arXiv subjects

Panagiotis Oikonomou

Publications and source records attributed to Panagiotis Oikonomou.

9 recordsLinked to original sources

arXiv2026

Integrating Heterogeneous Digital Twins in Federated Ecosystems

Digital Twins (DTs) are increasingly used to virtualise physical systems at different scales, enabling monitoring, simulation, and predictions to support decision-making. However, while individual DTs are effective in stand-alone settings, ecosystem-scale deployments require multiple autonomous and distributed DTs to cooperate across system boundaries despite differences in modelling approaches or software technologies, making interoperability and runtime coordination critical challenges. Although \textit{Federated Digital Twin Ecosystems} have emerged as a promising direction, existing research remains at the conceptual stage, offering high-level architectures while leaving the practical integration of heterogeneous DTs underexplored. This paper proposes the \textit{Federation Node Manager}, a modular integration mechanism that connects local DTs to a federated environment through controlled capability exposure, protocol and schema adaptation, and timely state and event exchange for coordinated operations. We present a conceptual design and a prototype implementation, and demonstrate their feasibility in the smart mobility domain for emergency response scenarios. The proposed mechanism serves as an enabling component within a broader service-oriented federated DT ecosystem.

cs.SE↗

arXiv2026

TwinLoop: Simulation-in-the-Loop Digital Twins for Online Multi-Agent Reinforcement Learning

Decentralised online learning enables runtime adaptation in cyber-physical multi-agent systems, but when operating conditions change, learned policies often require substantial trial-and-error interaction before recovering performance. To address this, we propose TwinLoop, a simulation-in-the-loop digital twin framework for online multi-agent reinforcement learning. When a context shift occurs, the digital twin is triggered to reconstruct the current system state, initialise from the latest agent policies, and perform accelerated policy improvement with simulation what-if analysis before synchronising updated parameters back to the agents in the physical system. We evaluate TwinLoop in a vehicular edge computing task-offloading scenario with changing workload and infrastructure conditions. The results suggest that digital twins can improve post-shift adaptation efficiency and reduce reliance on costly online trial-and-error.

cs.LG↗

arXiv2025

A Digital Twin-based Multi-Agent Reinforcement Learning Framework for Vehicle-to-Grid Coordination

The coordination of large-scale, decentralised systems, such as a fleet of Electric Vehicles (EVs) in a Vehicle-to-Grid (V2G) network, presents a significant challenge for modern control systems. While collaborative Digital Twins have been proposed as a solution to manage such systems without compromising the privacy of individual agents, deriving globally optimal control policies from the high-level information they share remains an open problem. This paper introduces Digital Twin Assisted Multi-Agent Deep Deterministic Policy Gradient (DT-MADDPG) algorithm, a novel hybrid architecture that integrates a multi-agent reinforcement learning framework with a collaborative DT network. Our core contribution is a simulation-assisted learning algorithm where the centralised critic is enhanced by a predictive global model that is collaboratively built from the privacy-preserving data shared by individual DTs. This approach removes the need for collecting sensitive raw data at a centralised entity, a requirement of traditional multi-agent learning algorithms. Experimental results in a simulated V2G environment demonstrate that DT-MADDPG can achieve coordination performance comparable to the standard MADDPG algorithm while offering significant advantages in terms of data privacy and architectural decentralisation. This work presents a practical and robust framework for deploying intelligent, learning-based coordination in complex, real-world cyber-physical systems.

cs.DC↗

arXiv2025

Dynamic Service Scheduling and Resource Management in Energy-Harvesting Multi-access Edge Computing

Multi-access Edge Computing (MEC) delivers low-latency services by hosting applications near end-users. To promote sustainability, these systems are increasingly integrated with renewable Energy Harvesting (EH) technologies, enabling operation where grid electricity is unavailable. However, balancing the intermittent nature of harvested energy with dynamic user demand presents a significant resource allocation challenge. This work proposes an online strategy for an MEC system powered exclusively by EH to address this trade-off. Our strategy dynamically schedules computational tasks with dependencies and governs energy consumption through real-time decisions on server frequency scaling and service module migration. Experiments using real-world datasets demonstrate our algorithm's effectiveness in efficiently utilizing harvested energy while maintaining low service latency.

cs.DC↗

arXiv2025

Dark counts in optical superconducting transition-edge sensors for rare-event searches

Superconducting transition-edge sensors (TESs) are a type of quantum sensor known for its high single-photon detection efficiency and low background. This makes them ideal for particle physics experiments searching for rare events. In this work, we present a comprehensive characterization of the background in optical TESs, distinguishing three types of events: electrical-noise, high-energy, and photonlike events. We introduce computational methods to automate the classification of events. For the first time, we experimentally verify and simulate the source of the high-energy events. We also isolate the photonlike events, the expected signal in dielectric haloscopes searching for dark matter dark photons, and achieve a record-low photonlike dark-count rate of $3.6 \times 10^{-4}$ Hz in the 0.8-3.2 eV energy range.

physics.ins-det↗

arXiv2022

Acoustic detection potential of single minimum ionizing particles in viscous liquids

An ionizing particle passing through a liquid generates acoustic signals via local heat deposition. We delve into modeling such acoustic signals in the case of a single particle that interacts with the liquid electromagnetically in a generic way. We present a systematic way of introducing corrections due to viscosity using a perturbative approach so that our solution is valid at large distances from the interaction point. A computational simulation framework to perform the calculations described is also provided. The methodology developed is then applied to predict the acoustic signal of relativistic muons in various liquids as a toy model.

physics.class-ph↗

arXiv2020

On the Road from Edge Computing to the Edge Mesh

Nowadays, we are witnessing the advent of the Internet of Things (EC) with numerous devices performing interactions between them or with end users. The huge number of devices leads to huge volumes of collected data that demand the appropriate processing. The 'legacy' approach is to rely on Cloud where increased computational resources can be adopted to realize any processing. However, even if the communication with the Cloud back end lasts for some seconds there are cases where problems in the network or the need for supporting real time applications require a reduced latency in the provision of responses/outcomes. Edge Computing (EC) comes into the scene as the 'solver' of the latency problem (and not only). Any processing can be performed close to data sources, i.e., at EC nodes having direct connection with IoT devices. Hence, an ecosystem of processing nodes can be present at the edge of the network giving the opportunity to apply novel services upon the collected data. Various challenges should be met before we talk about a fully automated ecosystem where EC nodes can cooperate or understand the status of them and the environment to be capable of efficiently serving end users or applications. In this paper, we perform a survey of the relevant research activities targeting to support the vision of Edge Mesh (EM), i.e., a 'cover' of intelligence upon the EC infrastructure. We present all the parts of the EC/EM framework starting from the necessary hardware and discussing research outcomes in every aspect of EC nodes functioning. We present technologies and theories adopted for data, tasks and resource management while discussing how (deep) machine learning and optimization techniques are adopted to solve various problems. Our aim is to provide a starting point for novel research to conclude efficient services/applications opening up the path to realize the future EC form.

cs.DC↗

arXiv2020

A Fuzzy Logic Controller for Tasks Scheduling Using Unreliable Cloud Resources

The Cloud infrastructure offers to end users a broad set of heterogenous computational resources using the pay-as-you-go model. These virtualized resources can be provisioned using different pricing models like the unreliable model where resources are provided at a fraction of the cost but with no guarantee for an uninterrupted processing. However, the enormous gamut of opportunities comes with a great caveat as resource management and scheduling decisions are increasingly complicated. Moreover, the presented uncertainty in optimally selecting resources has also a negatively impact on the quality of solutions delivered by scheduling algorithms. In this paper, we present a dynamic scheduling algorithm (i.e., the Uncertainty-Driven Scheduling - UDS algorithm) for the management of scientific workflows in Cloud. Our model minimizes both the makespan and the monetary cost by dynamically selecting reliable or unreliable virtualized resources. For covering the uncertainty in decision making, we adopt a Fuzzy Logic Controller (FLC) to derive the pricing model of the resources that will host every task. We evaluate the performance of the proposed algorithm using real workflow applications being tested under the assumption of different probabilities regarding the revocation of unreliable resources. Numerical results depict the performance of the proposed approach and a comparative assessment reveals the position of the paper in the relevant literature.

cs.DC↗

arXiv2020

On the Use of Interpretable Machine Learning for the Management of Data Quality

Data quality is a significant issue for any application that requests for analytics to support decision making. It becomes very important when we focus on Internet of Things (IoT) where numerous devices can interact to exchange and process data. IoT devices are connected to Edge Computing (EC) nodes to report the collected data, thus, we have to secure data quality not only at the IoT but also at the edge of the network. In this paper, we focus on the specific problem and propose the use of interpretable machine learning to deliver the features that are important to be based for any data processing activity. Our aim is to secure data quality, at least, for those features that are detected as significant in the collected datasets. We have to notice that the selected features depict the highest correlation with the remaining in every dataset, thus, they can be adopted for dimensionality reduction. We focus on multiple methodologies for having interpretability in our learning models and adopt an ensemble scheme for the final decision. Our scheme is capable of timely retrieving the final result and efficiently select the appropriate features. We evaluate our model through extensive simulations and present numerical results. Our aim is to reveal its performance under various experimental scenarios that we create varying a set of parameters adopted in our mechanism.

cs.LG↗