Network-aware video streaming for future media Internet

272 p

An Approach to Data Analysis in 5G Networks

5G networks expect to provide significant advances in network management compared to traditional mobile infrastructures by leveraging intelligence capabilities such as data analysis, prediction, pattern recognition and artificial intelligence. The key idea behind these actions is to facilitate the decision-making process in order to solve or mitigate common network problems in a dynamic and proactive way. In this context, this paper presents the design of Self-Organized Network Management in Virtualized and Software Defined Networks (SELFNET) Analyzer Module, which main objective is to identify suspicious or unexpected situations based on metrics provided by different network components and sensors. The SELFNET Analyzer Module provides a modular architecture driven by use cases where analytic functions can be easily extended. This paper also proposes the data specification to define the data inputs to be taking into account in diagnosis process. This data specification has been implemented with different use cases within SELFNET Project, proving its effectiveness.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEUnión Europea. Horizonte 2020pu

Data Analytics and Performance Enhancement in Edge-Cloud Collaborative Internet of Things Systems

Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets self-organized by IoT devices. First of all, the issues on outlier detection and data aggregation are addressed through the development of recursive principal component analysis (R-PCA) based data analysis framework. The framework is developed in a cluster-based structure to fully exploit the spatial correlation of IoT data. Specifically, the sensing devices are gathered into clusters based on spatial data correlation. Edge devices are assigned to the clusters for the R-PCA based outlier detection and data aggregation. The outlier-free and aggregated data are forwarded to the remote cloud server for data reconstruction and storage. Moreover, a data reduction scheme is further proposed to relieve the burden on the trunk link for data uploading by utilizing the temporal data correlation. Kalman filters (KFs) with identical parameters are maintained at the edge and cloud for data prediction. The amount of data uploading is reduced by using the data predicted by the KF in the cloud instead of uploading all the practically measured data. Furthermore, an unmanned aerial vehicle (UAV) assisted IoT system is particularly designed for large-scale monitoring. Wireless sensor nodes are flexibly deployed for environmental sensing and self-organized into wireless sensor networks (WSNs). A physical topology discovery scheme is proposed to construct the physical topology of WSNs in the cloud server to facilitate performance optimization, where the physical topology indicates both the logical connectivity statuses of WSNs and the physical locations of WSN nodes. The physical topology discovery scheme is implemented through the newly developed parallel Metropolis-Hastings random walk based information sampling and network-wide 3D localization algorithms, where UAVs are served as the mobile edge devices and anchor nodes. Based on the physical topology constructed in the cloud, a UAV-enabled spatial data sampling scheme is further proposed to efficiently sample data from the monitoring area by using denoising autoencoder (DAE). By deploying the encoder of DAE at the UAV and decoder in the cloud, the data can be partially sampled from the sensing field and accurately reconstructed in the cloud. In the final part of the thesis, a novel autoencoder (AE) neural network based data outlier detection algorithm is proposed, where both encoder and decoder of AE are deployed at the edge devices. Data outliers can be accurately detected by the large fluctuations in the squared error generated by the data passing through the encoder and decoder of the AE

Examining citizens' perceived value of internet of things technologies in facilitating public sector services engagement

YesWith the advancement of disruptive new technologies, there has been a considerable focus on personalisation as an important component in nurturing users' engagement. In the context of smart cities, Internet of Things (IoT) offer a unique opportunity to help empower citizens and improve societies' engagement with their governments at both micro and macro levels. This study aims to examine the role of perceived value of IoT in improving citizens' engagement with public services. A survey of 313 citizens in the UK, engaging in various public services, enabled through IoT, found that the perceived value of IoT is strongly influenced by empowerment, perceived usefulness and privacy related issues resulting in significantly affecting their continuous use intentions. The study offers valuable insights into the importance of perceived value of IoT-enabled services, while at the same time, providing an intersectional perspective of UK citizens towards the use of disruptive new technologies in the public sector

Developing a Model of Mobile Web Uptake in the Developing World

This research was motivated by the limited penetration of the Internet within emerging economies and the ‘mobile miracle’, which refers to a steep increase of mobile phone penetration. In the context of the developing world, harnessing the ‘mobile miracle’ to improve Internet access can leverage the potential of the Web. However, no comprehensive model exists, which can identify and measure indicators of Mobile Web uptake. The absence of such a model creates problems in understanding the impact of the Mobile Web. This has generated the key question under study in this thesis: “What is a suitable model for Mobile Web uptake and its impact in the developing world?”In order to address the research question, the Model of Mobile Web Uptake in the Developing World (MMWUDW) was created. It was informed by a literature review, pilot study in Kenya and expert reviews. The MMWUDW was evaluated using Structural Equation Modelling (SEM) with the primary data that consisted of the questionnaire and interview data from Indonesia. The SEM analysis was triangulated with the questionnaire results and interview findings. Examining the primary data to evaluate the MMWUDW was essential to understand why people used mobile phones to make or follow links on the Web. The MMWUDW has three main factors. These are Mobile Web maturity, uptake and impact. The results of the SEM suggested that mobile networks, percentage of income for mobile credits, literacy and digital literacy did not affect Mobile Web uptake. In contrast, web-enabled phones, Web applications or contents, and mobile operator services strongly indicated Mobile Web maturity, which was a prerequisite for Mobile Web uptake. The uptake then created Mobile Web impact, which included both positive and negative features; ease of access to information and a convenient way to communicate; being entertained and empowered; maintaining of social cohesion and economic benefits, as well as wasting time and money, and being exposed to cyber bullying. Moreover, the research identified areas for improvement in the Mobile Web and regression equations to measure the factors and indicators of the MMWUDW. Possible future work comprises advancement of the MMWUDW and new Web Science research on the Mobile Web in developing countries.<br/

Towards Autonomous Computer Networks in Support of Critical Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen