6,976 research outputs found
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants
Within the field of soft computing, intelligent optimization modelling techniques include
various major techniques in artificial intelligence. These techniques pretend to generate new business
knowledge transforming sets of "raw data" into business value. One of the principal applications of
these techniques is related to the design of predictive analytics for the improvement of advanced
CBM (condition-based maintenance) strategies and energy production forecasting. These advanced
techniques can be used to transform control system data, operational data and maintenance event data
to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation.
One of the systems where these techniques can be applied with massive potential impact are the
legacy monitoring systems existing in solar PV energy generation plants. These systems produce a
great amount of data over time, while at the same time they demand an important e ort in order to
increase their performance through the use of more accurate predictive analytics to reduce production
losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of
the problems to address. This paper presents a review and a comparative analysis of six intelligent
optimization modelling techniques, which have been applied on a PV plant case study, using the
energy production forecast as the decision variable. The methodology proposed not only pretends
to elicit the most accurate solution but also validates the results, in comparison with the di erent
outputs for the di erent techniques
Detecting Prominent Features and Classifying Network Traffic for Securing Internet of Things Based on Ensemble Methods
abstract: Rapid growth of internet and connected devices ranging from cloud systems to internet of things have raised critical concerns for securing these systems. In the recent past, security attacks on different kinds of devices have evolved in terms of complexity and diversity. One of the challenges is establishing secure communication in the network among various devices and systems. Despite being protected with authentication and encryption, the network still needs to be protected against cyber-attacks. For this, the network traffic has to be closely monitored and should detect anomalies and intrusions. Intrusion detection can be categorized as a network traffic classification problem in machine learning. Existing network traffic classification methods require a lot of training and data preprocessing, and this problem is more serious if the dataset size is huge. In addition, the machine learning and deep learning methods that have been used so far were trained on datasets that contain obsolete attacks. In this thesis, these problems are addressed by using ensemble methods applied on an up to date network attacks dataset. Ensemble methods use multiple learning algorithms to get better classification accuracy that could be obtained when the corresponding learning algorithm is applied alone. This dataset for network traffic classification has recent attack scenarios and contains over fifteen attacks. This approach shows that ensemble methods can be used to classify network traffic and detect intrusions with less training times of the model, and lesser pre-processing without feature selection. In addition, this thesis also shows that only with less than ten percent of the total features of input dataset will lead to similar accuracy that is achieved on whole dataset. This can heavily reduce the training times and classification duration in real-time scenarios.Dissertation/ThesisMasters Thesis Computer Science 201
Mecanismos para controlo e gestão de redes 5G: redes de operador
In 5G networks, time-series data will be omnipresent for the monitoring of network
metrics. With the increase in the number of Internet of Things (IoT) devices
in the next years, it is expected that the number of real-time time-series
data streams increases at a fast pace. To be able to monitor those streams,
test and correlate different algorithms and metrics simultaneously and in a
seamless way, time-series forecasting is becoming essential for the pro-active
successful management of the network.
The objective of this dissertation is to design, implement and test a prediction
system in a communication network, that allows integrating various networks,
such as a vehicular network and a 4G operator network, to improve the network
reliability and Quality-of-Service (QoS). To do that, the dissertation has
three main goals: (1) the analysis of different network datasets and implementation
of different approaches to forecast network metrics, to test different
techniques; (2) the design and implementation of a real-time distributed
time-series forecasting architecture, to enable the network operator to make
predictions about the network metrics; and lastly, (3) to use the forecasting
models made previously and apply them to improve the network performance
using resource management policies.
The tests done with two different datasets, addressing the use cases of congestion
management and resource splitting in a network with a limited number
of resources, show that the network performance can be improved with proactive
management made by a real-time system able to predict the network
metrics and act on the network accordingly.
It is also done a study about what network metrics can cause reduced accessibility
in 4G networks, for the network operator to act more efficiently and
pro-actively to avoid such eventsEm redes 5G, séries temporais serão omnipresentes para a monitorização
de métricas de rede. Com o aumento do número de dispositivos da Internet
das Coisas (IoT) nos próximos anos, é esperado que o número de fluxos de
séries temporais em tempo real cresça a um ritmo elevado. Para monitorizar
esses fluxos, testar e correlacionar diferentes algoritmos e métricas simultaneamente
e de maneira integrada, a previsão de séries temporais está a
tornar-se essencial para a gestão preventiva bem sucedida da rede.
O objetivo desta dissertação é desenhar, implementar e testar um sistema
de previsão numa rede de comunicações, que permite integrar várias redes
diferentes, como por exemplo uma rede veicular e uma rede 4G de operador,
para melhorar a fiabilidade e a qualidade de serviço (QoS). Para isso,
a dissertação tem três objetivos principais: (1) a análise de diferentes datasets
de rede e subsequente implementação de diferentes abordagens para
previsão de métricas de rede, para testar diferentes técnicas; (2) o desenho
e implementação de uma arquitetura distribuÃda de previsão de séries temporais
em tempo real, para permitir ao operador de rede efetuar previsões
sobre as métricas de rede; e finalmente, (3) o uso de modelos de previsão
criados anteriormente e sua aplicação para melhorar o desempenho da rede
utilizando polÃticas de gestão de recursos.
Os testes efetuados com dois datasets diferentes, endereçando os casos de
uso de gestão de congestionamento e divisão de recursos numa rede com
recursos limitados, mostram que o desempenho da rede pode ser melhorado
com gestão preventiva da rede efetuada por um sistema em tempo real capaz
de prever métricas de rede e atuar em conformidade na rede.
Também é efetuado um estudo sobre que métricas de rede podem causar
reduzida acessibilidade em redes 4G, para o operador de rede atuar mais
eficazmente e proativamente para evitar tais acontecimentos.Mestrado em Engenharia de Computadores e Telemátic
Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations
Urban traffic speed prediction aims to estimate the future traffic speed for
improving the urban transportation services. Enormous efforts have been made on
exploiting spatial correlations and temporal dependencies of traffic speed
evolving patterns by leveraging explicit spatial relations (geographical
proximity) through pre-defined geographical structures ({\it e.g.}, region
grids or road networks). While achieving promising results, current traffic
speed prediction methods still suffer from ignoring implicit spatial
correlations (interactions), which cannot be captured by grid/graph
convolutions. To tackle the challenge, we propose a generic model for enabling
the current traffic speed prediction methods to preserve implicit spatial
correlations. Specifically, we first develop a Dual-Transformer architecture,
including a Spatial Transformer and a Temporal Transformer. The Spatial
Transformer automatically learns the implicit spatial correlations across the
road segments beyond the boundary of geographical structures, while the
Temporal Transformer aims to capture the dynamic changing patterns of the
implicit spatial correlations. Then, to further integrate both explicit and
implicit spatial correlations, we propose a distillation-style learning
framework, in which the existing traffic speed prediction methods are
considered as the teacher model, and the proposed Dual-Transformer
architectures are considered as the student model. The extensive experiments
over three real-world datasets indicate significant improvements of our
proposed framework over the existing methods
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