The rapid development and scaling of mobile telecommunications networks, together with related domains such as the edge-cloud continuum have raised significant concerns regarding energy consumption and environmental sustainability. Addressing these concerns requires a focus on CPU energy consumption, as CPUs are among the largest energy consumers in these systems. This paper investigates existing techniques, with a focus on CPU idle states (C-states), performance states (P-states), and frequency scaling governors implemented at both hardware and software levels. These mechanisms enable the dynamic adjustment of CPU parameters, providing opportunities to optimize power consumption, frequency, voltage, and overall system performance. In this regard, three CPUs with different architectures from well-known manufacturers, Intel® and AMD®, are thoroughly examined. A comprehensive dataset, collected under three load scenarios (idle, medium, and high), is used to support the analysis, reflect realistic runtime conditions, and enable a comparison of the technological differences in how these parameters are exposed and utilized.

The rapid advancements in technologies across the Computing Continuum have reinforced the need for the interplay of various network and compute orchestration mechanisms within distributed infrastructure architectures to support the hyper-distributed application (HDA) deployments. A unified approach to managing heterogeneous components is crucial for reconciling conflicting objectives and creating a synergetic framework. To undertake these challenges, we present NEPHELE, a platform that realizes a hierarchical multi-layered orchestration architecture that incorporates infrastructure and application orchestration workflows across diverse resource management layers. The proposed platform integrates well-defined components spanning network and multi-cluster compute domains to enable intent-driven, dynamic orchestration. At its core, the Synergetic Meta Orchestrator (SMO) integrates diverse application requirements, generating deployment plans by interfacing with underlying orchestrators over distributed compute and network infrastructure. In the current work, we present the NEPHELE architecture, enumerate its interaction workflows, and evaluate key components of the overall architecture based on the instantiation and usage of the NEPHELE platform. The platform is evaluated in a multi-domain infrastructure setup to assess the operational overhead of the introduced orchestration functionality, considering also the assessment of different topology configurations on resource instantiation times and allocation dynamics, and network latency. Finally, we demonstrate the platform’s effectiveness in orchestrating distributed application graphs under varying placement intents, performance constraints, and workload stress conditions. The evaluation results outline the effectiveness of NEPHELE in orchestrating various infrastructure layers and application lifecycle scenarios through a unified interface.

The exponential rise of intelligent Internet of Things (IoT) devices and the development of Cyber-Physical Systems (CPS) pose new challenges and requirements for modern applications. These include the need for seamless interconnectivity and interoperable interaction between various physical and virtual elements. The enrichment and transformation of IoT technologies to support such interactions is undergoing, considering the need for convergence with edge and cloud computing technologies and the management of IoT applications across resources in the computing continuum. This broader sense of connectivity is tightly connected with the development of Digital Twins (DT), which take advantage of the development of virtual counterparts of IoT devices and CPS. Novel architectural approaches are required to manage complex DTs’ topologies, collectively forming a Digital Twin Network (DTN) that acts as a middleware to provide advanced communication, efficient orchestration, and autonomous decision-making capabilities. This manuscript presents an architectural approach and a relevant open-source software stack implementation -called VOStack- for developing DTs. VOStack is open and modular by design, while it tackles IoT interoperability and convergence challenges with edge and cloud computing technologies. VOStack is thoroughly evaluated under various deployment schemas, virtualization techniques, and based on the provision of an IoT application in the context of a smart city scenario, demonstrating efficient utilization of resources and high efficiency of Machine Learning (ML)-driven orchestration mechanisms.

Question Answering (QA) over Knowledge Graphs (KGs) has greatly benefited from the rapid growth of Large Language Models (LLMs), which enable the translation of natural language questions into Cypher queries. Most existing approaches rely on one-shot generation via in-context learning or on fine-tuning LLMs; however, both strategies often struggle to generate accurate or executable queries, particularly when dealing with complex or unfamiliar graph schemas. To address these limitations, in this work we propose CyVerACT, an agentic workflow for Text-to-Cypher generation that empowers LLMs with execution- and schema-aware feedback mechanisms. CyVerACT leverages CyVer, a software tool that evaluates Cypher queries in terms of syntax validity and semantic compliance with respect to a specific KG schema, and detects their points of failure. The system customizes the input graph schema based on the input question and iteratively refines the generated queries taking advantage of the error metadata from CyVer to guide subsequent LLM generations. We evaluated and compared CyVerACT to existing single-shot generation and iterative refinement approaches in two publicly available Text-to-Cypher datasets of 2180 entries across various domains and complexities, using both foundational (e.g., GPT-4o, LLama-3) and fine-tuned state-of-the-art models. Experimental results demonstrate that the proposed workflow significantly improves query correctness and execution success rates, achieving up to 52.7% gain in accuracy in terms of syntax validity and schema access, and 13.5% gain in exact match.

The development of social and emotional competencies of students is associated with positive impact on their character development, progress on school activities, establishment of qualitative social relationships, overall well-being and health. Similar effects are also noticed at group level, since improvement of such competencies lead to more inclusive and collaborative climate within a classroom. By considering these positive impacts, in the current work we propose some inventories based on an existing open-access Emotional Intelligence (EI) model to make it applicable for the assessment of social and emotional competencies of students. The basis for our work regards the EmoSocio open-access EI model, while three adaptations are provided for different age groups (6–8, 9–12, 13–18 years old). The detailed versions of the EmoSocio inventories are evaluated and validated based on their wide usage within classrooms in a set of schools across Spain. Their usage is based on the application of a methodology for the assessment of social and emotional competencies of students that takes advantage of the adoption of information and communication technologies.

The cloud-to-edge-to-IoT continuum represents a seamless flow of data processing and management, spanning from centralized cloud services to distributed edge computing and interconnected IoT devices. This paradigm can become very challenging in real implementations, especially in the presence of multiple stakeholders using proprietary and heterogeneous software and hardware. The Horizon Europe NEPHELE project proposes the virtualization of IoT devices through a specific software stack called Virtual Object Stack (VOStack) that promotes openness and interoperability. In this paper, we present an implementation of VOStack using W3C Web of Things (WoT) standard in a post-disaster domain for two different types of IoT devices: a ground robot (Turtlebot2) for navigation and mapping in an unknown environment, and a Raspberry Pi 3 acting as a wireless sensor network gateway. We propose an application graph for the resulting hyper-distributed application (HDA) and present our first implementation to validate the proposed solution.

While the Cloud-Edge continuum scheduling domain is well studied, and proposes numerous approaches, from high-level modeling to dynamic scheduling, monitoring means are rarely debated. Solving the ever-growing number of metrics, which can exist in different units and update frequencies, support various system scales and topologies, relies upon classical 2D features (made of charts, gauges, maps and lists) without proposing visual abstractions directed towards multi-layered architectures and distributed infrastructures. Moreover, the need for monitoring in modern Edge systems and applications exceeds system metrics, as other informational dimensions can be considered, such as the ones collected through web services or IoT devices and sensors in the user spaceWe propose with Palindrome.js a 3D monitoring probe, which enables multidimensional visual modeling and analysis. Through sets of metrics called layers, Palindrome.js builds in 3D a structured visual abstraction for any problem modeled with metrics or KPIs. We present in this article the solution initial background, the related works, its design principles, discuss experimental results, and conclude with our observations and future outcomes.

The positive impact of the development of social and emotional competencies of students is well documented, with benefits being evident in various aspects at individual and classroom level. Based on these findings, multiple social and emotional training programs have been created and combined with methodologies for their proper implementation within classrooms. However, the development of relevant tools to assess these competencies has not followed a similar pace. The few assessment techniques currently available require extensive expertise from teachers in the area of social and emotional training, making accurate and efficient assessments difficult to achieve. To address this issue and improve the assessment process, we introduce the EmoSociograms psychometric tool, an open-source software designed for easy application by teachers in both in-person and online classrooms. We detail the main components and functionalities of EmoSociograms, along with an evaluation based on its usage in primary and secondary education schools.

The dynamic workload demands of smart city applications hosted on edge infrastructures require the development of advanced scaling mechanisms. Recent studies proposed single-application autoscaling solutions based on various technical approaches. However, for edge infrastructures with limited resource availability, it is essential to simultaneously manage heterogeneous application requirements, aiming at optimal resource allocation and minimal operational costs. This study introduces a multi-application hierarchical autoscaling framework for Kubernetes Edge Clusters. An application-based mechanism nominates the best applications’ deployments based on workload prediction and several criteria that guarantee the application’s performance while minimizing the infrastructure provider’s cost. For the joint application orchestration, an aggregation mechanism composes the candidate scaling solutions for the cluster. Then, a cluster autoscaling mechanism, based on the Analytic Hierarchy Process, undertakes the cluster’s scaling decision to optimize the resource allocation and energy consumption of the cluster. The evaluation illustrates the benefits of the proposed scaling strategy, achieving significant improvement in the average allocated resources and energy consumption compared to single-application approaches.

This paper aims to showcase the ability of the MATILDA Platform to enable vertical applications fully utilizing the capabilities offered by the fifth generation of mobile networks (5G). Although 5G, powered by network slicing and edge computing, promises to flexibly support radically new and extremely heterogeneous vertical applications, vertical stakeholders generally lack both the skills to exploit the full potentials of 5G networks and the vision of the underlying resources, owned by Telecom providers that are reluctant to expose them in an unmediated way. The MATILDA platform bridges the gap between the vertical application and the network service domains. This paper presents the case of an Industry 4.0 application, and highlights the role played by the MATILDA solution in its successful deployment and orchestration.

The Industry 4.0 sector is evolving in a tremendous pace by introducing a set of industrial automation mechanisms tightly coupled with the exploitation of Internet of Things (IoT), 5G and Artificial Intelligence (AI) technologies. By combining such emerging technologies, interconnected sensors, instruments, and other industrial devices are networked together with industrial applications, formulating the Industrial IoT (IIoT) and aiming to improve the efficiency and reliability of the deployed applications and provide Quality of Service (QoS) guarantees. However, in a 5G era, efficient, reliable and highly performant applications' provision has to be combined with exploitation of capabilities offered by 5G networks. Optimal usage of the available resources has to be realised, while guaranteeing strict QoS requirements such as high data rates, ultra-low latency and jitter. The first step towards this direction is based on the accurate profiling of vertical industries' applications in terms of resources usage, capacity limits and reliability characteristics. To achieve so, in this paper we provide an integrated methodology and approach for benchmarking and profiling 5G vertical industries' applications. This approach covers the realisation of benchmarking experiments and the extraction of insights based on the analysis of the collected data. Such insights are considered the cornerstones for the development of AI models that can lead to optimal infrastructure usage along with assurance of high QoS provision. The detailed approach is applied in a real IIoT use case, leading to profiling of a set of 5G network functions.

The Internet of Things (IoT) brings a multidisciplinary revolution in several application areas. However, security and privacy concerns are undermining a reliable and resilient broad-scale deployment of IoT-enabled critical infrastructures (IoT-CIs). To fill this gap, this paper proposes a comprehensive architectural design that captures the main security and privacy challenges related to cyber-physical systems and IoT-CIs. The architecture is devised to empower IoT systems and networks to make autonomous security decisions through the usage of novel technologies such as software defined networking and network function virtualization, as well as endowing them with intelligent and dynamic security reaction capabilities by relying on monitoring methodologies and cyber-situational tools. The architecture has been successfully implemented and evaluated in the scope of ANASTACIA H2020 EU research project.

The majority of enterprises are in the process of recognizing that business data analytics have the potential to transform their daily operations and make them extremely effective at addressing business challenges, identifying new market trends and embracing new ways to engage customers. Such analytics are in most cases related with the processing of data coming from various data sources that include structured and unstructured data. In order to get insight through the analysis results, appropriate input has to be provided that in many cases has to combine data from cross-sectorial and heterogeneous public or private data sources. Thus, there is inherent a need for applying novel techniques in order to harvest complex and heterogeneous datasets, turn them into insights and make decisions. In this paper, we present an approach for the production of added-value business analytics through the consumption of interlinked versions of data and the exploitation of linked data principles. Such interlinked data constitute valuable input for the initiation of an analytics extraction process and can lead to the realization of analysis that was not envisaged in the past. In addition to the production of analytics based on the consumption of linked data, the proposed approach supports the interlinking of the produced results with the associated input data, increasing in this way the value of the produced data and making them discoverable for further use in the future. The designed business analytics and data mining component is described in detail, along with an indicative application scenario combining data from the governmental, societal and health sectors.

Fueled by concern over the energy consumption of backbone networks, lot's of work has recently gone into proposals for energy-aware traffic engineering and routing. Local and network-wide policies have been developed for switching off network interfaces and concentrating traffic in as few links as allowed by SLA constraints. In this work we examine empirically the energy saving margins of such policies using extensive data from a national and a pan-European research and academic network. We analyze the dependence of such margins on several parameters, including the level of energy proportionality, QoS constraints and the geographic span of a network. Our findings reveal that with existing devices, smart powering-off can save more than 50% of currently consumed energy, and that energy-aware traffic engineering has still quite away to go before it can be made redundant by improvements in the energy proportionality of devices.

The European project ELISA (e-learning for improving access to information society for SMEs in the South East European Area) is an INTERREG CADSES project that involves 13 partners from 8 countries, coming from academia, government and the service sector, with the main objective of implementing combined training methods for improving the acceleration of e-business penetration in the target countries. In this paper we give a brief description of ELISA, we summarize the implementation of various technology enhanced training deliveries and we analyse the characteristics and functionality of the ELISA e-learning platform that was created based on the open-source learning management system (LMS) Moodle. We are trying to prove that, regardless the difficulties, technology enhanced learning can be efficiently used by small and medium enterprises (SMEs) and by governments who wish to create learning centres providing such training to local SMEs with reduced cost. Given that the 23 million SMEs represent the 99% of the European enterprises, this can prove to be a very efficient contribution to the Lisbon European strategy for the information society.

This paper presents a lightweight approach for providing web-based location aware multimedia content retrieval through Java enabled handheld devices. The main distinguishing characteristic of the proposed approach is that it separates the positioning system from the content access mechanisms, while being generic to the selection of the localization technology, i.e. GPS, Bluetooth, etc. Furthermore, it is built as an open, standards-based, modular architecture comprising a core of reusable components and interfaces for supporting different types of services, through web technologies. On-site services are provided through smart phones, which exploit the user's contextual state, mainly defined as end-user location and organization of points of interest. A test case of a museum e-guidance application for Bluetooth enabled smart phones is presented.