15 research outputs found
Enabling RAN Slicing Through Carrier Aggregation in mmWave Cellular Networks
The ever increasing number of connected devices and of new and heterogeneous
mobile use cases implies that 5G cellular systems will face demanding technical
challenges. For example, Ultra-Reliable Low-Latency Communication (URLLC) and
enhanced Mobile Broadband (eMBB) scenarios present orthogonal Quality of
Service (QoS) requirements that 5G aims to satisfy with a unified Radio Access
Network (RAN) design. Network slicing and mmWave communications have been
identified as possible enablers for 5G. They provide, respectively, the
necessary scalability and flexibility to adapt the network to each specific use
case environment, and low latency and multi-gigabit-per-second wireless links,
which tap into a vast, currently unused portion of the spectrum. The
optimization and integration of these technologies is still an open research
challenge, which requires innovations at different layers of the protocol
stack. This paper proposes to combine them in a RAN slicing framework for
mmWaves, based on carrier aggregation. Notably, we introduce MilliSlice, a
cross-carrier scheduling policy that exploits the diversity of the carriers and
maximizes their utilization, thus simultaneously guaranteeing high throughput
for the eMBB slices and low latency and high reliability for the URLLC flows.Comment: 8 pages, 8 figures. Proc. of the 18th Mediterranean Communication and
Computer Networking Conference (MedComNet 2020), Arona, Italy, 202
Towards Touch-to-Access Device Authentication Using Induced Body Electric Potentials
This paper presents TouchAuth, a new touch-to-access device authentication
approach using induced body electric potentials (iBEPs) caused by the indoor
ambient electric field that is mainly emitted from the building's electrical
cabling. The design of TouchAuth is based on the electrostatics of iBEP
generation and a resulting property, i.e., the iBEPs at two close locations on
the same human body are similar, whereas those from different human bodies are
distinct. Extensive experiments verify the above property and show that
TouchAuth achieves high-profile receiver operating characteristics in
implementing the touch-to-access policy. Our experiments also show that a range
of possible interfering sources including appliances' electromagnetic
emanations and noise injections into the power network do not affect the
performance of TouchAuth. A key advantage of TouchAuth is that the iBEP sensing
requires a simple analog-to-digital converter only, which is widely available
on microcontrollers. Compared with existing approaches including intra-body
communication and physiological sensing, TouchAuth is a low-cost, lightweight,
and convenient approach for authorized users to access the smart objects found
in indoor environments.Comment: 16 pages, accepted to the 25th Annual International Conference on
Mobile Computing and Networking (MobiCom 2019), October 21-25, 2019, Los
Cabos, Mexic
WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City
The literature is rich in techniques and methods to perform Continuous Authentication (CA) using biometric data, both physiological and behavioral. As a recent trend, less invasive methods such as the ones based on context-aware recognition allows the continuous identification of the user by retrieving device and app usage patterns. However, a still uncovered research topic is to extend the concepts of behavioral and context-aware biometric to take into account all the sensing data provided by the Internet of Things (IoT) and the smart city, in the shape of user habits. In this paper, we propose a meta-model-driven approach to mine user habits, by means of a combination of IoT data incoming from several sources such as smart mobility, smart metering, smart home, wearables and so on. Then, we use those habits to seamlessly authenticate users in real time all along the smart city when the same behavior occurs in different context and with different sensing technologies. Our model, which we called WoX+, allows the automatic extraction of user habits using a novel Artificial Intelligence (AI) technique focused on high-level concepts. The aim is to continuously authenticate the users using their habits as behavioral biometric, independently from the involved sensing hardware. To prove the effectiveness of WoX+ we organized a quantitative and qualitative evaluation in which 10 participants told us a spending habit they have involving the use of IoT. We chose the financial domain because it is ubiquitous, it is inherently multi-device, it is rich in time patterns, and most of all it requires a secure authentication. With the aim of extracting the requirement of such a system, we also asked the cohort how they expect WoX+ will use such habits to securely automatize payments and identify them in the smart city. We discovered that WoX+ satisfies most of the expected requirements, particularly in terms of unobtrusiveness of the solution, in contrast with the limitations observed in the existing studies. Finally, we used the responses given by the cohorts to generate synthetic data and train our novel AI block. Results show that the error in reconstructing the habits is acceptable: Mean Squared Error Percentage (MSEP) 0.04%
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Sustainable IoT Sensing Applications Development through GraphQL-Based Abstraction Layer
Internet of Things (IoT) networks are mostly comprised of power-constrained devices, therefore the most important consideration in designing IoT applications, based on sensor networks is energy efficiency. Minor improvement in energy conservation methods can lead to a significant increase in the lifetime of IoT devices and overall network. To achieve efficient utilisation of energy, different solutions are proposed such as duty cycling optimization, design changes at the MAC layer, etc. In this paper, we propose a new approach to overcome this challenge in cloud-based IoT sensing applications, based on integration of an abstraction layer with constrained application mechanism. To achieve energy conservation and efficient data management in IoT sensing applications, we incorporate modules of efficient web framework with cloud services, in order to minimize the number of round trips for data delivery and graph-based data representation. Our study is the first attempt in the literature, to the best of our knowledge, which introduces the potential of this integration for achieving the aforementioned objectives in the target applications. We implemented the proposed interfacing of abstraction layer in constrained applications, to develop a testbed using Z1 IoT motes, Contiki OS and GraphQL web framework with Google cloud services. Experimental comparisons against baseline REST architecture approach show that our proposed approach achieved significant reductions in data delivery delay and energy consumption (minimum 51.53% and 52.88%, respectively) in IoT applications involving sensor network.</jats:p
Clothing-Integrated Human-Technology Interaction
Due to the different disabilities of people and versatile use environments, the current handheld and screen-based digital devices on the market are not suitable for all consumers and all situations. Thus, there is an urgent need for human- technology interaction solutions, where the required input actions to digital devices are simple, easy to establish, and instinctive, allowing the whole society to effortlessly interact with the surrounding technology.
In passive ultra-high frequency (UHF) radio frequency identification (RFID) systems, the tag consists only of an antenna and a simple integrated circuit (IC). The tag gets all the needed power from the RFID reader and can be thus seamlessly and in a maintenance-free way integrated into clothing.
In this thesis, it is presented that by integrating passive UHF RFID technology into clothing, body movements and gestures can be monitored by monitoring the individual IDs and backscattered signals of the tags. Electro-textiles and embroidery with conductive thread are found to be suitable options when manufacturing and materials for such garments are considered. This thesis establishes several RFID- based interface solutions, multiple types of inputs through RFID platforms, and controlling the surrounding and communicating with RFID-based on/off functions.
The developed intelligent clothing is visioned to provide versatile applications for assistive technology, for entertainment, and ambient assistant living, and for comfort and safety in work environments, just to name a few examples
Sistemas de posicionamento baseados em comunicação por luz para ambientes interiores
The demand for highly precise indoor positioning systems (IPSs) is growing
rapidly due to its potential in the increasingly popular techniques of the
Internet of Things, smart mobile devices, and artificial intelligence. IPS
becomes a promising research domain that is getting wide attention due to its
benefits in several working scenarios, such as, industries, indoor public
locations, and autonomous navigation. Moreover, IPS has a prominent
contribution in day-to-day activities in organizations such as health care
centers, airports, shopping malls, manufacturing, underground locations, etc.,
for safe operating environments. In indoor environments, both radio frequency
(RF) and optical wireless communication (OWC) based technologies could be
adopted for localization. Although the RF-based global positioning system,
such as, Global positioning system offers higher penetration rates with
reduced accuracy (i.e., in the range of a few meters), it does not work well in
indoor environments (and not at all in certain cases such as tunnels, mines,
etc.) due to the very weak signal and no direct access to the satellites. On the
other hand, the light-based system known as a visible light positioning (VLP)
system, as part of the OWC systems, uses the pre-existing light-emitting
diodes (LEDs)-based lighting infrastructure, could be used at low cost and
high accuracy compared with the RF-based systems. VLP is an emerging
technology promising high accuracy, high security, low deployment cost,
shorter time response, and low relative complexity when compared with RFbased
positioning.
However, in indoor VLP systems, there are some concerns such as,
multipath reflection, transmitter tilting, transmitter’s position, and orientation
uncertainty, human shadowing/blocking, and noise causing the increase in
the positioning error, thereby reducing the positioning accuracy of the system.
Therefore, it is imperative to capture the characteristics of different VLP
channel and properly model them for the dual purpose of illumination and
localization. In this thesis, firstly, the impact of transmitter tilting angles and
multipath reflections are studied and for the first time, it is demonstrated that
tilting the transmitter can be beneficial in VLP systems considering both line of
sight (LOS) and non-line of sight transmission paths. With the transmitters
oriented towards the center of the receiving plane, the received power level is
maximized due to the LOS components. It is also shown that the proposed
scheme offers a significant accuracy improvement of up to ~66% compared
with a typical non-tilted transmitter VLP. The effect of tilting the transmitter on
the lighting uniformity is also investigated and results proved that the
uniformity achieved complies with the European Standard EN 12464-1.
After that, the impact of transmitter position and orientation uncertainty on
the accuracy of the VLP system based on the received signal strength (RSS)
is investigated. Simulation results show that the transmitter uncertainties have
a severe impact on the positioning error, which can be leveraged through the
usage of more transmitters. Concerning a smaller transmitter’s position
epochs, and the size of the training set. It is shown that,
the ANN with Bayesian regularization outperforms the traditional RSS
technique using the non-linear least square estimation for all values of signal
to noise ratio.
Furthermore, a novel indoor VLP system is proposed based on support
vector machines and polynomial regression considering two different
multipath environments of an empty room and a furnished room. The results
show that, in an empty room, the positioning accuracy improvement for the
positioning error of 2.5 cm are 36.1, 58.3, and 72.2 % for three different
scenarios according to the regions’ distribution in the room. For the furnished
room, a positioning relative accuracy improvement of 214, 170, and 100 % is
observed for positioning error of 0.1, 0.2, and 0.3 m, respectively. Ultimately,
an indoor VLP system based on convolutional neural networks (CNN) is
proposed and demonstrated experimentally in which LEDs are used as
transmitters and a rolling shutter camera is used as receiver. A detection
algorithm named single shot detector (SSD) is used which relies on CNN (i.e.,
MobileNet or ResNet) for classification as well as position estimation of each
LED in the image. The system is validated using a real-world size test setup
containing eight LED luminaries. The obtained results show that the maximum
average root mean square positioning error achieved is 4.67 and 5.27 cm with
SSD MobileNet and SSD ResNet models, respectively. The validation results
show that the system can process 67 images per second, allowing real-time
positioning.A procura por sistemas de posicionamento interior (IPSs) de alta precisão tem
crescido rapidamente devido ao seu interesse nas técnicas cada vez mais
populares da Internet das Coisas, dispositivos móveis inteligentes e
inteligência artificial. O IPS tornou-se um domínio de pesquisa promissor que
tem atraído grande atenção devido aos seus benefícios em vários cenários de
trabalho, como indústrias, locais públicos e navegação autónoma. Além disso,
o IPS tem uma contribuição destacada no dia a dia de organizações, como,
centros de saúde, aeroportos, supermercados, fábricas, locais subterrâneos,
etc. As tecnologias baseadas em radiofrequência (RF) e comunicação óptica
sem fio (OWC) podem ser adotadas para localização em ambientes interiores.
Embora o sistema de posicionamento global (GPS) baseado em RF ofereça
taxas de penetração mais altas com precisão reduzida (ou seja, na faixa de
alguns metros), não funciona bem em ambientes interiores (e não funciona
bem em certos casos como túneis, minas, etc.) devido ao sinal muito fraco e
falta de acesso direto aos satélites. Por outro lado, o sistema baseado em luz
conhecido como sistema de posicionamento de luz visível (VLP), como parte
dos sistemas OWC, usa a infraestrutura de iluminação baseada em díodos
emissores de luz (LEDs) pré-existentes, é um sistemas de baixo custo e alta
precisão quando comprado com os sistemas baseados em RF. O VLP é uma
tecnologia emergente que promete alta precisão, alta segurança, baixo custo
de implantação, menor tempo de resposta e baixa complexidade relativa
quando comparado ao posicionamento baseado em RF.
No entanto, os sistemas VLP interiores, exibem algumas limitações, como, a
reflexão multicaminho, inclinação do transmissor, posição do transmissor e
incerteza de orientação, sombra/bloqueio humano e ruído, que têm como
consequência o aumento do erro de posicionamento, e consequente redução
da precisão do sistema. Portanto, é imperativo estudar as características dos
diferentes canais VLP e modelá-los adequadamente para o duplo propósito de
iluminação e localização. Esta tesa aborda, primeiramente, o impacto dos
ângulos de inclinação do transmissor e reflexões multipercurso no
desempenho do sistema de posicionamento. Demonstra-se que a inclinação
do transmissor pode ser benéfica em sistemas VLP considerando tanto a linha
de vista (LOS) como as reflexões. Com os transmissores orientados para o
centro do plano recetor, o nível de potência recebido é maximizado devido aos
componentes LOS. Também é mostrado que o esquema proposto oferece
uma melhoria significativa de precisão de até ~66% em comparação com um
sistema VLP de transmissor não inclinado típico. O efeito da inclinação do
transmissor na uniformidade da iluminação também é investigado e os
resultados comprovam que a uniformidade alcançada está de acordo com a
Norma Europeia EN 12464-1.
O impacto da posição do transmissor e incerteza de orientação na precisão
do sistema VLP com base na intensidade do sinal recebido (RSS) foi também investigado. Os resultados da simulação mostram que as incertezas do
transmissor têm um impacto severo no erro de posicionamento, que pode ser
atenuado com o uso de mais transmissores. Para incertezas de
posicionamento dos transmissores menores que 5 cm, os erros médios de
posicionamento são 23.3, 15.1 e 13.2 cm para conjuntos de 4, 9 e 16
transmissores, respetivamente. Enquanto que, para a incerteza de orientação
de um transmissor menor de 5°, os erros médios de posicionamento são 31.9,
20.6 e 17 cm para conjuntos de 4, 9 e 16 transmissores, respetivamente.
O trabalho da tese abordou a investigação dos aspetos de projeto de um
sistema VLP indoor no qual uma rede neuronal artificial (ANN) é utilizada para
estimativa de posicionamento considerando um canal multipercurso. O estudo
considerou a influência do ruído como indicador de desempenho para a
comparação entre diferentes abordagens de projeto. Três algoritmos de treino
de ANNs diferentes foram considerados, a saber, Levenberg-Marquardt,
regularização Bayesiana e algoritmos de gradiente conjugado escalonado,
para minimizar o erro de posicionamento no sistema VLP. O projeto da ANN foi
otimizado com base no número de neurónios nas camadas ocultas, no número
de épocas de treino e no tamanho do conjunto de treino. Mostrou-se que, a
ANN com regularização Bayesiana superou a técnica RSS tradicional usando
a estimação não linear dos mínimos quadrados para todos os valores da
relação sinal-ruído.
Foi proposto um novo sistema VLP indoor baseado em máquinas de vetores
de suporte (SVM) e regressão polinomial considerando dois ambientes
interiores diferentes: uma sala vazia e uma sala mobiliada. Os resultados
mostraram que, numa sala vazia, a melhoria da precisão de posicionamento
para o erro de posicionamento de 2.5 cm são 36.1, 58.3 e 72.2% para três
cenários diferentes de acordo com a distribuição das regiões na sala. Para a
sala mobiliada, uma melhoria de precisão relativa de posicionamento de 214,
170 e 100% é observada para erro de posicionamento de 0.1, 0.2 e 0.3 m,
respetivamente.
Finalmente, foi proposto um sistema VLP indoor baseado em redes neurais
convolucionais (CNN). O sistema foi demonstrado experimentalmente usando
luminárias LED como transmissores e uma camara com obturador rotativo
como recetor. O algoritmo de detecção usou um detector de disparo único
(SSD) baseado numa CNN pré configurada (ou seja, MobileNet ou ResNet)
para classificação. O sistema foi validado usando uma configuração de teste
de tamanho real contendo oito luminárias LED. Os resultados obtidos
mostraram que o erro de posicionamento quadrático médio alcançado é de
4.67 e 5.27 cm com os modelos SSD MobileNet e SSD ResNet,
respetivamente. Os resultados da validação mostram que o sistema pode
processar 67 imagens por segundo, permitindo o posicionamento em tempo
real.Programa Doutoral em Engenharia Eletrotécnic