A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

Towards Authentication of IoMT Devices via RF Signal Classification

The increasing reliance on the Internet of Medical Things (IoMT) raises great concern in terms of cybersecurity, either at the device’s physical level or at the communication and transmission level. This is particularly important as these systems process very sensitive and private data, including personal health data from multiple patients such as real-time body measurements. Due to these concerns, cybersecurity mechanisms and strategies must be in place to protect these medical systems, defending them from compromising cyberattacks. Authentication is an essential cybersecurity technique for trustworthy IoMT communications. However, current authentication methods rely on upper-layer identity verification or key-based cryptography which can be inadequate to the heterogeneous Internet of Things (IoT) environments. This thesis proposes the development of a Machine Learning (ML) method that serves as a foundation for Radio Frequency Fingerprinting (RFF) in the authentication of IoMT devices in medical applications to improve the flexibility of such mechanisms. This technique allows the authentication of medical devices by their physical layer characteristics, i.e. of their emitted signal. The development of ML models serves as the foundation for RFF, allowing it to evaluate and categorise the released signal and enable RFF authentication. Multiple feature take part of the proposed decision making process of classifying the device, which then is implemented in a medical gateway, resulting in a novel IoMT technology.A confiança crescente na IoMT suscita grande preocupação em termos de cibersegurança, quer ao nível físico do dispositivo quer ao nível da comunicação e ao nível de transmissão. Isto é particularmente importante, uma vez que estes sistemas processam dados muito sensíveis e dados, incluindo dados pessoais de saúde de diversos pacientes, tais como dados em tempo real de medidas do corpo. Devido a estas preocupações, os mecanismos e estratégias de ciber-segurança devem estar em vigor para proteger estes sistemas médicos, defendendo-os de ciberataques comprometedores. A autenticação é uma técnica essencial de ciber-segurança para garantir as comunicações em sistemas IoMT de confiança. No entanto, os métodos de autenticação atuais focam-se na verificação de identidade na camada superior ou criptografia baseada em chaves que podem ser inadequadas para a ambientes IoMT heterogéneos. Esta tese propõe o desenvolvimento de um método de ML que serve como base para o RFF na autenticação de dispositivos IoMT para melhorar a flexibilidade de tais mecanismos. Isto permite a autenticação dos dispositivos médicos pelas suas características de camada física, ou seja, a partir do seu sinal emitido. O desenvolvimento de modelos de ML serve de base para o RFF, permitindo-lhe avaliar e categorizar o sinal libertado e permitir a autenticação do RFF. Múltiplas features fazem parte do processo de tomada de decisão proposto para classificar o dispositivo, que é implementada num gateway médico, resultando numa nova tecnologia IoMT

Multivariate Stochastic Approximation to Tune Neural Network Hyperparameters for Criticial Infrastructure Communication Device Identification

The e-government includes Wireless Personal Area Network (WPAN) enabled internet-to-government pathways. Of interest herein is Z-Wave, an insecure, low-power/cost WPAN technology increasingly used in critical infrastructure. Radio Frequency (RF) Fingerprinting can augment WPAN security by a biometric-like process that computes statistical features from signal responses to 1) develop an authorized device library, 2) develop classifier models and 3) vet claimed identities. For classification, the neural network-based Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is employed. GRLVQI has shown high fidelity in classifying Z-Wave RF Fingerprints; however, GRLVQI has multiple hyperparameters. Prior work optimized GRLVQI via a full factorial experimental design. Herein, optimizing GRLVQI via stochastic approximation, which operates by iterative searching for optimality, is of interest to provide an unconstrained optimization approach to avoid limitations found in full factorial experimental designs. The results provide an improvement in GRLVQI operation and accuracy. The methodology is further generalizable to other problems and algorithms

Wi-Fi For Indoor Device Free Passive Localization (DfPL): An Overview

The world is moving towards an interconnected and intercommunicable network of animate and inanimate objects with the emergence of Internet of Things (IoT) concept which is expected to have 50 billion connected devices by 2020. The wireless communication enabled devices play a major role in the realization of IoT. In Malaysia, home and business Internet Service Providers (ISP) bundle Wi-Fi modems working in 2.4 GHz Industrial, Scientific and Medical (ISM) radio band with their internet services. This makes Wi-Fi the most eligible protocol to serve as a local as well as internet data link for the IoT devices. Besides serving as a data link, human entity presence and location information in a multipath rich indoor environment can be harvested by monitoring and processing the changes in the Wi-Fi Radio Frequency (RF) signals. This paper comprehensively discusses the initiation and evolution of Wi-Fi based Indoor Device free Passive Localization (DfPL) since the concept was first introduced by Youssef et al. in 2007. Alongside the overview, future directions of DfPL in line with ongoing evolution of Wi-Fi based IoT devices are briefly discussed in this paper

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

Privacy in Indoor Positioning Systems: A Systematic Review

Ponència presentada a 10th International Conference on Localization and GNSS (ICL-GNSS), celebrada a Tampere (Finland) del 2 al 4 de juny de 2020This article presents a systematic review of privacy in indoor positioning systems. The selected 41 articles on location privacy preserving mechanisms employ non-inherently private methods such as encryption, k-anonymity, and differential privacy. The 15 identified mechanisms are categorized and summarized by where they are processed: on device, during transmission, or at a server. Trade-offs such as calculation speed, granularity, or complexity in set-up are identified for each mechanism. In 40% of the papers, some trade-offs are minimized by combining several methods into a hybrid solution. The combinations of mechanisms and their levels of offered privacy are suggested based on a series of user mobility cases

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future