12 research outputs found
A Trustable and Interoperable Decentralized Solution for Citizen-Centric and Cross-Border eGovernance: A Conceptual Approach
No full textPart 5: Data Analytics, Decision Making, and Artificial IntelligenceInternational audienceAiming to support a cross-sector and cross-border eGovernance paradigm for sharing common public services, this paper introduces an AI-enhanced solution that enables beneficiaries to participate in a decentralized network for effective big data exchange and service delivery that promotes the once-only priority and is by design digital, efficient, cost-effective, interoperable and secure. The solution comprises (i) a reliable and efficient decentralized mechanism for data sharing, capable of addressing the complexity of the processes and their high demand of resources; (ii) an ecosystem for delivering mobile services tailored to the needs of stakeholders; (iii) a single sign-on Wallet mechanism to manage transactions with multiple services; and (iv) an intercommunication layer, responsible for the secure exchange of information among existing eGovernment systems with newly developed ones. An indicative application scenario showcases the potential of our approach
A Comparison of Computational and Experimental Fluid Dynamics Studies between Scaled and Original Wing Sections, in Single-Phase and Two-Phase Flows, and Evaluation of the Suggested Method
No full textThe correlation between computational fluid dynamics (CFD) and experimental fluid dynamics (EFD) is crucial for the behavior prediction of aerodynamic bodies. This paper’s objective is twofold: (1) to develop a method that approaches commercial CFD codes and their link with EFD in a more efficient way, using a downscaled model, and (2) to investigate the effect of rain on the aerodynamic behavior of a wing. More specifically, we investigate the one-phase and two-phase flow over a typical wing section NACA 641-212 airfoil, in the commercial code Ansys Fluent. Two computational models were developed; the first model represents the original dimensions of the wing, while the second is downscaled to 23% of the original. The response of the models in air and air–water flow were primarily studied, as well as the impact on aerodynamic efficiency due to the existence of the second phase. For the computational fluid dynamics simulations, a pressure-based solver with a second-order upwind scheme for the spatial discretization and the Spalart–Allmaras (SA) turbulence model were utilized. Meanwhile, for the two-phase flow of air–water, the discrete phase model (DPM) with wall–film boundary conditions on the surface of the wing and two-way coupling between continuous and discrete phase was considered. The second phase was simulated as water droplets injected in the continuous phase, in a Euler–Lagrange approach. The experimental model was constructed in accordance with the downscaled model and tested in a subsonic wind tunnel, using 3D printing technology which reduced the experiment expenses. The presence of water in two-phase flow was proven to deteriorate the aerodynamic factors of the wing compared to one-phase flow, as expected. The three-stage comparison of CFD and EFD results showed a very good convergence, in both single and two-phase flow. This can lead to the conclusion that a rapid and low-cost study for the estimation of the aerodynamic performance of objects with high accuracy is feasible with the suggested method
Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions
No full textAutonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophisticated technologies, including sensors, cameras, GPS, radar, light imaging detection and ranging (LiDAR), and advanced computing systems. These components work in concert to accurately perceive the vehicle’s environment, ensuring the capacity to make optimal decisions in real-time. At the heart of AV functionality lies the ability to facilitate intercommunication between vehicles and with critical road infrastructure—a characteristic that, while central to their efficacy, also renders them susceptible to cyber threats. The potential infiltration of these communication channels poses a severe threat, enabling the possibility of personal information theft or the introduction of malicious software that could compromise vehicle safety. This paper offers a comprehensive exploration of the current state of AV technology, particularly examining the intersection of autonomous vehicles and emotional intelligence. We delve into an extensive analysis of recent research on safety lapses and security vulnerabilities in autonomous vehicles, placing specific emphasis on the different types of cyber attacks to which they are susceptible. We further explore the various security solutions that have been proposed and implemented to address these threats. The discussion not only provides an overview of the existing challenges but also presents a pathway toward future research directions. This includes potential advancements in the AV field, the continued refinement of safety measures, and the development of more robust, resilient security mechanisms. Ultimately, this paper seeks to contribute to a deeper understanding of the safety and security landscape of autonomous vehicles, fostering discourse on the intricate balance between technological advancement and security in this rapidly evolving field
TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
No full textIn the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI
FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE
No full textIn this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data management while simultaneously reinforcing privacy across broad network configurations. Our FLIBD architecture was thoughtfully designed to safeguard data and model privacy through a synergistic integration of distributed model training and secure model consolidation. Notably, we delved into an in-depth examination of adversarial activities within federated learning contexts. The Federated Adversarial Attack for Multi-Task Learning (FAAMT) was thoroughly assessed, unmasking its proficiency in showcasing and exploiting vulnerabilities across various federated learning approaches. Moreover, we offer an incisive evaluation of numerous federated learning defence mechanisms, including Romoa and RFA, in the scope of the FAAMT. Utilising well-defined evaluation metrics and analytical processes, our study demonstrated a resilient framework suitable for managing IoT Big Data across widespread deployments, while concurrently presenting a solid contribution to the progression and discussion surrounding defensive methodologies within the federated learning and IoT areas
The extreme heat wave of July–August 2021 in the Athens urban area (Greece): Atmospheric and human-biometeorological analysis exploiting ultra-high resolution numerical modeling and the local climate zone framework
No full textGreece was affected by a prolonged and extreme heat wave (HW) event (July 28–August 05) during the abnormally hot summer of 2021, with the maximum temperature in Athens, the capital of the country, reaching up to 43.9 °C in the city center. This observation corresponds to the second highest maximum temperature recorded since 1900, based on the historical temperature time series of the National Observatory of Athens weather station at Thissio. In the present study, a multi-scale numerical modeling system is used to analyze the urban climate and thermal bioclimate in the Athens urban area (AUA) in the course of the HW event, as well as during 3 days prior to the heat wave and 3 days after the episode. The system consists of the Weather Research and Forecasting model, the advanced urban scheme BEP/BEM (Building Energy Parameterization/Building Energy Model) and the human-biometeorological model RayMan Pro, and incorporates the local climate zone (LCZ) classification scheme. The system's validation results demonstrated a robust modeling set-up, characterized by high capability in capturing the observed magnitude and diurnal variation of the urban meteorological and heat stress conditions. The analysis of two- and three-dimensional fields of near-surface air temperature, humidity and wind unraveled the interplay of geographical factors (surface relief and proximity to the sea), background atmospheric circulations (Etesians and sea breeze) and HW-related synoptic forcing with the AUA's urban form. These interactions had a significant impact on the LCZs heat stress responsiveness, expressed using the modified physiologically equivalent temperature (mPET), between different regions of the study area, as well as at inter- and intra-LCZ level (statistically significant differences at 95 % confidence interval), providing thus, urban design and health-related implications that can be exploited in human thermal discomfort mitigation strategies in AUA. © 2022 The Author
Development of an operational modeling system for urban heat islands: An application to Athens, Greece
No full textThe urban heat island (UHI) effect is one prominent form of localized anthropogenic climate modification. It represents a significant urban climate problem since it occurs in the layer of the atmosphere where almost all daily human activities take place. This paper presents the development of a high-resolution modeling system that could be used for simulating the UHI effect in the context of operational weather forecasting activities. The modeling system is built around a state-of-the-art numerical weather prediction model, properly modified to allow for the better representation of the urban climate. The model performance in terms of simulating the near-surface air temperature and thermal comfort conditions over the complex urban area of Athens, Greece, is evaluated during a 1.5-month operational implementation in the summer of 2010. Results from this case study reveal an overall satisfactory performance of the modeling system. The discussion of the results highlights the important role that, given the necessary modifications, a meteorological model can play as a supporting tool for developing successful heat island mitigation strategies. This is further underlined through the operational character of the presented modeling system. © Author(s) 2014
DISARM Early Warning System for Wildfires in the Eastern Mediterranean
No full textThis paper discusses the main achievements of DISARM (Drought and fIre ObServatory and eArly waRning system) project, which developed an early warning system for wildfires in the Eastern Mediterranean. The four pillars of this system include (i) forecasting wildfire danger, (ii) detecting wildfires with remote sensing techniques, (iii) forecasting wildfire spread with a coupled weather-fire modeling system, and (iv) assessing the wildfire risk in the frame of climate change. Special emphasis is given to the innovative and replicable parts of the system. It is shown that for the effective use of fire weather forecasting in different geographical areas and in order to account for the local climate conditions, a proper adjustment of the wildfire danger classification is necessary. Additionally, the consideration of vegetation dryness may provide better estimates of wildfire danger. Our study also highlights some deficiencies of both EUMETSAT (Exploitation of Meteorological Satellites) and LSA-SAF (Satellite Application Facility on Land Surface Analysis) algorithms in their skill to detect wildfires over islands and near the coastline. To tackle this issue, a relevant modification is proposed. Furthermore, it is shown that IRIS, the coupled atmosphere-fire modeling system developed in the frame of DISARM, has proven to be a valuable supporting tool in fire suppression actions. Finally, assessment of the wildfire danger in the future climate provides the necessary context for the development of regional adaptation strategies to climate change
