With the emergence of autonomous vehicles and the ever-increasing volume of generated data, the Mobile Edge Computing paradigm has been proposed to address challenges related to latency and computational capacity. However, static vehicle-to-MEC association policies fail to meet these requirements due to the highly dynamic nature of vehicular networks. To address these challenges, this PhD research focuses on mobilityaware orchestration in MEC-enabled vehicular networks. In our first contribution, we introduce an ETSI-compliant proactive migration framework based on proximity-triggered migration notifications and a mobility-aware task migration strategy to relocate vehicular applications during mobility. The second contribution extends this work toward adaptive orchestration by formulating allocation and migration as a decision-making problem and developing a learning-based framework with a Simu5G–Python interface and a fairness- and delay-aware Maskable PPO agent. Results show improved response time, lower deadline miss rate, and balanced resource utilization under dense vehicular conditions. Our current work focuses on explainability methods to understand the learned policy and support trustworthy deployment while improving performance in terms of E2E delay, deadline miss rate, and fairness in resource utilization.

This research presents the design and implementation of the Decentralized Smart City of Things (DSCoT), a novel framework leveraging Web3 architecture to enhance the security and authentication of assets in cyber-physical systems (CPSs) for smart cities on a private blockchain. Unlike traditional non-fungible tokens that primarily identify and distinguish financial assets, existing approaches lack robust mechanisms for attributing and authenticating CPS assets such as owners, users, and IoT-enabled smart devices. DSCoT addresses this gap by introducing an extended ERC721 protocol, enabling IoT-enabled devices to have unique blockchain identities similar to user accounts, which enhances device management and tracking. Novel smart contract modules facilitate secure identification and authentication of CPS assets. Evaluated results on a private Hyperledger Besu blockchain show that DSCoT achieves significant performance improvements, including sub-second latency (∼0.1–0.5 s for application programming interface (API) calls), minimal transaction costs (∼0.01–0.05 USD), and a high processing capacity (∼1000 transactions per second (TPS)). These results, along with improved security through immutable authentication records, demonstrate DSCoT’s effectiveness as a scalable and secure solution for smart city CPSs.

Network slicing (NS) enables service providers to create independent virtual networks, storage, and computing resources (so-called slices) from a shared physical infrastructure across one or more operators. These slices can be tailored to specific services, user needs, and Quality-of-Service (QoS) requirements. However, most production-grade orchestration frameworks deployed today still rely on manually crafted templates and centralized controllers, even though a growing body of research has begun to explore deep reinforcement learning (DRL) and multi-agent DRL (MARL) for autonomous and distributed orchestration. As the number of slices increases, limited visibility across administrative domains and human-driven configuration render provisioning slow and error-prone. We address these limitations by proposing a multi-agent management framework where Large Language Models (LLMs) act as intelligent interfaces between tenants and the Management and Orchestration (MANO) stack. Agents interpret high-level intent provided by users or applications, translate it into standards-compliant descriptors, cooperate with other agents and underlying ML-based optimizers to allocate resources, and supervise the slice lifecycle. To validate this paradigm, we conduct two complementary experiments that together cover the AI-driven stages of the proposed workflow. First, we benchmark four state-of-the-art LLMs in generating Yet Another Markup Language (YAML)-based slice descriptions from natural-language prompts of varying complexity. GPT-4.1 consistently produces valid schemas and adheres to constraints, while other models occasionally violate conditions but still yield usable outputs. Second, we implement a proof-of-concept multi-agent system that, from a single user request, models a slice, synthesizes a Topology and Orchestration Specification for Cloud Applications (TOSCA) descriptor, and generates a deployment plan. This second experiment validates the multi-agent transformation chain from a slice request to deployable artifacts. The end-to-end workflow completes in about 40 seconds and produces artifacts validated by a domain expert. The final infrastructure instantiation of these already validated artifacts, e.g., through Kubernetes, Docker Compose, or NFV orchestration tools, is intentionally not treated as a separate AI experiment in this paper. Our results show that modern LLMs, when orchestrated in a collaborative agent framework, can significantly reduce the engineering effort and turnaround time required to move from high-level intents to deployable slice descriptors while ensuring alignment with industry standards. The proposed LLM-based framework can be integrated with any resource allocation optimizers or ML-based (e.g., DRL/MARL) infrastructure and network controllers for multi-tenant resource optimization. We emphasize that the present validation targets the orchestration/specification layer of network slice management, namely the translation of natural-language intents into standards-compliant, internally consistent, and infrastructure-feasible artifacts. Open challenges and future research directions are discussed at the end of the paper.

Vehicular Digital Twins (VDTs) are emerging as a key driver for secure, adaptive, and data-driven Internet of Vehicles (IoV) systems. However, existing VDT-based authentication approaches often rely on centralized coordination or suffer from high communication and consensus overhead in case of distributed mechanism. This limits their suitability for highly dynamic vehicular environments. This paper proposes DQ-VeDT, a Decentralized, Quantum-safe Vehicular Digital Twin architecture that enables low-latency, resilient authentication without continuous reliance on infrastructure. The proposed design treats digital twins as first-class entities and employs localized quorum verification with overlap-based continuity to support mobility-aware authentication and Byzantine resilience. A lightweight trust and cryptographic state model is integrated to ensure robustness against insider and external adversaries without compromising the scalability. The simulation results show that DQ-VeDT achieves low authentication latency, stable performance under high vehicle density, and graceful degradation under adversarial conditions. The results indicate that DQ-VeDT provides a good trade-off between security, accuracy, and communication efficiency. Hence, it is well-suited for next-generation vehicular digital twin deployments.

The telecommunications and networking domain stands at the precipice of a transformative era, driven by the necessity to manage increasingly complex, hierarchical, multi administrative domains (i.e., several operators on the same path) and multilingual systems. Recent research has demonstrated that Large Language Models (LLMs), with their exceptional general-purpose text analysis and code generation capabilities, can be effectively applied to certain telecom problems (e.g., auto-configuration of data plan to meet certain application requirements). However, due to their inherent token-by-token processing and limited capacity for maintaining extended context, LLMs struggle to fulfill telecom-specific requirements such as cross-layer dependency cascades (i.e., over OSI), temporal-spatial fault correlation, and real-time distributed coordination. In contrast, Large Concept Models (LCMs), which reason at the abstraction level of semantic concepts rather than individual lexical tokens, offer a fundamentally superior approach for addressing these telecom challenges. By employing hyperbolic latent spaces for hierarchical representation and encapsulating complex multi-layered network interactions within concise concept embeddings, LCMs overcome critical shortcomings of LLMs in terms of memory efficiency, cross-layer correlation, and native multimodal integration. This paper argues that adopting LCMs is not simply an incremental step, but a necessary evolutionary leap toward achieving robust and effective AI-driven telecom management.

With the emergence of autonomous vehicles and the ever-increasing data reported, providing the required latency and computational capabilities is becoming challenging. To address this issue, multi-access edge computing (MEC) for 3GPP 5G Cellular vehicles to everything (C-V2X) has been proposed recently. In this paper, we propose the Mobility Aware Task Migration (MATM) algorithm that strives the limitations of the benchmark algorithms that the MEC orchestrator utilizes for offloading tasks from vehicles. We provide a detailed simulation-based study for End-to-End (E2E) delay by serving the safety application as a function of different network densities. Firstly, we propose an extension to the location service defined in the ETSI MEC reference architecture; this extension enables the MEC service to send a migration notification to the MEC orchestrator to start the migration. Secondly, we introduce an enhancement to the MEC orchestrator module to enable task migration during vehicle mobility by selecting the most suitable MEC host with the closest proximity to the vehicle. Additionally, we use the Simu5G simulator to implement our scenario and conduct an evaluation of task offloading algorithms, and a detailed E2E delay analysis. Simulation results highlight the ability of our proposed algorithm to minimize E2E delay while ensuring fairness in resource allocation.

Skin cancer is a pervasive and potentially life-threatening disease. Early detection plays a crucial role in improving patient outcomes. Machine learning (ML) techniques, particularly when combined with pre-trained deep learning models, have shown promise in enhancing the accuracy of skin cancer detection. In this paper, we enhanced the VGG19 pre-trained model with max pooling and dense layer for the prediction of skin cancer. Moreover, we also explored the pre-trained models such as Visual Geometry Group 19 (VGG19), Residual Network 152 version 2 (ResNet152v2), Inception-Residual Network version 2 (InceptionResNetV2), Dense Convolutional Network 201 (DenseNet201), Residual Network 50 (ResNet50), Inception version 3 (InceptionV3), For training, skin lesions dataset is used with malignant and benign cases. The models extract features and divide skin lesions into two categories: malignant and benign. The features are then fed into machine learning methods, including Linear Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR) and Support Vector Machine (SVM), our results demonstrate that combining E-VGG19 model with traditional classifiers significantly improves the overall classification accuracy for skin cancer detection and classification. Moreover, we have also compared the performance of baseline classifiers and pre-trained models with metrics (recall, F1 score, precision, sensitivity, and accuracy). The experiment results provide valuable insights into the effectiveness of various models and classifiers for accurate and efficient skin cancer detection. This research contributes to the ongoing efforts to create automated technologies for detecting skin cancer that can help healthcare professionals and individuals identify potential skin cancer cases at an early stage, ultimately leading to more timely and effective treatments.

AbstractThe Metaverse, distinguished by its capacity to integrate the physical and digital realms seamlessly, presents a dynamic virtual environment offering diverse opportunities for engagement across innovation, entertainment, socialization, and commercial endeavors. However, the Metaverse is poised for a transformative evolution through the convergence of contemporary technological advancements, including artificial intelligence (AI), Blockchain, Robotics, augmented reality, virtual reality, and mixed reality. This convergence is anticipated to revolutionize the global digital landscape, introducing novel social, economic, and operational paradigms for organizations and communities. To comprehensively elucidate the future potential of this technological fusion and its implications for digital innovation, this research endeavors to undertake a thorough analysis of scholarly discourse and research pertaining to the Metaverse, AI, Blockchain, and associated technologies. This survey delves into various critical facets of the Metaverse ecosystem, encompassing component analysis, exploration of digital currencies, assessment of AI utilization in virtual environments, and examination of Blockchain's role in enhancing digital content and data security. Leveraging articles retrieved from esteemed digital repositories including ScienceDirect, IEEE Xplore, Springer Nature, Google Scholar, and ACM, published between 2017 and 2023, this study adopts an analytical approach to engage with these materials. Through rigorous examination and discourse, this research aims to provide insights into the emerging trends, challenges, and future directions in the convergence of the Metaverse, Blockchain, and AI.This article is categorized under: Application Areas > Industry Specific Applications

Network slicing, a cornerstone technology for future networks, enables the creation of customized virtual networks on a shared physical infrastructure. This fosters innovation and agility by providing dedicated resources tailored to specific applications. However, current orchestration and management approaches face limitations in handling the complexity of new service demands within multi-administrative domain environments. This paper proposes a future vision for network slicing powered by Large Language Models (LLMs) and multi-agent systems, offering a framework that can be integrated with existing Management and Orchestration (MANO) frameworks. This framework leverages LLMs to translate user intent into technical requirements, map network functions to infrastructure, and manage the entire slice lifecycle, while multi-agent systems facilitate collaboration across different administrative domains. We also discuss the challenges associated with implementing this framework and potential solutions to mitigate them.

Insider threats pose a significant challenge in cybersecurity, demanding advanced detection methods for effective risk mitigation. This paper presents a comparative evaluation of data imbalance addressing techniques for CNN-based insider threat detection. Specifically, we integrate Convolutional Neural Networks (CNN) with three popular data imbalance addressing techniques: Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE, and Adaptive Synthetic Sampling (ADASYN). The objective is to enhance insider threat detection accuracy and robustness in imbalanced datasets common to cybersecurity domains. Our study addresses the lack of consensus in the literature regarding the superiority of data imbalance addressing techniques in this field. We analyze a human behavior-based dataset (i.e., CERT) that reports users’ Information Technology (IT) activities with a substantial number of samples to provide a clear conclusion on the effectiveness of these balancing techniques when coupled with CNN. Experimental results demonstrate that ADASYN, in conjunction with CNN, achieves a ROC curve of 96%, surpassing SMOTE and Borderline-SMOTE in enhancing detection accuracy in imbalanced datasets. We compare the results of these three hybrid models (CNN + imbalance addressing techniques) with state-of-the-art selective studies focusing on ROC, recall, and accuracy measures. Our findings contribute to the advancement of insider threat detection methodologies.

In vehicular edge computing (VEC), multi-access edge computing (MEC) plays a crucial role in enabling vehicles to offload computationally intensive tasks, thereby enhancing data processing efficiency. However, MEC encounters challenges related to limited resources and the complexity of optimal task offloading due to the dynamicity of the environment. This short paper provides a preliminary analysis of decision-making algorithms responsible for assigning vehicle tasks to MECs, with a focus on the latency metric. The study examines two types of vehicle applications-safety and infotainment-using the open source Simu5G simulator to assess how environmental complexity and different policies influence the delay. The results underscore the impact of these factors on the end-to-end (E2E) latency and highlight potential enhancements. Future work will explore strategies to further reduce E2E latency, improving the overall responsiveness and efficiency of VEC systems.

Farmers face the formidable challenge of meeting the increasing demands of a rapidly growing global population for agricultural products, while plant diseases continue to wreak havoc on food production. Despite substantial investments in disease management, agriculturists are increasingly turning to advanced technology for more efficient disease control. This paper addresses this critical issue through an exploration of a deep learning-based approach to disease detection. Utilizing an optimized Convolutional Neural Network (E-CNN) architecture, the study concentrates on the early detection of prevalent leaf diseases in Apple, Corn, and Potato crops under various conditions. The research conducts a thorough performance analysis, emphasizing the impact of hyperparameters on plant disease detection across these three distinct crops. Multiple machine learning and pre-trained deep learning models are considered, comparing their performance after fine-tuning their parameters. Additionally, the study investigates the influence of data augmentation on detection accuracy. The experimental results underscore the effectiveness of our fine-tuned enhanced CNN model, achieving an impressive 98.17% accuracy in fungal classes. This research aims to pave the way for more efficient plant disease management and, ultimately, to enhance agricultural productivity in the face of mounting global challenges. To improve accessibility for farmers, the developed model seamlessly integrates with a mobile application, offering immediate results upon image upload or capture. In case of a detected disease, the application provides detailed information on the disease, its causes, and available treatment options.

Present dynamic performance reputation and technical innovations in Internet of Things (IoT) technologies have endowed ultra-inexpensive, energy effcient, smart, and tiny IoT gadgets. IoT gadgets can be easily implanted inside, attached to, or placed around the chronic patient body, and they can be employed in several healthcare monitoring systems such as mobile, wearable, and implantable healthcare monitoring for chronic diseases. Healthcare monitoring for chronic diseases is one of the major applications of IoT and is also a typical challenging area. The rapidly rising proportion of patients with chronic diseases brought enormous pressure on governments and healthcare providers and required up-to-date long-term healthcare service and continuous monitoring. In this survey paper, we extensively review numerous industrial and non-commercial contemporary healthcare monitoring systems (HMS) and applications. We present a design layout of IoT-based HMS for chronic diseases. We presented societal and technological challenges associated with the design of IoT-based HMS and their solutions. To accomplish this, more than 80 different healthcare monitoring systems have been characterized and classied. Moreover, we describe contemporary healthcare monitoring networks and communication technologies. This review also presents the dynamic capabilities of key IoT technologies for chronic diseases healthcare monitoring to show dedicated research pathways to IoT researchers. Last, we deeply analyze different healthcare monitoring systems and describe open issues that will help researchers and healthcare system designers design future systems.

5G/6G network slicing is identified as key enabler technology for allowing a diversity of sustainable applications while satisfying user’s requirements. A major problem of the traditional networking technologies is the use of the ”one size fits all” approach that handles all types of services. In this work, we focus on drawing a new architecture born of the need of a multi-level services’ orchestration for seeking flexible intelligent management of the new generation use-cases such as massively deployed cloud and edge-cloud based IoT applications for (i) environment monitoring, (ii) Agriculture development, (iii) and new generation Augmented Reality applications in medicine or e-learning.Thispaperproposesanovelmulti-levelDelegationArchitecture for Network Slicing Orchestration (DANSO). DANSO proposes a multi-level delegation for slices management and optimization framework for resources allocation. Our proposal is based on three pillars: (i) definition of levels to fulfill support of network slicing deployment and management via delegated decisions (ii) split of orchestrator, manager and control roles and (iii) placement-independence of entities in (ii). DANSO has the following advantages. First, it provides an optimization mechanism for the deployment of network slices. second, it takes into consideration a negotiation process for the requested slice. Third, it manages the life-cycle not only for the Virtual Network Function (VNF) level but also for the dynamic deployment and suppression of the whole slices. last, it considers network slicing over several administrative domains.

Recent advances in telecommunication and machine learning (ML) have allowed for new smart and autonomous vehicle applications to improve road safety, environmental conditions, and traffic management through Vehicle to Vehicle (V2V) or Vehicle to Infrastructure (V2I) communication. However, with the rise of advanced cyber-attacks, the authenticity of a message guarantees its source but not its correctness. To mitigate the new sophisticated attacks, new Misbehavior Detection Systems (MDS), that use machine learning algorithms to detect misbehaving vehicles, have been proposed. This work provides first a comprehensive review of recent developments in ML-based MDS technology within a Vehicular Ad-Hoc Network (VANET) context, covering data collection, feature selection, model training, model evaluation and deployment. We survey useful public datasets and summarize recent studies. We report useful pieces of information for every work. In particular, we highlight the considered dataset for ML training, list the selected ML models, indicate the feature selection and dimensionality reduction techniques, recapitulate the main results, report the performance metrics and mention the deployment guidelines when applicable. Then, we compare the surveyed studies discussing not only the strength points but also their limitations. One of the key observations from the surveyed works is the absence of a quantitative analysis of the proposed models' execution time, which is a crucial performance metric considering the limited on-board and edge computing resources. To develop a feasible ML-based MDS for V2X communication, it is essential to address this issue and propose a deployment strategy that optimizes the allocated resources for this technology. However, achieving this remains a challenge. Moreover, in view of the fact that data generation and analysis are critical phases in this technology. Also, using simulation has many advantages over the real data collection, we provide a tutorial on how realizing a useful dataset collection with popular open-source tools while considering exemplar types of attacks. Last, we demonstrate through the tutorial the use of the collected dataset for ML-based MDS model selection and training. An open source github repository is provided for regenerating the whole explained scenarios and modify according to the given research issue.

The metaverse, an emergent interconnected network that harmoniously merges digital and physical realities, represents a revolutionary paradigm in the computing realm, engendering a nexus of immersive, interactive experiences through user avatars. This new digital landscape, forged by the advancement of immersive technologies like virtual and augmented reality, coupled with the sophistication of artificial intelligence, blockchain, and edge computing, presents diverse prospects from innovative experiential creations to the resolution of complex issues like remote work and virtual social engagement, to remote surgeries, immersive learning and so on. Nevertheless, it confronts obstacles, including privacy, security, equitable access, and ethical concerns, necessitating the construction of robust legal and ethical frameworks for the common good. This research, a comprehensive examination of this burgeoning phenomenon, systematically scrutinizes its underpinning constructs and trailblazing applications via databases such as ScienceDirect, ResearchGate, and IEEE Xplore. It uncovers the metaverse's incarnations in gaming, social platforms, education, and healthcare, signifying its transformative capacity across these sectors. The exploration underscores the imminent requirement of addressing legal and ethical dimensions as we move towards this novel digital existence, thereby paving the way for future research to architect a secure, efficient, and inclusive metaverse.

In recent years, the deployment of Vehicular Ad hoc Networks (VANETs) has gained significant attention due to their potential to enhance road safety and traffic efficiency. However, the dynamic nature of VANETs makes them vulnerable to various security threats, including attacks on network infrastructure and misbehavior of individual vehicles. To address these challenges, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as promising solutions for the detection of attacks and misbehavior in VANETs. In this paper, we present an empirical evaluation of ML and DL approaches for misbehavior detection in the context of VANETs using realistic simulation. We employ a synthetic generated dataset that includes a wide range of attacks commonly encountered in VANETs. To simulate realistic scenarios, we utilize a popular widely used and validated network simulator (i.e., Omnet++) with different open source projects to generate VANET-specific traffic patterns and communication dynamics. An useful overview of the whole process from data generation, passing by pre-processing and model training to performance evaluation is provided with an open source guithub repository. Our evaluation encompasses different ML and DL algorithms, including support vector machines (SVM), random forests (RF), convolutional neural networks (CNN), and recurrent neural networks (RNN). We assess the performance of these approaches by measuring key metrics such as accuracy, precision, recall, and F1-score. Additionally, we compare the computational efficiency of the algorithms to identify their suitability for real-time deployment in VANET environments.

Network slicing plays a crucial role in the progression of 5G and beyond, facilitating dedicated logical networks to meet diverse and specific service requirements. The principle of End-to-End (E2E) slice includes not only a service chain of physical or virtual functions for the radio and core of 5G/6G networks but also the full path to the application servers that might be running at some edge computing or at central cloud. Nonetheless, the development and optimization of E2E network slice management systems necessitate a reliable simulation tool for evaluating different aspects at large-scale network topologies such as resource allocation and function placement models. This paper introduces Slicenet, a mininetlike simulator crafted for E2E network slicing experimentation at the flow level. Slicenet aims at facilitating the investigation of a wide range of slice optimization techniques, delivering measurable, reproducible results without the need for physical resources or complex integration tools. It provides a well-defined process for conducting experiments, which includes the creation and implementation of policies for various components such as edge and central cloud resources, network functions of multiple slices of different characteristics. Furthermore, Slicenet effortlessly produces meaningful visualizations from simulation results, aiding in comprehensive understanding. Utilizing Slicenet, service providers can derive invaluable insights into resource optimization, capacity planning, Quality of Service (QoS) assessment, cost optimization, performance comparison, risk mitigation, and Service Level Agreement (SLA) compliance, thereby fortifying network resource management and slice orchestration.