Augmented Reality can significantly enhance and highlight cultural tourism by providing an immersive experience. This study focuses on the evaluation of the “Virtual Street Museum” AR mobile application which enables the user to experience the ancient topography of Athens from a different aspect. This study begins with introducing cultural tourism and the impact AR can have on it. Then, some related work is presented. The third section briefly presents the “Virtual Street Museum” AR application while the fourth section acts as a short survey of the most widely used evaluation tools. Having outlined the chosen evaluation tool for the current application, the fifth section presents its results while in the final section conclusions and future work are presented. This study’s findings highlight the potential of utilizing AR technology in cultural tourism.

Background: Explainable Artificial Intelligence (XAI) is deployed in Internet of Things (IoT) ecosystems for smart cities and precision agriculture, where opaque models can compromise trust, accountability, and regulatory compliance. Objective: This survey investigates how XAI is currently integrated into distributed and federated IoT architectures and identifies systematic gaps in evaluation under real-world resource constraints. Methods: A structured search across IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Google Scholar targeted publications related to XAI, IoT, edge/fog computing, smart cities, smart agriculture, and federated learning. Relevant peer-reviewed works were synthesized along three dimensions: deployment tier (device, edge/fog, cloud), explanation scope (local vs. global), and validation methodology. Results: The analysis reveals a persistent resource–interpretability gap: computationally intensive explainers are frequently applied on constrained edge and federated platforms without explicitly accounting for latency, memory footprint, or energy consumption. Only a minority of studies quantify privacy–utility effects or address causal attribution in sensor-rich environments, limiting the reliability of explanations in safety- and mission-critical IoT applications. Contribution: To address these shortcomings, the survey introduces a hardware-centric evaluation framework with the Computational Complexity Score (CCS), Memory Footprint Ratio (MFR), and Privacy–Utility Trade-off (PUT) metrics and proposes a hierarchical IoT–XAI reference architecture, together with the conceptual Internet of Things Interpretability Evaluation Standard (IOTIES) for cross-domain assessment. Conclusions: The findings indicate that IoT–XAI research must shift from accuracy-only reporting to lightweight, model-agnostic, and privacy-aware explanation pipelines that are explicitly budgeted for edge resources and aligned with the needs of heterogeneous stakeholders in smart city and agricultural deployments.

This study introduces a novel approach to narrate the historical trajectory of Aitoloakarnania's fortifications by integrating the rich cultural and natural assets of the region into a comprehensive digital tour. Aimed at amplifying the accessibility of archaeological information both locally and internationally, the initiative promotes regional visibility and boosts the local economy through potential tourism. While Aitoloakarnania stands unique, boasting fortifications spanning Classical to post-Byzantine eras, there has been a discernible gap in presenting them cohesively to the public, especially given their association with European co-funded restoration programs. To bridge this gap, we developed and evaluated three interactive applications: a virtual tour designed for conventional computer systems, a Virtual Reality (VR) environment suitable for VR Headsets, and an Augmented Reality (AR) application targeted at smartphone users. These applications were developed not only to enhance visitor experience but also to foster interdisciplinary research and educational applications. In conclusion, by intertwining technology with history, we endeavor to connect the public to the rich heritage of Aitoloakarnania, promoting education, knowledge, and cultural appreciation on a global scale.

The increasing number of data a booking platform such as Booking.com and AirBnB offers make it challenging for interested parties to browse through the available accommodations and analyze reviews in an efficient way. Efforts have been made from the booking platform providers to utilize recommender systems in an effort to enable the user to filter the results by factors such as stars, amenities, cost but most valuable insights can be provided by the unstructured text-based reviews. Going through these reviews one-by-one requires a substantial amount of time to be devoted while a respectable percentage of the reviews won't provide to the user what they are actually looking for. This research publication explores how Large Language Models (LLMs) can enhance short rental apartments recommendations by summarizing and mining key insights from user reviews. The web application presented in this paper, named "instaGuide", automates the procedure of isolating the text-based user reviews from a property on the Booking.com platform, synthesizing the summary of the reviews, and enabling the user to query specific aspects of the property in an effort to gain feedback on their personal questions/criteria. During the development of the instaGuide tool, numerous LLM models were evaluated based on accuracy, cost, and response quality. The results suggest that the LLM-powered summarization reduces significantly the amount of time the users need to devote on their search for the right short rental apartment, improving the overall decision-making procedure.

This study explores the integration of sensor technology into organic Kalamon table olive cultivation, focusing on soil temperature, moisture, and salinity dynamics at two organic farms in Messolonghi, Greece. The selected farms-Farm A (irrigated) and Farm B (dry)-represented distinct irrigation practices. An integrated sensor platform was deployed to continuously monitor soil moisture, temperature and salinity at various depths. Real-time data were collected at 10-minute intervals during the flowering-to-fruit-growth and ripening-to-harvest stages. Complementary meteorological data, including air temperature, relative humidity and rainfall, were obtained from local stations. The results revealed significant variations in soil temperature in both farms, with higher temperature fluctuations in shallow than deeper soil layers. Farm A maintained more stable temperatures in the upper soil layers, whereas Farm B displayed greater temperature fluctuations due to reduced moisture retention. In contrast, in deeper soil layers, Farm B retained more moisture, while Farm A demonstrated more pronounced moisture loss. Salinity distribution analysis revealed higher ion content in the surface soil layers of irrigated farm, whereas the non-irrigated farm showed better ion retention at deeper depths. Air temperature variations indicated distinct microclimatic differences between farms, with Farm A generally experiencing lower temperatures during the fruit ripening-to-harvest stage. These findings highlight the role of irrigation in stabilizing soil conditions and emphasize the importance of continuous monitoring and precise environmental tracking for enhancing sustainability in organic olive farming. Additionally, the integration of sensor data enables farmers to enhance irrigation practices, improving decision-making and ensuring the quality and authenticity of their produce from flowering to harvest.

During the last decade, artificial intelligence (AI) has enabled key technological innovations within the modern dementia and frailty healthcare and prevention landscape. This has boosted the impact of technology in the clinical setting, enabling earlier diagnosis with improved specificity and sensitivity, leading to accurate and time-efficient support that has driven the development of preventative interventions minimizing the risk and rate of progression. Background/Objectives: The rapid ageing of the European population places a substantial strain on the current healthcare system and imposes several challenges. COMFORTage is the joint effort of medical experts (i.e., neurologists, psychiatrists, neuropsychologists, nurses, and memory clinics), social scientists and humanists, technical experts (i.e., data scientists, AI experts, and robotic experts), digital innovation hubs (DIHs), and living labs (LLs) to establish a pan-European framework for community-based, integrated, and people-centric prevention, monitoring, and progression-managing solutions for dementia and frailty. Its main goal is to introduce an integrated and digitally enabled framework that will facilitate the provision of personalized and integrated care prevention and intervention strategies on dementia and frailty, by piloting novel technologies and producing quantified evidence on the impact to individuals’ wellbeing and quality of life. Methods: A robust and comprehensive design approach adopted through this framework provides the guidelines, tools, and methodologies necessary to empower stakeholders by enhancing their health and digital literacy. The integration of the initial information from 13 pilots across 8 European countries demonstrates the scalability and adaptability of this approach across diverse healthcare systems. Through a systematic analysis, it aims to streamline healthcare processes, reduce health inequalities in modern communities, and foster healthy and active ageing by leveraging evidence-based insights and real-world implementations across multiple regions. Results: Emerging technologies are integrated with societal and clinical innovations, as well as with advanced and evidence-based care models, toward the introduction of a comprehensive global coordination framework that: (a) improves individuals’ adherence to risk mitigation and prevention strategies; (b) delivers targeted and personalized recommendations; (c) supports societal, lifestyle, and behavioral changes; (d) empowers individuals toward their health and digital literacy; and (e) fosters inclusiveness and promotes equality of access to health and care services. Conclusions: The proposed framework is designed to enable earlier diagnosis and improved prognosis coupled with personalized prevention interventions. It capitalizes on the integration of technical, clinical, and social innovations and is deployed in 13 real-world pilots to empirically assess its potential impact, ensuring robust validation across diverse healthcare settings.

In this work, we present a principled framework for the deployment of Large Language Models (LLMs) in enterprise big data management across digital governance, marketing, and accounting domains. Unlike conventional predictive applications, our approach integrates LLMs as auditable, sector-adaptive components that robustly and directly enhance data curation, lineage, and regulatory compliance. The study contributes (i) a systematic evaluation of seven LLM-enabled functions—including schema mapping, entity resolution, and document extraction—that directly improve data quality and operational governance; (ii) a distributed architecture that deploys Apache Spark orchestration with Markov Chain Monte Carlo sampling to achieve quantifiable uncertainty and reproducible audit trails; and (iii) a cross-sector analysis demonstrating robust semantic accuracy, compliance management, and explainable outputs suited to diverse assurance requirements. Empirical evaluations reveal that the proposed architecture persistently attains elevated mapping precision, resilient multimodal feature extraction, and consistent human supervision. These characteristics collectively reinforce the integrity, accountability, and transparency of information ecosystems, particularly within compliance-driven organizational settings.

In 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.

This paper presents an innovative information system designed to support the management and augmented reality (AR) visits of cultural tourism points of interest, focusing on the fortifications of Aitoloakarnania, Greece. The system integrates advanced technologies including photogrammetry, 3D modeling, virtual reality (VR), AR, and 360° virtual tours to enhance the accessibility and promotion of cultural heritage sites. We describe the technical approaches used to capture, process, and optimize 3D models and panoramic imagery for use in mobile AR and VR applications. The resulting applications provide engaging, interactive experiences for visitors while also serving as management tools for cultural heritage professionals. User evaluation results demonstrate the effectiveness of the system in enhancing cultural tourism experiences. This work showcases how intelligent digital systems can be leveraged to preserve, study, and disseminate cultural content in novel ways, contributing to the field of digital humanities.

In modern agriculture, the capability to promptly detect and respond to specific events is crucial. This study centres on the transformative potential of TinyML for enhancing event detection in Smart Agriculture, particularly when integrated with LoRa-based Wireless Sensor Networks (WSNs). In this work, we underscore the unique advantages of utilizing TinyML at the edge-bypassing the latency and overhead associated with cloud-centric models and ensuring immediate, on-site analytical insights. Employing LoRa WSNs as the backbone provides a seamless, low-power, and expansive data communication framework. Detailed experiments demonstrate TinyML's efficacy in accurately predicting agricultural events while reducing computational and energy consumption. In conclusion, the synergy between TinyML and LoRa WSNs offers a promising approach for fine-tuned, real-time event detection, aiming for sustainable and high-yield agricultural practices.

Mobile Edge Computing (MEC) is a promising computing paradigm that provides computing and storage services for mobile and big data applications. MEC servers are deployed at base stations to establish a mobile edge network (MEN) where mobile users can offload tasks to nearby servers to speed up their mobile applications. However, challenges such as the quality of workload distribution in edge computing environments still need to be tackled. Most studies and offloading strategies assume mobile users are stationary, but in reality, users move and this affects workload distribution and response time of mobile applications. In this work, we propose a solution that addresses these challenges by reducing energy consumption and enhancing QoS by assigning mobile tasks to MEC servers in the users' predicted trajectories using a Random waypoint Model. We propose OptiMEC for scheduling mobile tasks in a Mobile Edge Computing (MEC) environment in 5G networks that utilize central-base stations. We consider the task properties, user mobility, and delay constraints in our proposed algorithmic scheme and we also propose an energy-load balancing heuristic. The problem is formulated as an optimization and constraint satisfaction problem and we propose a near-optimal solution for scheduling mobile tasks to MEC servers. The results of our simulation experiments show that our proposed solution can significantly reduce energy consumption in Mobile Edge Networks (MENs) and improve QoS by executing tasks under constrained time.

Research into the ancient topography of the historical center of Athens has produced a rich archive database. Augmented reality technology is also becoming more and more accessible to the public as most mobile devices nowadays can support it. In this publication, the effort to build the augmented reality application “Virtual Street Museum” to highlight the information contained in aforementioned database is analyzed. The application provides a personalized experience for the user by creating a route, in the historical center of Athens, adapted to her personal choices. This route includes points with augmented reality scenes where the user can see the information of the ancient topography in an impressive way. Finally, an attempt to construct a time estimation algorithm for the walking tour was made. This algorithm also adapts to each user by data collected from her use of the application providing a personalized experience.

As the Internet of Things (IoT) landscape grows, with estimates exceeding 75 billion devices by 2025, effective data management and processing become primary challenges. Traditional cloud-centric models may struggle under this large data volume. This research presents Edge AI as an innovative solution, integrating artificial intelligence directly at data sources like sensors and cameras. This ensures real-time analytics and decision-making, promoting responsive and tailored actions. Our literature review details Edge AI's distinct characteristics and applications. The interaction of Edge AI with large-scale IoT domains is critically examined, emphasizing their combined potential. Within big data infrastructures, a comparative study contrasts Edge AI and cloud-based AI, investigating processing speeds, optimization techniques, and essential metrics. The in-herent limitations of Edge AI and current challenges are also discussed. In summary, Edge AI offers notable improvements in operational efficiency, data privacy, and bandwidth use. As IoT continues its rapid expansion, the strategic deployment of Edge AI becomes crucial, leading to a future where data is not just collected but smartly utilized.

In 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.

Autonomous 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.

Blockchain technology is being successfully applied in the cultural sector to enable the lawful distribution of works. This paper studies copyright related issues regarding the two basic characteristics of the specific technology: its nature as a ledger where information related to ownership is registered and the fact that it provides smart contracts functionality. We are addressing questions related to the legitimacy of the existence of a registry (ledger) and the significance of smart contracts for copyright law.

Aiming 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.

Great value can be given to any old-school organization by providing its users with personalised experience. The present study aims at the use of innovative technology in libraries to enable the study and understanding of the libraries’ frequent visitors to better support them through the provision of enriched personalised experience. To support this goal, modern technologies such as recommendation systems, gamification and indoor localization techniques such as beacons will be used. Recommendation systems are the basis for providing personalised user experience since their task is to exploit user information (demographic information, habits, actions, likes and dislikes) to get to know each and every one of the users by building a profile for them. Gamification will also support the concept by enriching the user experience with game-like attributes to boost user engagement like rewarding the user with experience points for each library visit or for each book they check out. Finally, indoor localization techniques will be used to help the libraries manage the on-premises crowd to avoid congestion and to also help the libraries’ visitors have fruitful interactions with each other. The combined application of the aforementioned techniques can help the libraries grow alongside the technological advancements by exploiting them for the modernisation of their model.