arXiv subjects

Ionut Anghel

Publications and source records attributed to Ionut Anghel.

18 recordsLinked to original sources

arXiv2026

SAREF-based Ontology for Distributed AI Workflows across the Edge-Fog-Cloud Continuum

Nowadays semantic models provide limited support for representing distributed AI workflows and their execution across heterogeneous edge, fog, and cloud environments. Therefore, AI processes and resources are often described using incompatible semantic representations, affecting the interoperability, orchestration, and reuse. To address these challenges, this paper proposes a SAREF-compliant ontology for representing distributed AI workflows across the edge-fog-cloud continuum. We extend the SAREF4SYST ontology with concepts for modeling AI pipelines, executable AI jobs, computational resources, deployment constraints, and communication relationships, providing a unified semantic model of both AI workflows and heterogeneous computing infrastructures. The ontology enables semantic interoperability, automated reasoning, and resource-aware orchestration of distributed AI applications while remaining fully aligned with the ETSI SAREF ecosystem. The ontology is evaluated using proof-of-concept smart grid energy services orchestration scenarios and validated using competency questions showing its ability to support AI workflow deployment, execution reasoning, and workload adaptation across heterogeneous edge, fog, and cloud environments. All competency questions were successfully validated using SPARQL querying and semantic reasoning. Experimental results demonstrate deployment success rates of 90-100% with average orchestration decision times below 80 ms across heterogeneous edge-fog-cloud environments, highlighting its effectiveness on ensuring semantic interoperability for distributed AI orchestration.

cs.AI↗

arXiv2026

A systematic review of generative AI usage for IT project management

This paper aims to synthesize current knowledge on generative AI in IT project management using the PRISMA methodology to provide researchers with a comprehensive perspective on techniques, applications, adoption trends, limitations, and integration across project management tools and process groups. The analysis reveals a clear dominance of OpenAI's GPT in the included studies but relying primarily on prompt engineering, suggesting that research in this area remains at an exploratory stage. Finally, it identifies and discusses three promising research directions for AI-enabled project management, including process group-specific AI agents, project role-based AI agents, and hybrid collaborative networks that enable human-guided orchestration.

cs.SE↗

arXiv2026

Performance Evaluation of LLMs in Automated RDF Knowledge Graph Generation

Cloud systems generate large, heterogeneous log data containing critical infrastructure, application, and security information. Transforming these logs into RDF triples enables their integration into knowledge graphs, improving interpretability, root-cause analysis, and cross-service reasoning beyond what raw logs allow. Large Language Models (LLMs) offer a promising approach to automate RDF knowledge graph generation; however, their effectiveness on complex cloud logs remains largely unexplored. In this paper, we evaluate multiple LLM architectures and prompting strategies for automated RDF extraction using a controlled framework with two pipelines for systematically processing semi-structured log data. The extraction pipeline integrates multiple LLMs to identify relevant entities and relationships, automatically generating subject-predicate-object triples. These outputs are evaluated using a dedicated validation pipeline with both syntactic and semantic metrics to assess accuracy, completeness, and quality. Due to the lack of public ground-truth datasets, we created a reference Log-to-KG dataset from OpenStack logs using manual annotation and ontology-driven methods, enabling objective baseline. Our analysis shows that Few-Shot learning is the most effective strategy, with Llama achieving a 99.35% F1 score and 100% valid RDF output while Qwen, NuExtract, and Gemma also perform well under Few-Shot prompting, with Chain-of-Thought approaches maintaining similar accuracy. One-Shot prompting offers a lighter but effective alternative, while Zero-Shot and advanced strategies such as Tree-of-Thought, Self-Critique, and Generate-Multiple perform substantially worse. These results highlight the importance of contextual examples and prompt design for accurate RDF extraction and reveal model-specific limitations across LLM architectures.

cs.IR↗

arXiv2025

A Laplace diffusion-based transformer model for heart rate forecasting within daily activity context

With the advent of wearable Internet of Things (IoT) devices, remote patient monitoring (RPM) emerged as a promising solution for managing heart failure. However, the heart rate can fluctuate significantly due to various factors, and without correlating it to the patient's actual physical activity, it becomes difficult to assess whether changes are significant. Although Artificial Intelligence (AI) models may enhance the accuracy and contextual understanding of remote heart rate monitoring, the integration of activity data is still rarely addressed. In this paper, we propose a Transformer model combined with a Laplace diffusion technique to model heart rate fluctuations driven by physical activity of the patient. Unlike prior models that treat activity as secondary, our approach conditions the entire modeling process on activity context using specialized embeddings and attention mechanisms to prioritize activity specific historical patents. The model captures both long-term patterns and activity-specific heart rate dynamics by incorporating contextualized embeddings and dedicated encoder. The Transformer model was validated on a real-world dataset collected from 29 patients over a 4-month period. Experimental results show that our model outperforms current state-of-the-art methods, achieving a 43% reduction in mean absolute error compared to the considered baseline models. Moreover, the coefficient of determination R2 is 0.97 indicating the model predicted heart rate is in strong agreement with actual heart rate values. These findings suggest that the proposed model is a practical and effective tool for supporting both healthcare providers and remote patient monitoring systems.

cs.LG↗

arXiv2025

Microgrids Coalitions for Energy Market Balancing

With the integration of renewable sources in electricity distribution networks, the need to develop intelligent mechanisms for balancing the energy market has arisen. In the absence of such mechanisms, the energy market may face imbalances that can lead to power outages, financial losses or instability at the grid level. In this context, the grouping of microgrids into optimal coalitions that can absorb energy from the market during periods of surplus or supply energy to the market during periods of is a key aspect in the efficient management of distribution networks. In this article, we propose a method that identify an optimal microgrids coalition capable of addressing the dynamics of the energy market. The proposed method models the problem of identifying the optimal coalition as an optimization problem that it solves by combining a strategy inspired by cooperative game theory with a memetic algorithm. An individual is represented as a coalition of microgrids and the evolution of population of individuals over generations is assured by recombination and mutation. The fitness function is defined as the difference between the total value generated by the coalition and a penalty applied to the coalition when the energy traded by coalition exceeds the energy available/demanded on/by the energy market. The value generated by the coalition is calculated based on the profit obtained by the collation if it sells energy on the market during periods of deficit or the savings obtained by the coalition if it buys energy on the market during periods of surplus and the costs associated with the trading process. This value is divided equitably among the coalition members, according to the Shapley value, which considers the contribution of each one to the formation of collective value.

cs.GT↗

arXiv2025

From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins

Artificial intelligence (AI) has long promised to improve energy management in smart grids by enhancing situational awareness and supporting more effective decision-making. While traditional machine learning has demonstrated notable results in forecasting and optimization, it often struggles with generalization, situational awareness, and heterogeneous data integration. Recent advances in foundation models such as Transformer architecture and Large Language Models (LLMs) have demonstrated improved capabilities in modelling complex temporal and contextual relationships, as well as in multi-modal data fusion which is essential for most AI applications in the energy sector. In this review we synthesize the rapid expanding field of AI applications in the energy domain focusing on Transformers and LLMs. We examine the architectural foundations, domain-specific adaptations and practical implementations of transformer models across various forecasting and grid management tasks. We then explore the emerging role of LLMs in the field: adaptation and fine tuning for the energy sector, the type of tasks they are suited for, and the new challenges they introduce. Along the way, we highlight practical implementations, innovations, and areas where the research frontier is rapidly expanding. These recent developments reviewed underscore a broader trend: Generative AI (GenAI) is beginning to augment decision-making not only in high-level planning but also in day-to-day operations, from forecasting and grid balancing to workforce training and asset onboarding. Building on these developments, we introduce the concept of the Agentic Digital Twin, a next-generation model that integrates LLMs to bring autonomy, proactivity, and social interaction into digital twin-based energy management systems.

cs.LG↗

arXiv2025

Evolutionary model for energy trading in community microgrids using Hawk-Dove strategies

This paper proposes a decentralized model of energy cooperation between microgrids, in which decisions are made locally, at the level of the microgrid community. Each microgrid is modeled as an autonomous agent that adopts a Hawk or Dove strategy, depending on the level of energy stored in the battery and its role in the energy trading process. The interactions between selling and buying microgrids are modeled through an evolutionary algorithm. An individual in the algorithm population is represented as an energy trading matrix that encodes the amounts of energy traded between the selling and buying microgrids. The population evolution is achieved by recombination and mutation operators. Recombination uses a specialized operator for matrix structures, and mutation is applied to the matrix elements according to a Gaussian distribution. The evaluation of an individual is made with a multi-criteria fitness function that considers the seller profit, the degree of energy stability at the community level, penalties for energy imbalance at the community level and for the degradation of microgrids batteries. The method was tested on a simulated scenario with 100 microgrids, each with its own selling and buying thresholds, to reflect a realistic environment with variable storage characteristics of microgrids batteries. By applying the algorithm on this scenario, 95 out of the 100 microgrids reached a stable energy state. This result confirms the effectiveness of the proposed model in achieving energy balance both at the individual level, for each microgrid, and at the level of the entire community.

cs.NE↗

arXiv2025

New care pathways for supporting transitional care from hospitals to home using AI and personalized digital assistance

Transitional care may play a vital role for the sustainability of Europe future healthcare system, offering solutions for relocating patient care from hospital to home therefore addressing the growing demand for medical care as the population is ageing. However, to be effective, it is essential to integrate innovative Information and Communications Technology technologies to ensure that patients with comorbidities experience a smooth and coordinated transition from hospitals or care centers to home, thereby reducing the risk of rehospitalization. In this paper, we present an overview of the integration of Internet of Things, artificial intelligence, and digital assistance technologies with traditional care pathways to address the challenges and needs of healthcare systems in Europe. We identify the current gaps in transitional care and define the technology mapping to enhance the care pathways, aiming to improve patient outcomes, safety, and quality of life avoiding hospital readmissions. Finally, we define the trial setup and evaluation methodology needed to provide clinical evidence that supports the positive impact of technology integration on patient care and discuss the potential effects on the healthcare system.

cs.HC↗

arXiv2024

Edge Offloading in Smart Grid

The energy transition supports the shift towards more sustainable energy alternatives, paving towards decentralized smart grids, where the energy is generated closer to the point of use. The decentralized smart grids foresee novel data-driven low latency applications for improving resilience and responsiveness, such as peer-to-peer energy trading, microgrid control, fault detection, or demand response. However, the traditional cloud-based smart grid architectures are unable to meet the requirements of the new emerging applications such as low latency and high-reliability thus alternative architectures such as edge, fog, or hybrid need to be adopted. Moreover, edge offloading can play a pivotal role for the next-generation smart grid AI applications because it enables the efficient utilization of computing resources and addresses the challenges of increasing data generated by IoT devices, optimizing the response time, energy consumption, and network performance. However, a comprehensive overview of the current state of research is needed to support sound decisions regarding energy-related applications offloading from cloud to fog or edge, focusing on smart grid open challenges and potential impacts. In this paper, we delve into smart grid and computational distribution architec-tures, including edge-fog-cloud models, orchestration architecture, and serverless computing, and analyze the decision-making variables and optimization algorithms to assess the efficiency of edge offloading. Finally, the work contributes to a comprehensive understanding of the edge offloading in smart grid, providing a SWOT analysis to support decision making.

cs.NI↗

arXiv2024

A Deep Q-Learning based Smart Scheduling of EVs for Demand Response in Smart Grids

Economic and policy factors are driving the continuous increase in the adoption and usage of electrical vehicles (EVs). However, despite being a cleaner alternative to combustion engine vehicles, EVs have negative impacts on the lifespan of microgrid equipment and energy balance due to increased power demand and the timing of their usage. In our view grid management should leverage on EVs scheduling flexibility to support local network balancing through active participation in demand response programs. In this paper, we propose a model-free solution, leveraging Deep Q-Learning to schedule the charging and discharging activities of EVs within a microgrid to align with a target energy profile provided by the distribution system operator. We adapted the Bellman Equation to assess the value of a state based on specific rewards for EV scheduling actions and used a neural network to estimate Q-values for available actions and the epsilon-greedy algorithm to balance exploitation and exploration to meet the target energy profile. The results are promising showing that the proposed solution can effectively schedule the EVs charging and discharging actions to align with the target profile with a Person coefficient of 0.99, handling effective EVs scheduling situations that involve dynamicity given by the e-mobility features, relying only on data with no knowledge of EVs and microgrid dynamics.

cs.LG↗

arXiv2023

Electric Vehicles coordination for grid balancing using multi-objective Harris Hawks Optimization

The rise of renewables coincides with the shift towards Electrical Vehicles (EVs) posing technical and operational challenges for the energy balance of the local grid. Nowadays, the energy grid cannot deal with a spike in EVs usage leading to a need for more coordinated and grid aware EVs charging and discharging strategies. However, coordinating power flow from multiple EVs into the grid requires sophisticated algorithms and load-balancing strategies as the complexity increases with more control variables and EVs, necessitating large optimization and decision search spaces. In this paper, we propose an EVs fleet coordination model for the day ahead aiming to ensure a reliable energy supply and maintain a stable local grid, by utilizing EVs to store surplus energy and discharge it during periods of energy deficit. The optimization problem is addressed using Harris Hawks Optimization (HHO) considering criteria related to energy grid balancing, time usage preference, and the location of EV drivers. The EVs schedules, associated with the position of individuals from the population, are adjusted through exploration and exploitation operations, and their technical and operational feasibility is ensured, while the rabbit individual is updated with a non-dominated EV schedule selected per iteration using a roulette wheel algorithm. The solution is evaluated within the framework of an e-mobility service in Terni city. The results indicate that coordinated charging and discharging of EVs not only meet balancing service requirements but also align with user preferences with minimal deviations.

cs.AI↗

arXiv2023

FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Prediction

Privacy is important when dealing with sensitive personal information in machine learning models, which require large data sets for training. In the energy field, access to household prosumer energy data is crucial for energy predictions to support energy grid management and large-scale adoption of renewables however citizens are often hesitant to grant access to cloud-based machine learning models. Federated learning has been proposed as a solution to privacy challenges however report issues in generating the global prediction model due to data heterogeneity, variations in generation patterns, and the high number of parameters leading to even lower prediction accuracy. This paper addresses these challenges by introducing FedWOA a novel federated learning model that employs the Whale Optimization Algorithm to aggregate global prediction models from the weights of local LTSM neural network models trained on prosumer energy data. The proposed solution identifies the optimal vector of weights in the search spaces of the local models to construct the global shared model and then is subsequently transmitted to the local nodes to improve the prediction quality at the prosumer site while for handling non-IID data K-Means was used for clustering prosumers with similar scale of energy data. The evaluation results on prosumers energy data have shown that FedWOA can effectively enhance the accuracy of energy prediction models accuracy by 25% for MSE and 16% for MAE compared to FedAVG while demonstrating good convergence and reduced loss.

cs.LG↗

arXiv2023

Social Factors in P2P Energy Trading Using Hedonic Games

Lately, the energy communities have gained a lot of attention as they have the potential to significantly contribute to the resilience and flexibility of the energy system, facilitating widespread integration of intermittent renewable energy sources. Within these communities the prosumers can engage in peer-to-peer trading, fostering local collaborations and increasing awareness about energy usage and flexible consumption. However, even under these favorable conditions, prosumer engagement levels remain low, requiring trading mechanisms that are aligned with their social values and expectations. In this paper, we introduce an innovative hedonic game coordination and cooperation model for P2P energy trading among prosumers which considers the social relationships within an energy community to create energy coalitions and facilitate energy transactions among them. We defined a heuristic that optimizes the prosumers coalitions, considering their social and energy price preferences and balancing the energy demand and supply within the community. We integrated the proposed hedonic game model into a state-of-the-art blockchain-based P2P energy flexibility market and evaluated its performance within an energy community of prosumers. The evaluation results on a blockchain-based P2P energy flexibility market show the effectiveness in considering social factors when creating coalitions, increasing the total amount of energy transacted in a market session by 5% compared with other game theory-based solutions. Finally, it shows the importance of the social dimensions of P2P energy transactions, the positive social dynamics in the energy community increasing the amount of energy transacted by more than 10% while contributing to a more balanced energy demand and supply within the community.

cs.DC↗

arXiv2021

A Lockable ERC20 Token for Peer to Peer Energy Trading

In this paper, we address the digitization of physical assets using blockchain technology focusing on energy and peer-to-peer trading on decentralized energy markets. Because they are forward markets and operate on a day-ahead timeline, the energy transactions are settled only at the movement of energy delivery. Having the option of locking the energy tokens by a third-party escrow becomes a highly desirable feature. Thus, we define a Lockable ERC20 token that provides the option for an owner to lock some of its tokens using smart contracts. A time-lock and an escrow party account or smart contract can be specified allowing the tokens to be unlocked when certain business conditions are met. For validation purposes, we have considered a peer-to-peer energy trading scenario in which the Lockable ERC20 token was used to digitize the surplus of energy of prosumers. In this case, the energy tokens committed in blockchain transactions are successfully locked up until the actual delivery of energy, the settlement considering the monitored data of energy meters.

cs.DC↗

arXiv2021

An Overview of Digital Twins Application Domains in Smart Energy Grid

The Digital Twins offer promising solutions for smart grid challenges related to the optimal operation, management, and control of energy assets, for safe and reliable distribution of energy. These challenges are more pressing nowadays than ever due to the large-scale adoption of distributed renewable resources at the edge of the grid. The digital twins are leveraging technologies such as the Internet of Things, big data analytics, machine learning, and cloud computing, to analyze data from different energy sensors, view and verify the status of physical energy assets and extract useful information to predict and optimize the assets performance. In this paper, we will provide an overview of the Digital Twins application domains in the smart grid while analyzing existing the state of the art literature. We have focused on the following application domains: energy asset modeling, fault and security diagnosis, operational optimization, and business models. Most of the relevant literature approaches found are published in the last three years showing that the domain of Digital Twins application in smart grid is hot and gradually developing. Anyway, there is no unified view on the Digital Twins implementation and integration with energy management processes, thus, much work still needs to be done to understand and automatize the smart grid management.

eess.SY↗

arXiv2021

Blockchain platform for COVID-19 vaccine supply management

In the context of the COVID-19 pandemic, the rapid roll-out of a vaccine and the implementation of a worldwide immunization campaign is critical, but its success will depend on the availability of an operational and transparent distribution chain that can be audited by all relevant stakeholders. In this paper, we discuss how blockchain technology can be used for assuring the transparent tracing of COVID-19 vaccine registration, storage and delivery, and side effects self-reporting. We present such system implementation in which blockchain technology is used for assuring data integrity and immutability in case of beneficiary registration for vaccination, eliminating identity thefts and impersonations. Smart contracts are defined to monitor and track the proper vaccine distribution conditions against the safe handling rules defined by vaccine producers enabling the awareness of all network peers. For vaccine administration, a transparent and tamper-proof side effects self-reporting solution is provided considering person identification and administrated vaccine association. A prototype was implemented using the Ethereum test network, Ropsten, considering the COVID-19 vaccine distribution tracking conditions. The results obtained for each on-chain operation can be checked and validated on the Etherscan, demonstrating various aspects of the proposed system such as immunization actors and safe rules registration, vaccine tracking, and administration. In terms of throughput and scalability, the proposed blockchain system shows promising results.

cs.DC↗

arXiv2020

Smart Grid Management using Blockchain: Future Scenarios and Challenges

Decentralized management and coordination of energy systems are emerging trends facilitated by the uptake of the Internet of Things and Blockchain offering new opportunities for more secure, resilient, and efficient energy distribution. Even though the use of distributed ledger technology in the energy domain is promising, the development of decentralized smart grid management solutions is in the early stages. In this paper, we define a layered architecture of a blockchain-based smart grid management platform featuring energy data metering and tamper-proof registration, business enforcement via smart contracts, and Oracle-based integration of high computational services supporting the implementation of future grid management scenarios. Three such scenarios are discussed from the perspective of their implementation using the proposed blockchain platform and associated challenges: peer to peer energy trading, decentralized management, and aggregation of energy flexibility and operation of community oriented Virtual Power Plants.

cs.DC↗

arXiv2020

Blockchain based Decentralized Applications: Technology Review and Development Guidelines

Blockchain or Distributed Ledger Technology is a disruptive technology that provides the infrastructure for developing decentralized applications enabling the implementation of novel business models even in traditionally centralized domains. In the last years it has drawn high interest from the academic community, technology developers and startups thus lots of solutions have been developed to address blockchain technology limitations and the requirements of applications software engineering. In this paper, we provide a comprehensive overview of DLT solutions analyzing the addressed challenges, provided solutions and their usage for developing decentralized applications. Our study reviews over 100 blockchain papers and startup initiatives from which we construct a 3-tier based architecture for decentralized applications and we use it to systematically classify the technology solutions. Protocol and Network Tier solutions address the digital assets registration, transactions, data structure, and privacy and business rules implementation and the creation of peer-to-peer networks, ledger replication, and consensus-based state validation. Scaling Tier solutions address the scalability problems in terms of storage size, transaction throughput, and computational capability. Finally, Federated Tier aggregates integrative solutions across multiple blockchain applications deployments. The paper closes with a discussion on challenges and opportunities for developing decentralized applications by providing a multi-step guideline for decentralizing the design of traditional systems and implementing decentralized applications.

cs.DC↗