With the expansion of 5G networks, new business models are arising where multi-tenancy and active infrastructure sharing will be key enablers for them. With these new opportunities, new security risks are appearing in the form of a complex and evolving threat landscape for 5G networks, being one of the main challenges for the 5G mass rollout. In 5G-enabled scenarios, adversaries can exploit vulnerabilities associated with resource sharing to perform lateral movements targeting other tenant resources, as well as to disturb the 5G services offered or even the infrastructure resources. Moreover, existing security and trust models are not adequate to react to the dynamicity of the 5G infrastructure threats nor to the multi-tenancy security risks. Hence, we propose in this work a new security and trust framework for 5G multi-domain scenarios. To motivate its application, we detail a threat model covering multi-tenant scenarios in an underlying 5G network infrastructure. We also propose different ways to mitigate these threats by increasing the security and trust levels using network security monitoring, threat investigation, and end-to-end trust establishments. The framework is applied in a realistic use case of the H2020 5GZORRO project, which envisions a multi-tenant environment where domain owners share resources at will. The proposed framework forms a secure environment with zero-touch automation capabilities, minimizing human intervention.

The electric vehicle (EV) industry has made significant progress but, in many markets, there are still barriers holding back its advancement. A key issue is the anxiety caused to the drivers by the limited range of current EV models and the inadequate access to charging stations in long-distance trips, as is the case on highways. We propose an intuitive multi-criteria approach that optimally places EV charging stations on highways that (partially) lack such points. The approach, which is applied in an iterative fashion to dynamically evaluate the alternatives, considers a set of practical criteria related to the traffic intensity and the relative location of the charging stations with interchanges, major cities, and existing stations, thus supporting decisions in a pragmatic way. The optimal locations are determined by taking into consideration constraints about the EV driving range and installation preferences to improve the operation of the highway while ensuring reasonable cost of investment. The proposed approach is showcased in the Egnatia Motorway, the longest highway in Greece that runs a total of 670 km but currently involves a single EV charging point. Our findings illustrate the utility of the proposed approach and highlight its merits as a decision-support tool.

The scheduled maintenance of industrial equipment is usually performed with a low frequency, as it usually leads to unpredicted downtime in business operations. Nevertheless, this confers a risk of failure in individual modules of the equipment, which may diminish its performance or even lead to its breakdown, rendering it non-operational. Lately, predictive maintenance methods have been considered for industrial systems, such as power generation stations, as a proactive measure for preventing failures. Such methods use data gathered from industrial equipment and Machine Learning (ML) algorithms to identify data patterns that indicate anomalies and may lead to potential failures. However, industrial equipment exhibits specific behavior and interactions that originate from its configuration from the manufacturer and the system that is installed, which constitutes a great challenge for the effectiveness of ML model maintenance and failure predictions. In this article, we propose a novel method for tackling this challenge based on the development of a digital twin for industrial equipment known as a Remote Terminal Unit (RTU). RTUs are used in electrical systems to provide the remote monitoring and control of critical equipment, such as power generators. The method is applied in an RTU that is connected to a real power generator within a Public Power Corporation (PPC) facility, where operational anomalies are forecasted based on measurements of its processing power, operating temperature, voltage, and storage memory.

The electrical grid is gradually transitioning towards being an interconnected area of the smart grid, where embedded devices operate in an autonomous manner without any human intervention. An important element for this transition is the energy demand prediction, since the needs for energy have substantially increased due to the introduction of new and heavy consumption sources, such as electric vehicles. Accurate energy demand prediction, especially for short-term durations (i.e., minutes to hours), allows grid operators to produce the substantial amount needed to satisfy the demand–response equilibrium and avoid peak electricity load conditions that may also lead to blackouts in densely populated areas. However, to achieve such an accuracy level, machine learning (ML) models require extensive training with historical measurements, which is usually resource intensive (e.g., in memory and processing power). Hence, deriving accurate predictions for short-term energy demands is challenging due to the absence of external factors such as environmental data from different regions and seasons and categorical values such as bank/bridging holidays in the ML model. Additionally, existing work focuses on ML model execution on Cloud platforms, which usually does not satisfy the real-time requirements of grid operators for short-term energy demand predictions. To address these challenges, this article presents a new method that considers environmental factors and categorical values to build an energy profile for each consumer in a multi-access edge computing (MEC) framework. The method is also based on the Temporal Fusion Transformer (TFT) ML model, which allows it to learn the temporal dependencies of the gathered historical measurements and predict energy demands with satisfying accuracy. The method is applied to a home energy management system testbed containing photovoltaic systems, smart meters, sensors and actuators for detecting environmental factors (i.e., temperature, humidity and radiation) as well as energy storage systems as an additional energy supply source. The MEC framework is deployed in data concentrator devices where the TFT ML model is executed with low resource requirements, ensuring additional security as the data do not leave the location where they are produced.

The opportunities created in the Greek electric vehicle (EV) market have allowed potential investors to participate in the development of the EV charging infrastructure of the country. Yet, the decision process of relevant stakeholders for strategic investments is challenging, involving the identification of the most promising charging sites from a set of multiple alternative locations of various features that may significantly affect their business competitive advantage. This paper attempts to facilitate decision making in such settings using a comprehensive, yet thorough multi-criteria decision analysis framework. The proposed approach is validated considering ten Greek municipalities of different characteristics. The results show- case the overall strengths of the proposed approach and its utility in the strategic planning process of potential investors.