Ibeacon based proximity and indoor localization system

User location can be leveraged to provide a wide range of services in a variety of indoor locations including retails stores, hospitals, airports, museums and libraries etc. The widescale proliferation of user devices such as smart phones and the interconnectivity among different entities, powered by Internet of Things (IoT), makes user device-based localization a viable approach to provide Location Based Services (LBS). Location based services can be broadly classified into 1) Proximity based services that provides services based on a rough estimate of users distance to any entity, and 2) Indoor localization that locates a user\u27s exact location in the indoor environment rather than a rough estimate of the distance. The primary requirements of these services are higher energy efficiency, localization accuracy, wide reception range, low cost and availability. Technologies such as WiFi, Radio Frequency Identification (RFID) and Ultra Wideband (UWB) have been used to provide both indoor localization and proximity based services. Since these technologies are not primarily intended for LBS, they do not fulfill the aforementioned requirements. Bluetooth Low Energy (BLE) enabled beacons that use Apple\u27s proprietary iBeacon protocol are mainly intended to provide proximity based services. iBeacons satisfy the energy efficiency, wide reception range and availability requirements of LBS. However, iBeacons are prone to noise due to their reliance on Received Signal Strength Indicator (RSSI), which drastically fluctuates in indoor environments due to interference from different obstructions. This limits its proximity detection accuracy. In this thesis, we present an iBeacon based proximity and indoor localization system. We present our two server-based algorithms to improve the proximity detection accuracy by reducing the variation in the RSSI and using the RSSI-estimated distance, rather than the RSSI itself, for proximity classification. Our algorithms Server-side Running Average and Server-side Kalman Filter improves the proximity detection accuracy by 29% and 32% respectively in contrast to Apple\u27s current approach of using moving average of RSSI values for proximity classification. We utilize a server-based approach because of the greater computing power of servers. Furthermore, server-based approach helps reduce the energy consumption of user device. We describe our cloud based architecture for iBeacon based proximity detection. We also use iBeacons for indoor localization. iBeacons are not primarily intended for indoor localization as their reliance on RSSI makes them unsuitable for accurate indoor localization. To improve the localization accuracy, we use Bayesian filtering algorithms such as Particle Filter (PF), Kalman Filter (KF), and Extended Kalman Filter (EKF). We show that by cascading Kalman Filter and Extended Kalman Filter with Particle Filter, the indoor localization accuracy can be improved by 28% and 33.94% respectively when compared with only using PF. The PF, KFPF and PFEKF algorithm on the server side have average localization error of 1.441 meters, 1.0351 meters and 0.9519 meters respectively

Accurate Range-based Indoor Localization Using PSO-Kalman Filter Fusion

Accurate indoor localization often depends on infrastructure support for distance estimation in range-based techniques. One can also trade off accuracy to reduce infrastructure investment by using relative positions of other nodes, as in range-free localization. Even for range-based methods where accurate Ultra-WideBand (UWB) signals are used, non line-of-sight (NLOS) conditions pose significant difficulty in accurate indoor localization. Existing solutions rely on additional measurements from sensors and typically correct the noise using a Kalman filter (KF). Solutions can also be customized to specific environments through extensive profiling. In this work, a range-based indoor localization algorithm called PSO - Kalman Filter Fusion (PKFF) is proposed that minimizes the effects of NLOS on localization error without using additional sensors or profiling. Location estimates from a windowed Particle Swarm Optimization (PSO) and a dynamically adjusted KF are fused based on a weighted variance factor. PKFF achieved a 40% lower 90-percentile root-mean-square localization error (RMSE) over the standard least squares trilateration algorithm at 61 cm compared to 102 cm

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

Self-healing radio maps of wireless networks for indoor positioning

Programa Doutoral em Telecomunicações MAP-tele das Universidades do Minho, Aveiro e PortoA Indústria 4.0 está a impulsionar a mudança para novas formas de produção e otimização em tempo real nos espaços industriais que beneficiam das capacidades da Internet of Things (IoT) nomeadamente, a localização de veículos para monitorização e optimização de processos. Normalmente os espaços industriais possuem uma infraestrutura Wi-Fi que pode ser usada para localizar pessoas, bens ou veículos, sendo uma oportunidade para aumentar a produtividade. Os mapas de rádio são importantes para os sistemas de posicionamento baseados em Wi-Fi, porque representam o ambiente de rádio e são usados para estimar uma posição. Os mapas de rádio são constituídos por amostras Wi-Fi recolhidas em posições conhecidas e degradam-se ao longo do tempo devido a vários fatores, por exemplo, efeitos de propagação, adição/remoção de APs, entre outros. O processo de construção do mapa de rádio costuma ser exigente em termos de tempo e recursos humanos, constituindo um desafio considerável. Os veículos, que operam em ambientes industriais podem ser explorados para auxiliar na construção de mapas de rádio, desde que seja possível localizá-los e rastreá-los. O objetivo principal desta tese é desenvolver um sistema de posicionamento para veículos industriais com mapas de rádio auto-regenerativos (capaz de manter os mapas de rádio atualizados). Os veículos são localizados através da fusão sensorial de Wi-Fi com sensores de movimento, que permitem anotar novas amostras Wi-Fi para o mapa de rádio auto-regenerativo. São propostas duas abordagens de fusão sensorial, baseadas em Loose Coupling e Tight Coupling, para a localização dos veículos. A abordagem Tight Coupling inclui uma métrica de confiança para determinar quando é que as amostras de Wi-Fi devem ser anotadas. Deste modo, esta solução não requer calibração nem esforço humano para a construção e manutenção do mapa de rádio. Os resultados obtidos em experiências sugerem que esta solução tem potencial para a IoT e a Indústria 4.0, especialmente em serviços de localização, mas também na monitorização, suporte à navegação autónoma, e interconectividade.Industry 4.0 is driving change for new forms of production and real-time optimization in factories, which benefit from the Industrial Internet of Things (IoT) capabilities to locate industrial vehicles for monitoring, improving safety, and operations. Most industrial environments have a Wi-Fi infrastructure that can be exploited to locate people, assets, or vehicles, providing an opportunity for enhancing productivity and interconnectivity. Radio maps are important for Wi-Fi-based Indoor Position Systems (IPSs) since they represent the radio environment and are used to estimate a position. Radio maps comprise a set of Wi- Fi samples collected at known positions, and degrade over time due to several aspects, e.g., propagation effects, addition/removal of Access Points (APs), among others, hence they should be periodically updated to maintain the IPS performance. The process to build and maintain radio maps is usually time-consuming and demanding in terms of human resources, thus being challenging to perform. Vehicles, commonly present in industrial environments, can be explored to help build and maintain radio maps, as long as it is possible to locate and track them. The main objective of this thesis is to develop an IPS for industrial vehicles with self-healing radio maps (capable of keeping radio maps up to date). Vehicles are tracked using sensor fusion of Wi-Fi with motion sensors, which allows to annotate new Wi-Fi samples to build the self-healing radio maps. Two sensor fusion approaches based on Loose Coupling and Tight Coupling are proposed to track vehicles. The Tight Coupling approach includes a reliability metric to determine when Wi-Fi samples should be annotated. As a result, this solution does not depend on any calibration or human effort to build and maintain the radio map. Results obtained in real-world experiments suggest that this solution has potential for IoT and Industry 4.0, especially in location services, but also in monitoring and analytics, supporting autonomous navigation, and interconnectivity between devices.MAP-Tele Doctoral Programme scientific committee and the FCT (Fundação para a Ciência e Tecnologia) for the PhD grant (PD/BD/137401/2018

Enhanced Integrated Indoor Positioning Algorithm Utilising Wi-Fi Fingerprint Technique

This paper describes an integrated positioning algorithm utilizing Wi-Fi fingerprint technique for indoor positioning. The main contribution of this work is the improvement of positioning accuracy for indoor localization even in extreme RSSI fluctuation which leads to variation of positioning error. Several layers of Wi-Fi positioning is proposed, which are based on deterministic techniques, iterative Bayesian estimation, and also Kalman filter to enhance accuracy due to noise presence. Here, accumulated accuracy is introduced where the distribution of location error is determined by estimation at each test point on path movement. The results show that the integrated algorithm enhances the estimation accuracy in several scenarios which are different Wi-Fi chipsets and movement directions. The error distribution shows an achievement of up to 65% for error less than 5m compared to the basic deterministic technique of only 45%

L/F-CIPS: Collaborative indoor positioning for smartphones with lateration and fingerprinting

The demand for indoor location-based services and the wide availability of mobile devices have triggered research into new positioning systems able to provide accurate indoor positions using smartphones. However, accurate solutions require a complex implementation and long-term maintenance of their infrastructure. Collaborative systems may help to alleviate these drawbacks. In this paper, we propose a smartphone-based collaborative architecture using neural networks and received signal strength, which exploits the built-in wireless communication technologies in smartphones and the collaboration between devices to improve traditional positioning systems without additional deployment. Experiments are carried out in two real-world scenarios, demonstrating that our proposed architecture enhances the position accuracy of traditional indoor positioning systems.The authors gratefully acknowledge funding from European Union’s Horizon 2020 RIA programme under the Marie Skłodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints) and No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories). The associate editor coordinating the review of this article and approving it for publication was Prof. Name Surname (Corresponding authors: J. Torres-Sospedra and S. Casteleyn)

L/F-CIPS: collaborative indoor positioning for smartphones with lateration and fingerprinting

The demand for indoor location-based services (LBS) and the wide availability of mobile devices have triggered research into new positioning systems able to provide accurate indoor positions using smartphones. However, accurate solutions require a complex implementation and long-term maintenance of their infrastructure. Collaborative systems may help alleviate these drawbacks. In this article, we propose a smartphone-based collaborative architecture using neural networks and received signal strength (RSS), which exploits the built-in wireless communication technologies in smartphones and the collaboration between devices to improve the traditional positioning systems without additional deployment. Experiments are carried out in two real-world scenarios, demonstrating that our proposed architecture enhances the position accuracy of the traditional indoor positioning systems (IPSs)

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

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

Sensors Utilisation and Data Collection of Underground Mining

This study reviews IMU significance and performance for underground mine drone localisation. This research has designed a Kalman filter which extracts reliable information from raw data. Kalman filter for INS combines different measurements considering estimated errors to produce a trajectory including time, position and attitude. To evaluate the feasibility of the proposed method, a prototype has been designed and evaluated. Experimental results indicate that the designed Kalman filter estimates the internal states of a system