iBeacon-based indoor positioning system: from theory to practical deployment

Developing an indoor positioning system became essential when global positioning system signals could not work well in indoor environments. Mobile positioning can be accomplished via many radio frequency technology such as Bluetooth low energy (BLE), wireless fidelity (Wi-Fi), ultra-wideband (UWB), and so on. With the pressing need for indoor positioning systems, we, in this work, present a deployment scheme for smartphone using Bluetooth iBeacons. Three main parts, hardware deployment, software deployment, and positioning accuracy assessment, are discussed carefully to find the optimal solution for a complete indoor positioning system. Our application and experimental results show that proposed solution is feasible and indoor positioning system is completely attainable

Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments

The technologies and sensors embedded in smartphones have contributed to the spread of disruptive applications built on top of Location Based Services (LBSs). Among them, Bluetooth Low Energy (BLE) has been widely adopted for proximity and localization, as it is a simple but efficient positioning technology. This article presents a database of received signal strength measurements (RSSIs) on BLE signals in a real positioning system. The system was deployed on two buildings belonging to the campus of the University of Extremadura in Badajoz. the database is divided into three different deployments, changing in each of them the number of measurement points and the configuration of the BLE beacons. the beacons used in this work can broadcast up to six emission slots simultaneously. Fingerprinting positioning experiments are presented in this work using multiple slots, improving positioning accuracy when compared with the traditional single slot approach

LOCATION-BASED MARKETING: CONCEPTS, TECHNOLOGIES AND SERVICES

siirretty Doriast

Practical implementation of a hybrid indoor localization system

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIndoor localization systems occupy a significant role to track objects during their life cycle, e.g., related to retail, logistics and mobile robotics. These positioning systems use several techniques and technologies to estimate the position of each object, and face several requirements such as position accuracy, security, coverage range, energy consumption and cost. This master thesis describes a real-world scenario implementation, based on Bluetooth Low Energy (BLE) beacons, evaluating a Hybrid Indoor Positioning System (H-IPS) that combines two RSSI-based approaches: Multilateration (MLT) and Fingerprinting (FP). The objective is to track a target node, assuming that the object follows a linear motion model. It was employed Kalman Filter (KF) to decrease the positioning errors of the MLT and FP techniques. Furthermore a Track-to-Track Fusion (TTF) is performed on the two KF outputs in order to maximize the performance. The results show that the accuracy of H-IPS overcomes the standalone FP in 21%, while the original MLT is outperformed in 52%. Finally, the proposed solution demonstrated a probability of error < 2 m of 80%, while the same probability for the FP and MLT are 56% and 20%, respectively.Os sistemas de localização de ambientes internos desempenham um papel importante na localização de objectos durante o seu ciclo de vida, como por exemplo os relacionados com o varejo, a logística e a robótica móvel. Estes sistemas de localização utilizam várias técnicas e tecnologias para estimar a posição de cada objecto, e possuem alguns critérios tais como precisão, segurança, alcance, consumo de energia e custo. Esta dissertação de mestrado descreve uma implementação num cenário real, baseada em Bluetooth Low Energy (BLE) beacons, avaliando um Sistema Híbrido de Posicionamento para Ambientes Internos (H-IPS, do inglês Hybrid Indoor Positioning System) que combina duas abordagens baseadas no Indicador de Intensidade do Sinal Recebido (RSSI, do inglês Received Signal Strength Indicator): Multilateração (MLT) e Fingerprinting (FP). O objectivo é localizar um nó alvo, assumindo que o objecto segue um modelo de movimento linear. Foi utilizado Filtro de Kalman (FK) para diminuir os erros de posicionamento do MLT e FP, além de aplicar uma fusão de vetores de estado nas duas saídas FK, a fim de maximizar o desempenho. Os resultados mostram que a precisão do H-IPS supera o FP original em 21%, enquanto que o MLT original tem um desempenho superior a 52%. Finalmente, a solução proposta apresentou uma probabilidade de erro de < 2 m de 80%, enquanto a mesma probabilidade para FP e MLT foi de 56% e 20%, respectivamente

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

SomBe:Self-Organizing Map for Unstructured and Non-Coordinated iBeacon Constellations

Bluetooth Low Energy (BLE) devices such as iBeacons have been popularly deployed for Location Based Services (LBS), including indoor infrastructure monitoring, positioning, and navigation. In these applications, the positions of iBeacons are assumed to be known. However, the location information is often unavailable or inaccurate as most iBeacons were deployed by different external parties. In addition, manual localizing the already-deployed iBeacons is costly and even impractical, especially in large-scale and complex indoor environments. Therefore, we propose a novel method, namely SomeBe, which can localize deployed iBeacons with a minimal effort and invasiveness to existing infrastructures. Specifically, our approach uses cooperative multilateration based on Received Signal Strength (RSS) of available smartphones and WiFi access points (APs) in the environment. Both Bluetooth signal strengths (between smartphones and iBeacons) and WiFi signal strengths (between smartphones and APs) are jointly employed in a single optimization cost function to surpass the local minima. Requiring that the positions of the APs are known only, the proposed cost function can also localize the iBeacons without knowing the positions of smartphones. To improve the localization accuracy, we employ a clustering method based on the RSS values for the coarse estimation of iBeacons' positions. SomBe also can be used to simplify iBeacon deployment as it can localize the iBeacons with a minimal effort. The performance evaluation results of our testbed experiments as well as realistic simulations show that SomBe outperforms non-cooperative approaches with 85% better in terms of accuracy

Wi-Fi Location Determination for Semantic Locations

In Wi-Fi location determination literature, little attention is paid to locations that do not have numeric, geometric coordinates, though many users prefer the convenience of non-coordinate locations (consider the ease of giving a street address as opposed to giving latitude and longitude). It is not often easy to tell from the title or abstract of a Wi-Fi location determination article whether or not it has applicability to semantic locations such as room-level names. This article surveys the literature through 2011 on Wi-Fi localization for symbolic locations