arXiv subjects

Alexandros Gazis

Publications and source records attributed to Alexandros Gazis.

15 recordsLinked to original sources

arXiv2026

Energy Efficient AI-Enabled Wireless Sensor Networks for Mission Critical Environments: A Systematic Review across Smart Grid, AI, and Urban Infrastructure Applications

Advanced wireless sensor networks powered by artificial intelligence are increasingly required for applications demanding continuous monitoring, autonomous operation, reliable communication, and fast decision support. This systematic review examines recent work from 2023 to 2026 on energy-efficient, AI-enabled wireless sensor networks (WSNs) in mission-critical environments, with particular focus on power electronics, and urban infrastructure systems. The authors synthesise a corpus of 50 DOI indexed studies satisfying inclusion criteria that received qualitative thematic coding and comparative analysis. Other references were only cited to provide historical, methodological, or technical context and were not included in the systematic review corpus. As such, our results show that AI can improve WSN energy behaviour through routing and clustering, edge AI, reinforcement learning, fuzzy logic, metaheuristic optimisation, and AI-based security. At the same time, energy efficiency cannot be treated as an isolated performance target. In mission-critical systems, security, latency, and reliability are closely interlinked requirements. The review concludes that future work should move away from optimising protocols in isolation, and instead focus on building lightweight, explainable, secure, and field-tested AI-driven WSN architectures suited to real operational environments.

cs.SE↗

arXiv2026

A Qualitative Comparative Study of Communication in Higher Distance Education

The rise of open and distance education has made it more important than ever to have communication tools that are simple, flexible, and good for helping students work together, talk to each other, and feel connected. Researchers have already looked at how instant messaging apps like WhatsApp and Telegram can be used for learning. Viber, on the other hand, has not been studied as much, especially when it comes to its use in higher education at a distance. This article builds on a previously published conference case study conducted at the Hellenic Open University (HOU), which examined the use of Viber in distance collaborative projects. The present study extends that work by offering a comparative discussion of communication ecosystems in higher distance education. Using ideas from connectivism learning theory, along with the concepts of social presence and community-based learning, this article looks at how chatting on Viber can add to and improve formal online learning. The findings show that Viber is not just a simple messaging app. It also works as a casual space where students pass along what they know, give each other a hand, and slowly build a sense of being part of a group. This lines up with other research showing that social media can help students in distance learning feel less isolated. Overall, the study shows why it makes sense to include informal chat platforms when planning courses for higher education at a distance.

cs.CY↗

arXiv2026

Distance Learning and Multilingual Education: A Case Study of Challenges and Pedagogical Perspectives in the Greek Border Region

In increasingly multicultural and multilingual societies, foreign language learning has become essential not only for communication but also for social cohesion and professional advancement. Distance education has emerged as a flexible and accessible solution, particularly for adults seeking to enhance their linguistic and intercultural competencies. This study explores the views of foreign language teachers regarding the role of distance education in promoting multilingualism, with a specific focus on culturally diverse border regions. Conducted in the Regional Unit of Evros, Greece, the research adopts a qualitative methodology based on semi-structured interviews with five language educators working in public and private education. Findings reveal that teachers recognize the potential of digital tools such as Massive Open Online Courses (MOOCs), machine translation applications (e.g., Google Translate, DeepL), and adaptive learning platforms to support multilingual learning, particularly when used as supplementary resources. However, concerns were raised about the lack of personalized feedback, limited interactivity, and the absence of culturally contextualized content on existing platforms. Teachers emphasized the importance of digital literacy, pedagogical training, and culturally inclusive design to ensure effective implementation. The study highlights the need for targeted support for educators in border regions and calls for more locally adapted digital resources that reflect linguistic diversity. These findings offer insights for policymakers and educational technology developers aiming to improve the quality and reach of multilingual education in remote or underserved areas.

cs.CY↗

arXiv2026

Synthetic Media in Multilingual MOOCs: Deepfake Tutors, Pedagogical Effects, and Ethical-Policy Challenges

In recent years, synthetic media from deepfake videos have emerged as a new interesting technology, whether that refers to cloned voices, multilingual translation models, or more recent applications of avatar tutors into higher education. As such, these technologies are rapidly becoming part of the multilingual distance learning model and, more recently, MOOCs worldwide. This article is a scoping review that focuses on recent international literature published between 2020 and 2025 to explore the usage of deepfake and synthetic media tools and methods in multilingual MOOC content and assess the influence of these technologies on social presence and participation. Similarly, we focus on ethical and political issues that are closely connected with the adaptation of these technologies, and upon analysing educational technology and policy documents, such as UNESCO's Guidelines and the EU AI Act, we pinpoint that the use of synthetic avatars and AI-generated videos can diminish production costs and assist multilingual learning. Evidently, concerns arise regarding authenticity, privacy, and the shifting nature of the teacher-learner relationship that are thoroughly discussed. As a result, the technical merit of this paper is the proposal of a policy framework that, in an effort to address these issues, focuses on transparency, responsible governance, and AI literacy. The goal is not to replace human instruction but to integrate synthetic media in ways that strengthen pedagogical design, safeguard rights, and ensure that multilingual MOOCs become more interesting and inclusive rather than more automated robotic processes and unequal

cs.CY↗

arXiv2025

E-polis: Gamifying Sociological Surveys through Serious Games -- A Data Analysis Approach Applied to Multiple-Choice Question Responses Datasets

E-polis is a serious digital game designed to gamify sociological surveys studying young people's political opinions. In this platform game, players navigate a digital world, encountering quests posing sociological questions. Players' answers shape the city-game world, altering building structures based on their choices. E-polis is a serious game, not a government simulation, aiming to understand players' behaviors and opinions thus we do not train the players but rather understand them and help them visualize their choices in shaping a city's future. Also, it is noticed that no correct or incorrect answers apply. Moreover, our game utilizes a novel middleware architecture for development, diverging from typical asset prefab scene and script segregation. This article presents the data layer of our game's middleware, specifically focusing on data analysis based on respondents' gameplay answers. E-polis represents an innovative approach to gamifying sociological research, providing a unique platform for gathering and analyzing data on political opinions among youth and contributing to the broader field of serious games.

cs.HC↗

arXiv2025

Comprehensive Classification of Web Tracking Systems: Technological In-sights and Analysis

Web tracking (WT) systems are advanced technologies used to monitor and analyze online user behavior. Initially focused on HTML and static webpages, these systems have evolved with the proliferation of IoT, edge computing, and Big Data, encompassing a broad array of interconnected devices with APIs, interfaces and computing nodes for interaction. WT systems are pivotal in technological innovation and business development, although trends like GDPR complicate data extraction and mandate transparency. Specifically, this study examines WT systems purely from a technological perspective, excluding organizational and privacy implications. A novel classification scheme based on technological architecture and principles is proposed, compared to two preexisting frameworks. The scheme categorizes WT systems into six classes, emphasizing technological mechanisms such as HTTP proto-cols, APIs, and user identification techniques. Additionally, a survey of over 1,000 internet users, conducted via Google Forms, explores user awareness of WT systems. Findings indicate that knowledge of WT technologies is largely unrelated to demographic factors such as age or gender but is strongly influenced by a user's background in computer science. Most users demonstrate only a basic understanding of WT tools, and this awareness does not correlate with heightened concerns about data misuse. As such, the research highlights gaps in user education about WT technologies and underscores the need for a deeper examination of their technical underpinnings. This study provides a foundation for further exploration of WT systems from multiple perspectives, contributing to advance-ments in classification, implementation, and user awareness.

cs.HC↗

arXiv2025

A comprehensive review of sensor technologies, instrumentation, and signal processing solutions for low-power Internet of Things systems with mini-computing devices

This article provides a comprehensive overview of sensors commonly used in low-cost, low-power systems, focusing on key concepts such as Internet of Things (IoT), Big Data, and smart sensor technologies. It outlines the evolving roles of sensors, emphasizing their characteristics, technological advancements, and the transition toward "smart sensors" with integrated processing capabilities. The article also explores the growing importance of mini-computing devices in educational environments. These devices provide cost-effective and energy-efficient solutions for system monitoring, prototype validation, and real-world application development. By interfacing with wireless sensor networks and IoT systems, mini-computers enable students and researchers to design, test, and deploy sensor-based systems with minimal resource requirements. Furthermore, this article examines the most widely used sensors, detailing their properties and modes of operation to help readers understand how sensor systems function. The aim of this study is to provide an overview of the most suitable sensors for various applications by explaining their uses and operations in simple terms. This clarity will assist researchers in selecting the appropriate sensors for educational and research purposes or understanding why specific sensors were chosen, along with their capabilities and possible limitations. Ultimately, this research seeks to equip future engineers with the knowledge and tools needed to integrate cutting-edge sensor networks, IoT, and Big Data technologies into scalable, real-world solutions.

eess.SP↗

arXiv2025

Knowledge representation and scalable abstract reasoning for simulated democracy in Unity

We present a novel form of scalable knowledge representation about agents in a simulated democracy, e-polis, where real users respond to social challenges associated with democratic institutions, structured as Smart Spatial Types, a new type of Smart Building that changes architectural form according to the philosophical doctrine of a visitor. At the end of the game players vote on the Smart City that results from their collective choices. Our approach uses deductive systems in an unusual way: by integrating a model of democracy with a model of a Smart City we are able to prove quality aspects of the simulated democracy in different urban and social settings, while adding ease and flexibility to the development. Second, we can infer and reason with abstract knowledge, which is a limitation of the Unity platform; third, our system enables real-time decision-making and adaptation of the game flow based on the player's abstract state, paving the road to explainability. Scalability is achieved by maintaining a dual-layer knowledge representation mechanism for reasoning about the simulated democracy that functions in a similar way to a two-level cache. The lower layer knows about the current state of the game by continually processing a high rate of events produced by the in-built physics engine of the Unity platform, e.g., it knows of the position of a player in space, in terms of his coordinates x,y,z as well as their choices for each challenge. The higher layer knows of easily-retrievable, user-defined abstract knowledge about current and historical states, e.g., it knows of the political doctrine of a Smart Spatial Type, a player's philosophical doctrine, and the collective philosophical doctrine of a community players with respect to current social issues.

cs.MA↗

arXiv2024

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design's innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis' residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy.

cs.DC↗

arXiv2023

E-polis: A serious game for the gamification of sociological surveys

E-polis is a multi-platform serious game that gamifies a sociological survey for studying young people's opinions regarding their ideal society. The gameplay is based on a user navigating through a digital city, experiencing the changes inflicted, triggered by responses to social and pedagogical surveys, known as "dilemmas". The game integrates elements of adventure, exploration, and simulation. Unity was the selected game engine used for the development of the game, while a middleware component was also developed to gather and process the users' data. At the end of each game, users are presented with a blueprint of the city they navigated to showcase how their choices influenced its development. This motivates them to reflect on their answers and validate them. The game can be used to collect data on a variety of topics, such as social justice, and economic development, or to promote civic engagement and encourage young people to think critically about the world around them.

cs.CY↗

arXiv2023

Serious Games in Digital Gaming: A Comprehensive Review of Applications, Game Engines and Advancements

Serious games are defined as applied games that focus on the gamification of an experience (e.g., learning and training activities) and are not strictly for entertainment purposes. In recent years, serious games have become increasingly popular due to their ability to simultaneously educate and entertain users. In this review, we provide a comprehensive overview of the different types of digital games and expand on the serious games genre while focusing on its various applications. Furthermore, we present the most widely used game engines used in the game development industry and extend the Unity game machine advantages. Lastly, we conclude our research with a detailed comparison of the two most popular choices (Unreal and Unity engines) and their respective advantages and disadvantages while providing future suggestions for serious digital game development.

cs.SE↗

arXiv2022

Comparison Analysis of Traditional Machine Learning and Deep Learning Techniques for Data and Image Classification

The purpose of the study is to analyse and compare the most common machine learning and deep learning techniques used for computer vision 2D object classification tasks. Firstly, we will present the theoretical background of the Bag of Visual words model and Deep Convolutional Neural Networks (DCNN). Secondly, we will implement a Bag of Visual Words model, the VGG16 CNN Architecture. Thirdly, we will present our custom and novice DCNN in which we test the aforementioned implementations on a modified version of the Belgium Traffic Sign dataset. Our results showcase the effects of hyperparameters on traditional machine learning and the advantage in terms of accuracy of DCNNs compared to classical machine learning methods. As our tests indicate, our proposed solution can achieve similar - and in some cases better - results than existing DCNNs architectures. Finally, the technical merit of this article lies in the presented computationally simpler DCNN architecture, which we believe can pave the way towards using more efficient architectures for basic tasks.

cs.CV↗

arXiv2022

A Blockchain Cloud Computing Middleware for Academic Manuscript Submission

One of the most important tasks in scientific publishing is the articles' evaluation via the editorial board and the reviewers' community. Additionally, in scientific publishing great concern exists regarding the peer-review process and how it can be further optimised to decrease the time from submission to the first decision, as well as increase the objectivity of the reviewers' remarks ensuring that no bias or human error exists in the reviewing process. In order to address this issue, our article suggests a novice cloud framework for manuscript submission based on blockchain technology that further enhances the anonymity between authors and reviewers alike. Our method covers the whole spectrum of current submission systems capabilities, but it also provides a decentralised solution using open-source tools such as Java Spring that enhance the anonymity of the reviewing process.

cs.DC↗

arXiv2022

A Method for Counting, Tracking and Monitoring of Visitors with RFID sensors

This publication presents a method responsible for counting tracking and monitoring visitors inside a building. The site examined is Manos Hatzidakis' House, situated in Xanthi. Specifically, we have conducted a study, which provides recommendations, regarding the installation of sensors in the building. We also present the communication protocols of the computer network used in order to ensure the efficient communication between the space examined and the sensor network. Finally, we describe the process of creating a website, which is designed to store and view the data.

cs.SE↗

arXiv2022

Crowd tracking and monitoring middleware via Map-Reduce

This paper presents the design, implementation, and operation of a novel distributed fault-tolerant middleware. It uses interconnected WSNs that implement the Map-Reduce paradigm, consisting of several low-cost and low-power mini-computers (Raspberry Pi). Specifically, we explain the steps for the development of a novice, fault-tolerant Map-Reduce algorithm which achieves high system availability, focusing on network connectivity. Finally, we showcase the use of the proposed system based on simulated data for crowd monitoring in a real case scenario, i.e., a historical building in Greece (M. Hatzidakis' residence).The technical novelty of this article lies in presenting a viable low-cost and low-power solution for crowd sensing without using complex and resource-intensive AI structures or image and video recognition techniques.

cs.DC↗