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

Smart hierarchical WiFi localization system for indoors

Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014En los últimos años, el número de aplicaciones para smartphones y tablets ha crecido rápidamente. Muchas de estas aplicaciones hacen uso de las capacidades de localización de estos dispositivos. Para poder proporcionar su localización, es necesario identificar la posición del usuario de forma robusta y en tiempo real. Tradicionalmente, esta localización se ha realizado mediante el uso del GPS que proporciona posicionamiento preciso en exteriores. Desafortunadamente, su baja precisión en interiores imposibilita su uso. Para proporcionar localización en interiores se utilizan diferentes tecnologías. Entre ellas, la tecnología WiFi es una de las más usadas debido a sus importantes ventajas tales como la disponibilidad de puntos de acceso WiFi en la mayoría de edificios y que medir la señal WiFi no tiene coste, incluso en redes privadas. Desafortunadamente, también tiene algunas desventajas, ya que en interiores la señal es altamente dependiente de la estructura del edificio por lo que aparecen otros efectos no deseados, como el efecto multicamino o las variaciones de pequeña escala. Además, las redes WiFi están instaladas para maximizar la conectividad sin tener en cuenta su posible uso para localización, por lo que los entornos suelen estar altamente poblados de puntos de acceso, aumentando las interferencias co-canal, que causan variaciones en el nivel de señal recibido. El objetivo de esta tesis es la localización de dispositivos móviles en interiores utilizando como única información el nivel de señal recibido de los puntos de acceso existentes en el entorno. La meta final es desarrollar un sistema de localización WiFi para dispositivos móviles, que pueda ser utilizado en cualquier entorno y por cualquier dispositivo, en tiempo real. Para alcanzar este objetivo, se propone un sistema de localización jerárquico basado en clasificadores borrosos que realizará la localización en entornos descritos topológicamente. Este sistema proporcionará una localización robusta en diferentes escenarios, prestando especial atención a los entornos grandes. Para ello, el sistema diseñado crea una partición jerárquica del entorno usando K-Means. Después, el sistema de localización se entrena utilizando diferentes algoritmos de clasificación supervisada para localizar las nuevas medidas WiFi. Finalmente, se ha diseñado un sistema probabilístico para seguir la posición del dispositivo en movimiento utilizando un filtro Bayesiano. Este sistema se ha probado en un entorno real, con varias plantas, obteniendo un error medio total por debajo de los 3 metros

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored

An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System

Pedestrian dead reckoning (PDR), enabled by smartphones’ embedded inertial sensors, is widely applied as a type of indoor positioning system (IPS). However, traditional PDR faces two challenges to improve its accuracy: lack of robustness for different PDR-related human activities and positioning error accumulation over elapsed time. To cope with these issues, we propose a novel adaptive human activity-aided PDR (HAA-PDR) IPS that consists of two main parts, human activity recognition (HAR) and PDR optimization. (1) For HAR, eight different locomotion-related activities are divided into two classes: steady-heading activities (ascending/descending stairs, stationary, normal walking, stationary stepping, and lateral walking) and non-steady-heading activities (door opening and turning). A hierarchical combination of a support vector machine (SVM) and decision tree (DT) is used to recognize steady-heading activities. An autoencoder-based deep neural network (DNN) and a heading range-based method to recognize door opening and turning, respectively. The overall HAR accuracy is over 98.44%. (2) For optimization methods, a process automatically sets the parameters of the PDR differently for different activities to enhance step counting and step length estimation. Furthermore, a method of trajectory optimization mitigates PDR error accumulation utilizing the non-steady-heading activities. We divided the trajectory into small segments and reconstructed it after targeted optimization of each segment. Our method does not use any a priori knowledge of the building layout, plan, or map. Finally, the mean positioning error of our HAA-PDR in a multilevel building is 1.79 m, which is a significant improvement in accuracy compared with a baseline state-of-the-art PDR system

Posicionamento cooperativo para redes sem fios heterogéneas

Doutoramento em Engenharia ElectrotécnicaFuture emerging market trends head towards positioning based services placing a new perspective on the way we obtain and exploit positioning information. On one hand, innovations in information technology and wireless communication systems enabled the development of numerous location based applications such as vehicle navigation and tracking, sensor networks applications, home automation, asset management, security and context aware location services. On the other hand, wireless networks themselves may bene t from localization information to improve the performances of di erent network layers. Location based routing, synchronization, interference cancellation are prime examples of applications where location information can be useful. Typical positioning solutions rely on measurements and exploitation of distance dependent signal metrics, such as the received signal strength, time of arrival or angle of arrival. They are cheaper and easier to implement than the dedicated positioning systems based on ngerprinting, but at the cost of accuracy. Therefore intelligent localization algorithms and signal processing techniques have to be applied to mitigate the lack of accuracy in distance estimates. Cooperation between nodes is used in cases where conventional positioning techniques do not perform well due to lack of existing infrastructure, or obstructed indoor environment. The objective is to concentrate on hybrid architecture where some nodes have points of attachment to an infrastructure, and simultaneously are interconnected via short-range ad hoc links. The availability of more capable handsets enables more innovative scenarios that take advantage of multiple radio access networks as well as peer-to-peer links for positioning. Link selection is used to optimize the tradeo between the power consumption of participating nodes and the quality of target localization. The Geometric Dilution of Precision and the Cramer-Rao Lower Bound can be used as criteria for choosing the appropriate set of anchor nodes and corresponding measurements before attempting location estimation itself. This work analyzes the existing solutions for node selection in order to improve localization performance, and proposes a novel method based on utility functions. The proposed method is then extended to mobile and heterogeneous environments. Simulations have been carried out, as well as evaluation with real measurement data. In addition, some speci c cases have been considered, such as localization in ill-conditioned scenarios and the use of negative information. The proposed approaches have shown to enhance estimation accuracy, whilst signi cantly reducing complexity, power consumption and signalling overhead.As tendências nos mercados emergentes caminham na direção dos serviços baseados em posicionamento, criando uma nova perspectiva na forma como podemos obter e utilizar informação de posicionamento. Por um lado, as inovações em tecnologias da informação e sistemas de comunicação sem fios permitiram o desenvolvimento de inúmeras aplicações baseadas em localização, tais como a navegação e monitorização de veículo, aplicações de redes de sensores, domótica, gestão de ativos, segurança e serviços de localização sensíveis ao contexto. Por outro lado, as próprias redes sem fios podem beneficiar da informação de localização dos utilizadores de forma a melhorarem as performances de diferentes camadas de rede. Routing baseado em localização, sincronização e cancelamento de interferência são os exemplos mais representativos de áreas onde a informação de localização pode ser útil. Soluções de localização típicas dependem de medições e de aproveitamento de métricas de sinal dependentes da distância, tais como a potência do sinal recebido, o tempo ou ângulo de chegada. São mais baratos e fáceis de implementar do que sistemas de localização dedicados com base em fingerprinting, com a desvantagem da perda de precisão. Consequentemente, algoritmos inteligentes de localização e técnicas de processamento de sinal têm de ser aplicados para compensar a falta de precisão das estimativas de distância. A cooperação entre nodos é usada nos casos em que as técnicas convencionais de posicionamento não têm um bom desempenho devido à inexistência de infraestrutura adequada, ou a um ambiente interior com obstruções. O objetivo é ter uma arquitetura híbrida, onde alguns nós têm pontos de ligação a uma infraestrutura e simultaneamente estão interligados através ligações ad-hoc de curto alcance. A disponibilidade de equipamentos mais capazes permite cenários mais inovadores que tiram proveito de múltiplas redes de acesso de rádio, bem como ligações peer-to-peer, para o posicionamento. A seleção de ligações é usada para otimizar o equilíbrio entre o consumo de energia dos nós participantes e da qualidade da localização do alvo. A diluição geométrica de precisão e a Cramér Rao Lower Bound podem ser utilizadas como critrio para a escolha do conjunto adequado de nodos de ancoragem e as medições correspondentes antes de realizar a tarefa de estimativa de localizaçãoo. Este trabalho analisa as soluções existentes para a seleção de nós, a fim de melhorar o desempenho de localização e propõe um novo método baseado em funções de utilidade. O método proposto é então estendido para ambientes móveis e heterogéneos. Foram realizadas simulações bem como avaliação de dados de medições reais. Além disso, alguns casos específicos foram considerados, tais como a localização em cenários mal-acondicionados e uso de informação negativa. As abordagens propostas revelaram uma melhoria na precisão da estimação, ao mesmo tempo que reduziram significativamente a complexidade do cálculo, o consumo de energia e o overhead do sinal

A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives

Efficient localisation plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAVs), which contributes to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities to enhance the localisation of UAVs and UGVs. In this paper, we review radio frequency (RF)-based approaches to localisation. We review the RF features that can be utilized for localisation and investigate the current methods suitable for Unmanned Vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localisation for both UAVs and UGVs is examined, and the envisioned 5G NR for localisation enhancement, and the future research direction are explored