A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning

In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor en-vironments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Never-theless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE- RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the stand-alone lateration method in terms of positioning accuracy.acceptedVersionPeer reviewe

Low-cost indoor localization system combining multilateration and Kalman filter

Indoor localization systems play an important role to track objects during their life-cycle in indoor environments, 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, range of coverage, energy consumption and cost. This paper describes a practical implementation of a BLE (Bluetooth Low Energy) based localization system that combines multilateration and Kalman filter techniques to achieve a low cost solution, maintaining a good position accuracy. The proposed approach was experimentally tested in an indoor environment, with the achieved results showing a clear low cost system presenting an increase of the estimated position accuracy by 10% for an average error of 2.33 metersThis work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope UIDB/05757/2020.info:eu-repo/semantics/publishedVersio

Collaborative Techniques for Indoor Positioning Systems

The demand for Indoor Positioning Systems (IPSs) developed specifically for mobile and wearable devices is continuously growing as a consequence of the expansion of the global market of Location-based Services (LBS), increasing adoption of mobile LBS applications, and ubiquity of mobile/wearable devices in our daily life. Nevertheless, the design of mobile/wearable devices-based IPSs requires to fulfill additional design requirements, namely low power consumption, reuse of devices’ built-in technologies, and inexpensive and straightforward implementation. Within the available indoor positioning technologies, embedded in mobile/wearable devices, IEEE 802.11 Wireless LAN (Wi-Fi) and Bluetooth Low Energy (BLE) in combination with lateration and fingerprinting have received extensive attention from research communities to meet the requirements. Although these technologies are straightforward to implement in positioning approaches based on Received Signal Strength Indicator (RSSI), the positioning accuracy decreases mainly due to propagation signal fluctuations in Line-of-sight (LOS) and Non-line-of-sight (NLOS), and the heterogeneity of the devices’ hardware. Therefore, providing a solution to achieve the target accuracy within the given constraints remains an open issue. The motivation behind this doctoral thesis is to address the limitations of traditional IPSs for human positioning based on RSSI, which suffer from low accuracy due to signal fluctuations and hardware heterogeneity, and deployment cost constraints, considering the advantages provided by the ubiquity of mobile devices and collaborative and machine learning-based techniques. Therefore, the research undertaken in this doctoral thesis focuses on developing and evaluating mobile device-based collaborative indoor techniques, using Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs), for human positioning to enhance the position accuracy of traditional indoor positioning systems based on RSSI (i.e., lateration and fingerprinting) in real-world conditions. The methodology followed during the research consists of four phases. In the first phase, a comprehensive systematic review of Collaborative Indoor Positioning Systems (CIPSs) was conducted to identify the key design aspects and evaluations used in/for CIPSs and the main concerns, limitations, and gaps reported in the literature. In the second phase, extensive experimental data collections using mobile devices and considering collaborative scenarios were performed. The data collected was used to create a mobile device-based BLE database for testing ranging collaborative indoor positioning approaches, and BLE and Wi-Fi radio maps to estimate devices’ position in the non-collaborative phase. Moreover, a detailed description of the methodology used for collecting and processing data and creating the database, as well as its structure, was provided to guarantee the reproducibility, use, and expansion of the database. In the third phase, the traditional methods to estimate distance (i.e., based on Logarithmic Distance Path Loss (LDPL) and fuzzy logic) and position (i.e., RSSI-lateration and fingerprinting–9-Nearest Neighbors (9-NN)) were described and evaluated in order to present their limitations and challenges. Also, two novel approaches to improve distance and positioning accuracy were proposed. In the last phase, our two proposed variants of collaborative indoor positioning system using MLP ANNs were developed to enhance the accuracy of the traditional indoor positioning approaches (BLE–RSSI lateration-based and fingerprinting) and evaluated them under real-world conditions to demonstrate their feasibility and benefits, and to present their limitations and future research avenues. The findings obtained in each of the aforementioned research phases correspond to the main contributions of this doctoral thesis. Specifically, the results of evaluating our CIPSs demonstrated that the first proposed variant of mobile device-based CIPS outperforms the positioning accuracy of the traditional lateration-based IPSs. Considering the distances among collaborating devices, our CIPS significantly outperforms the lateration baseline in short distances (≤ 4m), medium distances (>4m and ≤ 8m), and large distances (> 8m) with a maximum error reduction of 49.15 %, 19.24 %, and 21.48 % for the “median” metric, respectively. Regarding the second variant, the results demonstrated that for short distances between collaborating devices, our collaborative approach outperforms the traditional IPSs based on BLE–fingerprinting and Wi-Fi–fingerprinting with a maximum error reduction of 23.41% and 19.49% for the “75th percentile” and “90th percentile” metric, respectively. For medium distances, our proposed approach outperforms the traditional IPSs based on BLE–fingerprinting in the first 60% and after the 90% of cases in the Empirical Cumulative Distribution Function (ECDF) and only partially (20% of cases in the ECDF) the traditional IPSs based on Wi-Fi–fingerprinting. For larger distances, the performance of our proposed approach is worse than the traditional IPSs based on fingerprinting. Overall, the results demonstrate the usefulness and usability of our CIPSs to improve the positioning accuracy of traditional IPSs, namely IPSs based on BLE– lateration, BLE–fingerprinting, and Wi-Fi–fingerprinting under specific conditions. Mainly, conditions where the collaborative devices have short and medium distances between them. Moreover, the integration of MLP ANNs model in CIPSs allows us to use our approach under different scenarios and technologies, showing its level of generalizability, usefulness, and feasibility.Cotutelle-yhteistyöväitöskirja

Collaborative Indoor Positioning Systems: A Systematic Review

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified

A bluetooth low energy indoor positioning system with channel diversity, weighted trilateration and Kalman filtering

Indoor Positioning Systems (IPS) using Bluetooth Low Energy (BLE) technology are currently becoming real and available, which has made them grow in popularity and use. However, there are still plenty of challenges related to this technology, especially in terms of Received Signal Strength Indicator (RSSI) fluctuations due to the behaviour of the channels and the multipath effect, that lead to poor precision. In order to mitigate these effects, in this paper we propose and implement a real Indoor Positioning System based on Bluetooth Low Energy, that improves accuracy while reducing power consumption and costs. The three main proposals are: frequency diversity, Kalman filtering and a trilateration method what we have denominated “weighted trilateration”. The analysis of the results proves that all the proposals improve the precision of the system, which goes up to 1.82 m 90% of the time for a device moving in a middle-size room and 0.7 m for static devices. Furthermore, we have proved that the system is scalable and efficient in terms of cost and power consumption. The implemented approach allows using a very simple device (like a SensorTag) on the items to locate. The system enables a very low density of anchor points or references and with a precision better than existing solutionsPeer ReviewedPostprint (published version

Neuro-Dominating Set Scheme for a Fast and Efficient Robot Deployment in Internet of Robotic Things

International audienceInternet of Robotic Things (IoRT) is a new concept introduced for the first time by ABI Research. Unlike the Internet of Things (IoT), IoRT provides an active sensorization and is considered as the new evolution of IoT. In this context, we propose a Neuro-Dominating Set algorithm (NDS) to efficiently deploy a team of mobile wireless robots in an IoRT scenario, in order to reach a desired inter-robot distance, while maintaining global connectivity in the whole network. We use the term Neuro-Dominating Set to describe our approach, since it is inspired by both neural network and dominating set principles. With NDS algorithm, a robot adopts different behaviors according whether it is a dominating or a dominated robot. Our main goal is to show and demonstrate the beneficial effect of using different behaviors in the IoRT concept. The obtained results show that the proposed method outperforms an existing related technique (i.e., the Virtual Angular Force approach) and the neural network based approach presented in our previous work. As an objective, we aim to decrease the overall traveled distance and keep a low energy consumption level, while maintaining network connectivity and an acceptable convergence time

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

Visible Light Positioning using Received Signal Strength for Industrial Environments

There is a forecast for exceptional digital data traffic growth due to the digitisation of industrial applications using the internet of things. As a result, a great need for high bandwidth and faster transmission data rates for future wireless networks has emerged. One of the considered communication technologies that can assist in satisfying this demand is visible light communications (VLC). VLC is an emerging technology that uses the visible light spectrum by mainly utilising lightemitting diodes (LEDs) for simultaneous indoor lighting and high bandwidth wireless communication. Some of the applications of VLC are to provide high data rate internet in homes, offices, campuses, hospitals, and several other areas. One of these promising areas of application is for industrial wireless communications. The research project will provide a review of VLC applications intended for industrial applications with an emphasis on visible light positioning (VLP). In this research work, a three-dimensional (3D) positioning algorithm for calculating the location of a photodiode (PD) is presented. It solely works on measured powers from different LED sources and does not require any prior knowledge of the receiver’s height unlike other works in the literature. The performance of the proposed VLP algorithm in terms of positioning error is evaluated using two different trilateration algorithms, the Cayley–Menger determinant (CMD) and the Linear Least Squares (LLS) trilateration algorithms. The evaluation considers different scenarios, with and without receiver tilt, and with multipath reflections. Simulation results show that the CMD algorithm is more accurate and outperforms the LLS trilateration positioning algorithm. Furthermore, the proposed method has been experimentally assessed under two different LED configurations, with different degrees of receiver tilt, and in the presence of a fully stocked storage rack to examine the effect of multipath reflections on the performance of VLP systems. It was observed from simulations and experimental investigations that the widely used square LED-configuration results in position ambiguities for 3D systems while a non-lattice layout, such as a star-shaped configuration, is much more accurate. An experimental accuracy with a 3D median error of 10.5 cm was achieved using the CMD algorithm in a 4 m × 4 m × 4.1 m area with a horizontal receiver. Adding receiver tilt of 5◦ and 10◦ increases the median error by an average of 29% and 110%, respectively. The effect of reflections from the i storage rack has also been thoroughly examined using the two mentioned trilateration algorithms and showed to increase the 3D median positioning error by an average of 69% in the experimental testbed for the areas close to the storage rack. These results highlight the degrading effect of multipath reflections on VLP systems and the necessity to consider it when evaluating these systems. As the primary consideration for positioning systems in industrial environments is for mobile robots, the encouraging results in this thesis can be further improved though the use of a sensor fusion method