ENABLING ATTRIBUTE BASED ACCESS CONTROL WITHIN THE INTERNET OF THINGS (IOT)

With the wide-scale development of the Internet of Things (IoT) and the usage of low-powered devices (sensors) together with smart devices, numerous people are using IoT systems in their homes and businesses to have more control over their technology. Unfortunately, some users of IoT systems that are controlled by a mobile application do not have a high level of data protection to respond in case the device is lost, stolen, or used by one of the owner’s friends or family members. The problem studied in this research is how to apply one of access control methods an IoT system whether they are stored locally on a sensor or on a cloud. To solve the problem, an attribute-based access control (ABAC) mechanism is applied to give the system the ability to apply policies to detect any unauthorized entry by evaluating some of the users’ attributes: the accessed time, the device media access control address (MAC address), the username, and password. Finally, a prototype was built to test the proposed solution in two ways; one is locally on a low-powered device, the second using cloud platform for the data storage. To evaluate both the prototype implementation, this research had an evaluation plan to mimic the real-world interactions by obtaining the response times when different numbers of requests sent from diverse numbers of users in different delays. The evaluation results showed that the first implementation was noticeably faster than the second implementation

CLOUD-BASED SOLUTIONS IMPROVING TRANSPARENCY, OPENNESS AND EFFICIENCY OF OPEN GOVERNMENT DATA

A central pillar of open government programs is the disclosure of data held by public agencies using Information and Communication Technologies (ICT). This disclosure relies on the creation of open data portals (e.g. Data.gov) and has subsequently been associated with the expression Open Government Data (OGD). The overall goal of these governmental initiatives is not limited to enhance transparency of public sectors but aims to raise awareness of how released data can be put to use in order to enable the creation of new products and services by private sectors. Despite the usage of technological platforms to facilitate access to government data, open data portals continue to be organized in order to serve the goals of public agencies without opening the doors to public accountability, information transparency, public scrutiny, etc. This thesis considers the basic aspects of OGD including the definition of technical models for organizing such complex contexts, the identification of techniques for combining data from several portals and the proposal of user interfaces that focus on citizen-centred usability. In order to deal with the above issues, this thesis presents a holistic approach to OGD that aims to go beyond problems inherent their simple disclosure by providing a tentative answer to the following questions: 1) To what extent do the OGD-based applications contribute towards the creation of innovative, value-added services? 2) What technical solutions could increase the strength of this contribution? 3) Can Web 2.0 and Cloud technologies favour the development of OGD apps? 4) How should be designed a common framework for developing OGD apps that rely on multiple OGD portals and external web resources? In particular, this thesis is focused on devising computational environments that leverage the content of OGD portals (supporting the initial phase of data disclosure) for the creation of new services that add value to the original data. The thesis is organized as follows. In order to offer a general view about OGD, some important aspects about open data initiatives are presented including their state of art, the existing approaches for publishing and consuming OGD across web resources, and the factors shaping the value generated through government data portals. Then, an architectural framework is proposed that gathers OGD from multiple sites and supports the development of cloud-based apps that leverage these data according to potentially different exploitation roots ranging from traditional business to specialized supports for citizens. The proposed framework is validated by two cloud-based apps, namely ODMap (Open Data Mapping) and NESSIE (A Network-based Environment Supporting Spatial Information Exploration). In particular, ODMap supports citizens in searching and accessing OGD from several web sites. NESSIE organizes data captured from real estate agencies and public agencies (i.e. municipalities, cadastral offices and chambers of commerce) in order to provide citizens with a geographic representation of real estate offers and relevant statistics about the price trend.A central pillar of open government programs is the disclosure of data held by public agencies using Information and Communication Technologies (ICT). This disclosure relies on the creation of open data portals (e.g. Data.gov) and has subsequently been associated with the expression Open Government Data (OGD). The overall goal of these governmental initiatives is not limited to enhance transparency of public sectors but aims to raise awareness of how released data can be put to use in order to enable the creation of new products and services by private sectors. Despite the usage of technological platforms to facilitate access to government data, open data portals continue to be organized in order to serve the goals of public agencies without opening the doors to public accountability, information transparency, public scrutiny, etc. This thesis considers the basic aspects of OGD including the definition of technical models for organizing such complex contexts, the identification of techniques for combining data from several portals and the proposal of user interfaces that focus on citizen-centred usability. In order to deal with the above issues, this thesis presents a holistic approach to OGD that aims to go beyond problems inherent their simple disclosure by providing a tentative answer to the following questions: 1) To what extent do the OGD-based applications contribute towards the creation of innovative, value-added services? 2) What technical solutions could increase the strength of this contribution? 3) Can Web 2.0 and Cloud technologies favour the development of OGD apps? 4) How should be designed a common framework for developing OGD apps that rely on multiple OGD portals and external web resources? In particular, this thesis is focused on devising computational environments that leverage the content of OGD portals (supporting the initial phase of data disclosure) for the creation of new services that add value to the original data. The thesis is organized as follows. In order to offer a general view about OGD, some important aspects about open data initiatives are presented including their state of art, the existing approaches for publishing and consuming OGD across web resources, and the factors shaping the value generated through government data portals. Then, an architectural framework is proposed that gathers OGD from multiple sites and supports the development of cloud-based apps that leverage these data according to potentially different exploitation roots ranging from traditional business to specialized supports for citizens. The proposed framework is validated by two cloud-based apps, namely ODMap (Open Data Mapping) and NESSIE (A Network-based Environment Supporting Spatial Information Exploration). In particular, ODMap supports citizens in searching and accessing OGD from several web sites. NESSIE organizes data captured from real estate agencies and public agencies (i.e. municipalities, cadastral offices and chambers of commerce) in order to provide citizens with a geographic representation of real estate offers and relevant statistics about the price trend

Toward Customizable Multi-tenant SaaS Applications

abstract: Nowadays, Computing is so pervasive that it has become indeed the 5th utility (after water, electricity, gas, telephony) as Leonard Kleinrock once envisioned. Evolved from utility computing, cloud computing has emerged as a computing infrastructure that enables rapid delivery of computing resources as a utility in a dynamically scalable, virtualized manner. However, the current industrial cloud computing implementations promote segregation among different cloud providers, which leads to user lockdown because of prohibitive migration cost. On the other hand, Service-Orented Computing (SOC) including service-oriented architecture (SOA) and Web Services (WS) promote standardization and openness with its enabling standards and communication protocols. This thesis proposes a Service-Oriented Cloud Computing Architecture by combining the best attributes of the two paradigms to promote an open, interoperable environment for cloud computing development. Mutil-tenancy SaaS applicantions built on top of SOCCA have more flexibility and are not locked down by a certain platform. Tenants residing on a multi-tenant application appear to be the sole owner of the application and not aware of the existence of others. A multi-tenant SaaS application accommodates each tenant’s unique requirements by allowing tenant-level customization. A complex SaaS application that supports hundreds, even thousands of tenants could have hundreds of customization points with each of them providing multiple options, and this could result in a huge number of ways to customize the application. This dissertation also proposes innovative customization approaches, which studies similar tenants’ customization choices and each individual users behaviors, then provides guided semi-automated customization process for the future tenants. A semi-automated customization process could enable tenants to quickly implement the customization that best suits their business needs.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Cloud Platform for Research Crowdsourcing in Mobile Testing

Mobile application testing and testing over a cloud are two highly topical fields nowadays. Mobile testing presents specific test activities, including verification of an application against a variety of heterogeneous smartphone models and versions of operating systems (OS), build distribution and test team management, monitoring and user experience analytics of an application in production, etc. Cloud benefits are widely used to support all these activities. This study conducts in-depth analyses of existing cloud services for mobile testing and addresses their weaknesses regarding research purposes and testing needs of the critical and business-critical mobile applications.   During this study, a Cloud Testing of Mobile Systems (CTOMS) framework for effective research crowdsourcing in mobile testing was developed. The framework is presented as a lightweight and easily scalable distributed system that provides a cloud service to run tests on a variety of remote mobile devices. CTOMS provides implementation of two novel functionalities that are demanded by advanced investigations in mobile testing. First, it allows full multidirectional testing, which provides the opportunities to test an application on different devices and/or OS versions, and new device models or OS versions for their compatibility with the most popular applications in the market, or just legacy critical apps, etc. Second, CTOMS demonstrates the effective integration of the appropriate testing techniques for mobile development within such a service. In particular, it provides a user with suggestions about coverage of configurations to test on using combinatorial approaches like a base choice, pair-wise, and t-way. The current CTOMS version supports automated functional testing of Android applications and detection of defects in the user interface (UI). This has a great value because requirements for UI and user experience are high for any modern mobile application.    The fundamental analysis of possible test types and techniques using a system like CTOMS was conducted, and ways of possible enhancements and extensions of functionality for possible research are listed. The first case studies prove the work of implemented novel concepts, their usefulness, and their convenience for experiments in mobile testing. The overall work proves that a study of cloud mobile testing is feasible even with small research resources.  M.S

Conformance Checking and Simulation-based Evolutionary Optimization for Deployment and Reconfiguration of Software in the Cloud

Many SaaS providers nowadays want to leverage the cloud's capabilities also for their existing applications, for example, to enable sound scalability and cost-effectiveness. This thesis provides the approach CloudMIG that supports SaaS providers to migrate those applications to IaaS and PaaS-based cloud environments. CloudMIG consists of a step-by-step process and focuses on two core components. (1) Restrictions imposed by specific cloud environments (so-called cloud environment constraints (CECs)), such as a limited file system access or forbidden method calls, can be validated by an automatic conformance checking approach. (2) A cloud deployment option (CDO) determines which cloud environment, cloud resource types, deployment architecture, and runtime reconfiguration rules for exploiting a cloud's elasticity should be used. The implied performance and costs can differ in orders of magnitude. CDOs can be automatically optimized with the help of our simulation-based genetic algorithm CDOXplorer. Extensive lab experiments and an experiment in an industrial context show CloudMIG's applicability and the excellent performance of its two core components

Design of an E-learning system using semantic information and cloud computing technologies

Humanity is currently suffering from many difficult problems that threaten the life and survival of the human race. It is very easy for all mankind to be affected, directly or indirectly, by these problems. Education is a key solution for most of them. In our thesis we tried to make use of current technologies to enhance and ease the learning process. We have designed an e-learning system based on semantic information and cloud computing, in addition to many other technologies that contribute to improving the educational process and raising the level of students. The design was built after much research on useful technology, its types, and examples of actual systems that were previously discussed by other researchers. In addition to the proposed design, an algorithm was implemented to identify topics found in large textual educational resources. It was tested and proved to be efficient against other methods. The algorithm has the ability of extracting the main topics from textual learning resources, linking related resources and generating interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks even for bigger books. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm‘s accuracy was evaluated against Gensim, largely improving its accuracy. Augmenting the system design with the implemented algorithm will produce many useful services for improving the learning process such as: identifying main topics of big textual learning resources automatically and connecting them to other well defined concepts from Wikipedia, enriching current learning resources with semantic information from external sources, providing student with browsable dynamic interactive knowledge graphs, and making use of learning groups to encourage students to share their learning experiences and feedback with other learners.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Luis Sánchez Fernández.- Secretario: Luis de la Fuente Valentín.- Vocal: Norberto Fernández Garcí

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Air Force Institute of Technology Research Report 2010

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physic