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

Actions speak louder than words: Semi-supervised learning for browser fingerprinting detection

As online tracking continues to grow, existing anti-tracking and fingerprinting detection techniques that require significant manual input must be augmented. Heuristic approaches to fingerprinting detection are precise but must be carefully curated. Supervised machine learning techniques proposed for detecting tracking require manually generated label-sets. Seeking to overcome these challenges, we present a semi-supervised machine learning approach for detecting fingerprinting scripts. Our approach is based on the core insight that fingerprinting scripts have similar patterns of API access when generating their fingerprints, even though their access patterns may not match exactly. Using this insight, we group scripts by their JavaScript (JS) execution traces and apply a semi-supervised approach to detect new fingerprinting scripts. We detail our methodology and demonstrate its ability to identify the majority of scripts ($\geqslant$94.9%) identified by existing heuristic techniques. We also show that the approach expands beyond detecting known scripts by surfacing candidate scripts that are likely to include fingerprinting. Through an analysis of these candidate scripts we discovered fingerprinting scripts that were missed by heuristics and for which there are no heuristics. In particular, we identified over one hundred device-class fingerprinting scripts present on hundreds of domains. To the best of our knowledge, this is the first time device-class fingerprinting has been measured in the wild. These successes illustrate the power of a sparse vector representation and semi-supervised learning to complement and extend existing tracking detection techniques

Graceful Degradation in IoT Security

As the consumer grade IoT devices industry advances, personal privacy is constantly eroded for the sake of convenience. Current security solutions, although available, ignore convenience by requiring the purchase of additional hardware, implementing confusing, out of scope updates for a non-technical user, or quarantining a device, rendering it useless. This paper proposes a solution that simultaneously maintains convenience and privacy, tailored for the Internet of Things. We propose a novel graceful degradation technique which targets individual device functionalities for acceptance or denial at the network level. When combined with current anomaly detection and fingerprinting methods, graceful degradation provides a personalized IoT security solution for the modern user

Characterizing Audio Events for Video Soundtrack Analysis

There is an entire emerging ecosystem of amateur video recordings on the internet today, in addition to the abundance of more professionally produced content. The ability to automatically scan and evaluate the content of these recordings would be very useful for search and indexing, especially as amateur content tends to be more poorly labeled and tagged than professional content. Although the visual content is often considered to be of primary importance, the audio modality contains rich information which may be very helpful in the context of video search and understanding. Any technology that could help to interpret video soundtrack data would also be applicable in a number of other scenarios, such as mobile device audio awareness, surveillance, and robotics. In this thesis we approach the problem of extracting information from these kinds of unconstrained audio recordings. Specifically we focus on techniques for characterizing discrete audio events within the soundtrack (e.g. a dog bark or door slam), since we expect events to be particularly informative about content. Our task is made more complicated by the extremely variable recording quality and noise present in this type of audio. Initially we explore the idea of using the matching pursuit algorithm to decompose and isolate components of audio events. Using these components we develop an approach for non-exact (approximate) fingerprinting as a way to search audio data for similar recurring events. We demonstrate a proof of concept for this idea. Subsequently we extend the use of matching pursuit to build an actual audio fingerprinting system, with the goal of identifying simultaneously recorded amateur videos (i.e. videos taken in the same place at the same time by different people, which contain overlapping audio). Automatic discovery of these simultaneous recordings is one particularly interesting facet of general video indexing. We evaluate this fingerprinting system on a database of 733 internet videos. Next we return to searching for features to directly characterize soundtrack events. We develop a system to detect transient sounds and represent audio clips as a histogram of the transients it contains. We use this representation for video classification over a database of 1873 internet videos. When we combine these features with a spectral feature baseline system we achieve a relative improvement of 7.5% in mean average precision over the baseline. In another attempt to devise features to better describe and compare events, we investigate decomposing audio using a convolutional form of non-negative matrix factorization, resulting in event-like spectro-temporal patches. We use the resulting representation to build an event detection system that is more robust to additive noise than a comparative baseline system. Lastly we investigate a promising feature representation that has been used by others previously to describe event-like sound effect clips. These features derive from an auditory model and are meant to capture fine time structure in sound events. We compare these features and a related but simpler feature set on the task of video classification over 9317 internet videos. We find that combinations of these features with baseline spectral features produce a significant improvement in mean average precision over the baseline

Framework for privacy-aware content distribution in peer-to- peer networks with copyright protection

The use of peer-to-peer (P2P) networks for multimedia distribution has spread out globally in recent years. This mass popularity is primarily driven by the efficient distribution of content, also giving rise to piracy and copyright infringement as well as privacy concerns. An end user (buyer) of a P2P content distribution system does not want to reveal his/her identity during a transaction with a content owner (merchant), whereas the merchant does not want the buyer to further redistribute the content illegally. Therefore, there is a strong need for content distribution mechanisms over P2P networks that do not pose security and privacy threats to copyright holders and end users, respectively. However, the current systems being developed to provide copyright and privacy protection to merchants and end users employ cryptographic mechanisms, which incur high computational and communication costs, making these systems impractical for the distribution of big files, such as music albums or movies.El uso de soluciones de igual a igual (peer-to-peer, P2P) para la distribución multimedia se ha extendido mundialmente en los últimos años. La amplia popularidad de este paradigma se debe, principalmente, a la distribución eficiente de los contenidos, pero también da lugar a la piratería, a la violación del copyright y a problemas de privacidad. Un usuario final (comprador) de un sistema de distribución de contenidos P2P no quiere revelar su identidad durante una transacción con un propietario de contenidos (comerciante), mientras que el comerciante no quiere que el comprador pueda redistribuir ilegalmente el contenido más adelante. Por lo tanto, existe una fuerte necesidad de mecanismos de distribución de contenidos por medio de redes P2P que no supongan un riesgo de seguridad y privacidad a los titulares de derechos y los usuarios finales, respectivamente. Sin embargo, los sistemas actuales que se desarrollan con el propósito de proteger el copyright y la privacidad de los comerciantes y los usuarios finales emplean mecanismos de cifrado que implican unas cargas computacionales y de comunicaciones muy elevadas que convierten a estos sistemas en poco prácticos para distribuir archivos de gran tamaño, tales como álbumes de música o películas.L'ús de solucions d'igual a igual (peer-to-peer, P2P) per a la distribució multimèdia s'ha estès mundialment els darrers anys. L'àmplia popularitat d'aquest paradigma es deu, principalment, a la distribució eficient dels continguts, però també dóna lloc a la pirateria, a la violació del copyright i a problemes de privadesa. Un usuari final (comprador) d'un sistema de distribució de continguts P2P no vol revelar la seva identitat durant una transacció amb un propietari de continguts (comerciant), mentre que el comerciant no vol que el comprador pugui redistribuir il·legalment el contingut més endavant. Per tant, hi ha una gran necessitat de mecanismes de distribució de continguts per mitjà de xarxes P2P que no comportin un risc de seguretat i privadesa als titulars de drets i els usuaris finals, respectivament. Tanmateix, els sistemes actuals que es desenvolupen amb el propòsit de protegir el copyright i la privadesa dels comerciants i els usuaris finals fan servir mecanismes d'encriptació que impliquen unes càrregues computacionals i de comunicacions molt elevades que fan aquests sistemes poc pràctics per a distribuir arxius de grans dimensions, com ara àlbums de música o pel·lícules

Literary review of content-based music recognition paradigms

During the last few decades, a need for novel retrieval strategies for large audio databases emerged as millions of digital audio documents became accessible for everyone through the Internet. It became essential that the users could search for songs that they had no prior information about using only the content of the audio as a query. In practice this means that when a user hears an unknown song coming out of the radio and wants to get more information about it, he or she can simply record a sample of the song with a mobile device and send it to a music recognition application as a query. Query results would then be presented on the screen with all the necessary meta data, such as the song name and artist. The retrieval systems are expected to perform quickly and accurately against large databases that may contain millions of songs, which poses lots of challenges for the researchers. This thesis is a literature review which will go through some audio retrieval paradigms that allow querying for songs using only their audio content, such as audio fingerprinting. It will also address the typical problems and challenges of audio retrieval and compare how each of these proposed paradigms performs in these challenging scenarios

Signal fingerprinting and machine learning framework for UAV detection and identification.

Advancement in technology has led to creative and innovative inventions. One such invention includes unmanned aerial vehicles (UAVs). UAVs (also known as drones) are now an intrinsic part of our society because their application is becoming ubiquitous in every industry ranging from transportation and logistics to environmental monitoring among others. With the numerous benign applications of UAVs, their emergence has added a new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a UAV. For this reason, nefarious actors can take advantage of these aircraft to intrude into restricted or private areas. A UAV detection and identification system is one of the ways of detecting and identifying the presence of a UAV in an area. UAV detection and identification systems employ different sensing techniques such as radio frequency (RF) signals, video, sounds, and thermal imaging for detecting an intruding UAV. Because of the passive nature (stealth) of RF sensing techniques, the ability to exploit RF sensing for identification of UAV flight mode (i.e., flying, hovering, videoing, etc.), and the capability to detect a UAV at beyond visual line-of-sight (BVLOS) or marginal line-of-sight makes RF sensing techniques promising for UAV detection and identification. More so, there is constant communication between a UAV and its ground station (i.e., flight controller). The RF signals emitting from a UAV or UAV flight controller can be exploited for UAV detection and identification. Hence, in this work, an RF-based UAV detection and identification system is proposed and investigated. In RF signal fingerprinting research, the transient and steady state of the RF signals can be used to extract a unique signature. The first part of this work is to use two different wavelet analytic transforms (i.e., continuous wavelet transform and wavelet scattering transform) to investigate and analyze the characteristics or impacts of using either state for UAV detection and identification. Coefficient-based and image-based signatures are proposed for each of the wavelet analysis transforms to detect and identify a UAV. One of the challenges of using RF sensing is that a UAV\u27s communication links operate at the industrial, scientific, and medical (ISM) band. Several devices such as Bluetooth and WiFi operate at the ISM band as well, so discriminating UAVs from other ISM devices is not a trivial task. A semi-supervised anomaly detection approach is explored and proposed in this research to differentiate UAVs from Bluetooth and WiFi devices. Both time-frequency analytical approaches and unsupervised deep neural network techniques (i.e., denoising autoencoder) are used differently for feature extraction. Finally, a hierarchical classification framework for UAV identification is proposed for the identification of the type of unmanned aerial system signal (UAV or UAV controller signal), the UAV model, and the operational mode of the UAV. This is a shift from a flat classification approach. The hierarchical learning approach provides a level-by-level classification that can be useful for identifying an intruding UAV. The proposed frameworks described here can be extended to the detection of rogue RF devices in an environment

Data analytics for mobile traffic in 5G networks using machine learning techniques

This thesis collects the research works I pursued as Ph.D. candidate at the Universitat Politecnica de Catalunya (UPC). Most of the work has been accomplished at the Mobile Network Department Centre Tecnologic de Telecomunicacions de Catalunya (CTTC). The main topic of my research is the study of mobile network traffic through the analysis of operative networks dataset using machine learning techniques. Understanding first the actual network deployments is fundamental for next-generation network (5G) for improving the performance and Quality of Service (QoS) of the users. The work starts from the collection of a novel type of dataset, using an over-the-air monitoring tool, that allows to extract the control information from the radio-link channel, without harming the users’ identities. The subsequent analysis comprehends a statistical characterization of the traffic and the derivation of prediction models for the network traffic. A wide group of algorithms are implemented and compared, in order to identify the highest performances. Moreover, the thesis addresses a set of applications in the context mobile networks that are prerogatives in the future mobile networks. This includes the detection of urban anomalies, the user classification based on the demanded network services, the design of a proactive wake-up scheme for efficient-energy devices.Esta tesis recoge los trabajos de investigación que realicé como Ph.D. candidato a la Universitat Politecnica de Catalunya (UPC). La mayor parte del trabajo se ha realizado en el Centro Tecnológico de Telecomunicaciones de Catalunya (CTTC) del Departamento de Redes Móviles. El tema principal de mi investigación es el estudio del tráfico de la red móvil a través del análisis del conjunto de datos de redes operativas utilizando técnicas de aprendizaje automático. Comprender primero las implementaciones de red reales es fundamental para la red de próxima generación (5G) para mejorar el rendimiento y la calidad de servicio (QoS) de los usuarios. El trabajo comienza con la recopilación de un nuevo tipo de conjunto de datos, utilizando una herramienta de monitoreo por aire, que permite extraer la información de control del canal de radioenlace, sin dañar las identidades de los usuarios. El análisis posterior comprende una caracterización estadística del tráfico y la derivación de modelos de predicción para el tráfico de red. Se implementa y compara un amplio grupo de algoritmos para identificar los rendimientos más altos. Además, la tesis aborda un conjunto de aplicaciones en el contexto de redes móviles que son prerrogativas en las redes móviles futuras. Esto incluye la detección de anomalías urbanas, la clasificación de usuarios basada en los servicios de red demandados, el diseño de un esquema de activación proactiva para dispositivos de energía eficiente.Postprint (published version