algorithms achieves faster run time compared to those for PFL and DFL. As a result, this Thesis improves the cost and reliability of the indoor location services

RSSI Based Indoor Localization for Smartphone Using Fixed and Mobile Wireless Node

Nowadays with the dispersion of wireless networks, smartphones and diverse related services, different localization techniques have been developed. Global Positioning System (GPS) has a high rate of accuracy for outdoor localization but the signal is not available inside of buildings. Also other existing methods for indoor localization have low accuracy. In addition, they use fixed infrastructure support. In this paper, we present a novel system for indoor localization, which also works well outside. We have developed a mathematical model for estimating location (distance and direction) of a mobile device using wireless technology. Our experimental results on Smartphones (Android and iOS) show good accuracy (an error less than 2.5 meters). We have also used our developed system in asset tracking and complex activity recognition

Wi-Fi Fingerprinting for Indoor Positioning

Wireless Fidelity (Wi-Fi) Fingerprinting is a remarkable approach developed by modern science to detect the user’s location efficiently. Today, the Global Positioning System (GPS) is used to keep track of our current location for outdoor positioning. In GPS technology, satellite signals cannot reach indoor environments as they are shielded from obstructions so that indoor environments with a lack of Line of Sight (LoS) do not provide enough satellite signal accuracy. Since indoor environments are very difficult to track, thus, a wide variety of techniques for dealing with them have been suggested. The best way to offer an indoor positioning service with the current technology is Wi-Fi since the most commercial infrastructure is well equipped with Wi-Fi routers. For indoor positioning systems (IPS), Wi-Fi fingerprinting approaches are being extremely popular. In this paper, all the approaches for Wi-Fi fingerprinting have been reviewed for indoor position localization. Related to Wi-Fi fingerprinting, most of the algorithms have been interpreted and the previous works of other researchers have been critically analyzed in this paper to get a clear view of the Wi-Fi fingerprinting process

Wi-Fi Fingerprinting for Indoor Positioning

Wireless Fidelity (Wi-Fi) Fingerprinting is a remarkable approach developed by modern science to detect the user’s location efficiently. Today, the Global Positioning System (GPS) is used to keep track of our current location for outdoor positioning. In GPS technology, satellite signals cannot reach indoor environments as they are shielded from obstructions so that indoor environments with a lack of Line of Sight (LoS) do not provide enough satellite signal accuracy. Since indoor environments are very difficult to track, thus, a wide variety of techniques for dealing with them have been suggested. The best way to offer an indoor positioning service with the current technology is Wi-Fi since the most commercial infrastructure is well equipped with Wi-Fi routers. For indoor positioning systems (IPS), Wi-Fi fingerprinting approaches are being extremely popular. In this paper, all the approaches for Wi-Fi fingerprinting have been reviewed for indoor position localization. Related to Wi-Fi fingerprinting, most of the algorithms have been interpreted and the previous works of other researchers have been critically analyzed in this paper to get a clear view of the Wi-Fi fingerprinting process

Influence of measured radio map interpolation on indoor positioning algorithms

Indoor positioning and navigation increasingly has become popular and there are many different approaches, using different technologies. In nearly all of the approaches the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation Radio Map (RM) by analysing the environment. As this is usually done on a regular grid, the collection of Received Signal Strength Indicator (RSSI) data at every Reference Point (RP) of a RM is a time consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful as it allows researchers to spend more time with research instead of data collection. In this paper we analyse the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using 5 ESP32 micro controllers working in monitoring mode and placed around our office. We then analyse the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian Process Regression (GPR) to find balance between final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.- (undefined

Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems

Recent advances in indoor positioning systems led to a business interest in those applications and services where a precise localization is crucial. Wi-Fi fingerprinting based on machine learning and expert systems are commonly used in the literature. They compare a current fingerprint to a database of fingerprints, and then return the most similar one/ones according to: 1) a distance function, 2) a data representation method for received signal strength values, and 3) a thresholding strategy. However, most of the previous works simply use the Euclidean distance with the raw unprocessed data. There is not any previous work that studies which is the best distance function, which is the best way of representing the data and which is the effect of applying thresholding. In this paper, we present a comprehensive study using 51 distance metrics, 4 alternatives to represent the raw data (2 of them proposed by us), a thresholding based on the RSS values and the public UJIIndoorLoc database. The results shown in this paper demonstrate that researchers and developers should take into account the conclusions arisen in this work in order to improve the accuracy of their systems. The IPSs based on k-NN are improved by just selecting the appropriate configuration (mainly distance function and data representation). In the best case, 13-NN with Sørensen distance and the powed data representation, the error in determining the place (building and floor) has been reduced in more than a 50% and the positioning accuracy has been increased in 1.7 m with respect to the 1-NN with Euclidean distance and raw data commonly used in the literature. Moreover, our experiments also demonstrate that thresholding should not be applied in multi-building and multi-floor environments

WIFI BASED INDOOR POSITIONING - A MACHINE LEARNING APPROACH

Navigation has become much easier these days mainly due to advancement in satellite technology. The current navigation systems provide better positioning accuracy but are limited to outdoors. When it comes to the indoor spaces such as airports, shopping malls, hospitals or office buildings, to name a few, it will be challenging to get good positioning accuracy with satellite signals due to thick walls and roofs as obstacles. This gap led to a whole new area of research in the field of indoor positioning. Many researches have been conducting experiments on different technologies and successful outcomes have beenseen. Each technology providing indoor positioning capability has its own limitations. In this thesis, different radio frequency (RF) and non-radio frequency (Non-RF) technologies are discussed but focus is set on Wi-Fi for indoor positioning. A demo indoor positioning app is developed for the Technobothnia building at the University of Vaasa premises. This building is already equipped with Wi-Fi infrastructure. A floor plan of the building, radio maps and a fingerprinting database with Wi-Fi signal strength measurements is created with help of tools from HERE technology. The app provides real-time positioning and routing as a future visitor tool. With the exceeding amounts of available data, one of the highly popular fields is applying Machine Learning (ML) to data. It can be applied in many disciplines from medicine to space. In ML, algorithms learn from the data and make predictions. Due to the significant growth in various sensor technologies and computational power, large amounts of data can be stored and processed. Here, the ML approach is also taken to the indoor positioning challenge. An open-source Wi-Fi fingerprinting dataset is obtained from Tampere University and ML algorithms are applied on it for performing indoor positioning. Algorithms are trained with received signal strength (RSS) values with their respective reference coordinates and the user location can be predicted. The thesis provides a performance analysis of different algorithms suitable for future mobile implementations