Security Assessment and Hardening of Fog Computing Systems

In recent years, there has been a shift in computing architectures, moving away from centralized cloud computing towards decentralized edge and fog computing. This shift is driven by factors such as the increasing volume of data generated at the edge, the growing demand for real-time processing and low-latency applications, and the need for improved privacy and data locality. Although this new paradigm offers numerous advantages, it also introduces significant security and reliability challenges. This paper aims to review the architectures and technologies employed in fog computing and identify opportunities for developing novel security assessment and security hardening techniques. These techniques include secure configuration and debloating to enhance the security of middleware, testing techniques to assess secure communication mechanisms, and automated rehosting to speed up the security testing of embedded firmware.Comment: 4 pages, Accepted for publication at The 34th IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

A free real-time hourly tilted solar irradiation data Website for Europe

The engineering of solar power applications, such as photovoltaic energy (PV) or thermal solar energy requires the knowledge of the solar resource available for the solar energy system. This solar resource is generally obtained from datasets, and is either measured by ground-stations, through the use of pyranometers, or by satellites. The solar irradiation data are generally not free, and their cost can be high, in particular if high temporal resolution is required, such as hourly data. In this work, we present an alternative method to provide free hourly global solar tilted irradiation data for the whole European territory through a web platform. The method that we have developed generates solar irradiation data from a combination of clear-sky simulations and weather conditions data. The results are publicly available for free through Soweda, a Web interface. To our knowledge, this is the first time that hourly solar irradiance data are made available online, in real-time, and for free, to the public. The accuracy of these data is not suitable for applications that require high data accuracy, but can be very useful for other applications that only require a rough estimate of solar irradiation.Comment: 3 pages, 2 figures, conference proceedings, 29th European Photovoltaic Solar Energy Conference and Exhibition, 2014, Amsterda

Model-driven Engineering IDE for Quality Assessment of Data-intensive Applications

This article introduces a model-driven engineering (MDE) integrated development environment (IDE) for Data-Intensive Cloud Applications (DIA) with iterative quality enhancements. As part of the H2020 DICE project (ICT-9-2014, id 644869), a framework is being constructed and it is composed of a set of tools developed to support a new MDE methodology. One of these tools is the IDE which acts as the front-end of the methodology and plays a pivotal role in integrating the other tools of the framework. The IDE enables designers to produce from the architectural structure of the general application along with their properties and QoS/QoD annotations up to the deployment model. Administrators, quality assurance engineers or software architects may also run and examine the output of the design and analysis tools in addition to the designer in order to assess the DIA quality in an iterative process