In emergency scenarios such as earthquakes and floods, Internet-of-Things (IoT) systems composed of heterogeneous devices—including wearable medical sensors, ground gateways, and unmanned aerial vehicles (UAVs)—are increasingly used to collect and relay real-time vital signs to edge nodes for rapid analytics and to cloud services for long-term storage. Although clustering is widely adopted to organize UAV ad hoc networks for efficient IoT data routing, the failure of a cluster head (CH) can fragment the network and result in severe data loss. Existing routing protocols often prioritize latency reduction while overlooking CH continuity and reliability, which are critical in crisis-driven e-health applications. To address these challenges, this work proposes a multi-agent redundant clustering strategy that enhances CH availability while maintaining efficient data delivery. A weighted clustering metric is introduced, incorporating distance stability, reward-based stability, velocity stability, and energy stability to improve cluster formation. In addition, a redundant CH is pre-designated to seamlessly assume routing responsibilities in the event of a CH failure, ensuring uninterrupted data transmission. The proposed protocol is implemented and evaluated through MATLAB simulations and compared against the LEACH protocol. Simulation results demonstrate that the proposed approach improves packet delivery ratio and throughput by approximately 0.7-1.0% and enhances forwarding efficiency by about 0.8%, indicating more reliable and effective data forwarding. While a marginal increase in average end-to-end delay is observed, the delay remains within acceptable limits for e-health monitoring scenarios. Overall, the proposed redundant clustering strategy significantly improves routing reliability and data delivery performance, thereby enhancing the dependability of emergency e-health communication systems.

Ensuring secure and efficient surveillance in maritime border security is critical to addressing threats such as illegal trafficking, unauthorized vessel movements, and piracy. This paper presents a novel Autonomous Aerial Vehicle (AAV)-assisted surveillance framework that leverages QoS-aware edge computing to enhance real-time situational awareness, task offloading, and secure trajectory optimization. The proposed system integrates Twin-Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning for adaptive AAV trajectory planning, ensuring optimal coverage and minimal energy consumption. Enhanced Particle Swarm Optimization (EPSO) is also employed for intelligent task offloading, efficiently balancing computational workloads between AAVs and edge nodes. It is evaluated through simulations with real-world maritime surveillance scenarios, demonstrating reduced latency and improved energy efficiency compared to conventional surveillance and task management strategies.

Green communication and sustainable operations have become critical objectives in Information and Communications Technology (ICT) systems, particularly when integrating energy-intensive technologies such as Autonomous Aerial Vehicles (AAVs). Therefore, this paper introduces a dynamic resource allocation framework for AAV-enabled green edge networks that adaptively manages bandwidth and computational power while optimizing AAV trajectories. By explicitly formulating the problem as a Markov Decision Process (MDP) and employing Deep Reinforcement Learning (DRL) with Proximal Policy Optimization (PPO), the proposed system strikes a balance between high data synchronization demands and strict energy constraints, leading to improved throughput and sustainability. The simulation results reveal that this approach significantly boosts data throughput and communication efficiency while reducing energy consumption. These findings pave the way for environmentally responsible edge networks that meet both performance requirements and sustainability targets.

The proposed system known as Vision Transformer-Masked Autoencoder-Cheetah Optimization Algorithm (ViT-MAE-COA) uses image preprocessing techniques in addition to segmentation and classification features and hyperparameter optimization capabilities to classify Otitis Media. The framework starts by improving image quality through the Wiener filter (WF) that minimizes mean squared error between original images and noisy images for noise reduction purposes. The W-Net architecture processes segmented data to maintain essential localization data and content information through a strategy that decreases parameters with max pooling. The results indicate that the model exhibited better prediction accuracy than other Deep Learning models.

The Internet of Medical Things (IoMT) is transforming healthcare by enabling real-time monitoring, diagnostics, and secure data-driven decision-making. However, IoMT networks are vulnerable to adversarial attacks, data breaches, and privacy threats, making secure and optimized node deployment a critical challenge. This paper presents a novel framework integrating 3D Voronoi diagrams and K-means clustering with a hybrid Particle Swarm Optimization-Genetic Algorithm (PSOGA) to optimize IoMT node placement while enhancing security and resilience. Initially, K-means clustering distributes nodes, followed by spatial partitioning with 3D Voronoi diagrams. The PSO-GA hybrid algorithm then iteratively refines node positions, balancing rapid convergence with global exploration to achieve optimal configurations that improve coverage, energy efficiency, and secure data exchange. Additionally, the proposed approach integrates risk assessment techniques and privacypreserving mechanisms to mitigate adversarial threats, ensuring robustness against poisoning and evasion attacks. By dynamically adapting to changing healthcare environments, the framework enhances network resiliency while aligning with AI security and privacy-by-design principles. Experimental results validate the algorithm's scalability and effectiveness, making it a promising solution for real-world IoMT applications in secure medical monitoring, diagnostics, and AI-driven threat intelligence.

In Intelligent Transportation Systems (ITS), driver recognition presents challenges of data privacy, computational efficiency, and energy consumption. Optimizing energy use in ITS has become crucial with the rise of environmentally conscious Information and Communication Technology (ICT) practices. Therefore, this paper introduces a privacy-preserving and energy-efficient task offloading strategy using Deep Reinforcement Learning (DRL) and Federated Learning (FL) within a network leveraging Smart Traffic Cameras (STCs) for edge computing. Initially, the public transports employ a DRL-based strategy to offload tasks to STCs, optimizing network resources and minimizing energy use. At the second phase, allows private vehicles to train models locally, offloading only model parameters, thus ensuring data privacy and reducing communication energy costs. Finally, aggregates these parameters at a central cloud server, refining a Network-Wide Model (NWM). The proposed framework enhances model performance, preserves privacy, and improves computational efficiency, reducing the carbon footprint of ITS operations. Simulations demonstrate that DRL's Actor-Critic Algorithm (ACA) reduces task latency and energy consumption while FL ensures efficient model training with minimal communication overhead.

Malware detection is increasingly challenged by evolving techniques like obfuscation and polymorphism, limiting the effectiveness of traditional methods. Meanwhile, the widespread adoption of software containers has introduced new security challenges, including the growing threat of malicious software injection, where a container, once compromised, can serve as entry point for further cyberattacks. In this work, we address these security issues by introducing a method to identify compromised containers through machine learning analysis of their file systems. We cast the entire software containers into large RGB images via their tarball representations, and propose to use established Convolutional Neural Network architectures on a streaming, patchbased manner. To support our experiments, we release the COSOCO dataset-the first of its kind-containing 3364 largescale RGB images of benign and compromised software containers at https://huggingface.co/datasets/k3ylabs/cosoco-imagedataset. Our method detects more malware and achieves higher F1 and Recall scores than all individual and ensembles of VirusTotal engines, demonstrating its effectiveness and setting a new standard for identifying malware-compromised software containers.

Network slicing has emerged as a transformative enabler for meeting the diverse requirements of 5G and beyond networks, including 6G. However, network slices’ dynamic and virtualized nature introduces significant security challenges, particularly against evolving cyber threats. We propose a Deep Reinforcement Learning (DRL)–based Moving Target Defense (MTD) strategy tailored for secure network slicing to address these challenges. Our approach utilizes a Q-Learning framework to manage MTD actions dynamically, optimizing security while maintaining service quality. Extensive simulations demonstrate the effectiveness of our framework in minimizing attack success rates and ensuring operational stability, significantly outperforming baseline methods such as random decision-making.

This paper addresses the challenge of device localization within a Wi-Fi network. It introduces a novel localization technique, using a pre-constructed map with predefined positions around an Access Point (AP). The Wi-Fi client devices (STAs), are positioned on the map utilizing the capabilities of the Fine Time Measurement (FTM) protocol (IEEE 802.11mc), and tracked using the Pedestrian Dead Reckoning (PDR) method, which leverages the device’s Micro-Electro-Mechanical Systems (MEMS). It also harnesses the capabilities of Neighbor Awareness Networking (NAN) technology, to discover the distances between Wi-Fi Aware certified devices. This method requires no additional equipment or prior setup, since one AP is sufficient to determine the STA’s position. The findings suggest that this approach provides a scalable and efficient solution for IoT applications.

Modern IoT and Fog environments are complex and diverse ecosystems that consist of numerous devices. Some of these devices can receive and process offloaded tasks. For such devices to operate at the highest capacity levels, there is a need for mechanisms that could optimize their performance with offloaded tasks. That includes, but is not limited to, such aspects as resource management, workload balancing and scheduling. Unlike local tasks, offloaded ones are not a part of device’s environment. Therefore, processing them should not irreparably disrupt a device’s functionality. This requires devices to have a mechanism for managing offloaded tasks differently from their local. The current work attempts to research possible ways to optimize in-device execution of offloaded tasks, while reducing detrimental effects to a device’s state. To achieve that, the solution involves application of Reinforcement Learning techniques. The work proposes to utilize Deep Deterministic Policy Gradient (DDPG) Actor/Critic method, to allow devices continuously learn optimal scheduling strategies for offloaded tasks. The contribution of this work is in its exploration of the impact machine learning makes on in-device scheduling, application feasibility and the overall execution time optimization of offloaded tasks.

This paper researches the system evaluations of the reconfigurable intelligent surface (RIS)-assisted satellite aerial terrestrial integrated systems. To ensure the stability of the regarded network, a link selection scheme is presented to get the balance between the system performance and the system efficiency. Besides, in order to build a practical environment of the transmission networks, the imperfect hardware, channel estimation errors and co-channel interference are both considered in the networks. Relied on the above considerations, the detailed analysis for the outage behaviors is shown along with the asymptotic outage probability in high signal-to-noise ratio scenarios. Moreover, the diversity order and coding gain are also provided to give fast methods to confirm the system evaluation. Finally, some re-presentative simulations are provided to confirm the efficiency and advantage of analytical results and the proposed link selection scheme.

Public key cryptography is nowadays a crucial component of global communications which are critical to our economy, security and way of life. The quantum computers are expected to be a threat and the widely used RSA, ECDSA, ECDH, and DSA cryptosystems will need to be replaced by quantum safe cryptography. The main objective of the HORIZON Europe PQ-REACT project is to design, develop and validate a framework for a faster and smoother transition from classical to quantum safe cryptography for a wide variety of contexts and usage domains that could have a potential interest for defence purposes. This framework will include Post Quantum Cryptography (PQC) migration paths and cryptographic agility methods and will develop a portfolio of tools for validation of post quantum cryptographic systems using Quantum Computing. A variety of real-world pilots using PQC and Quantum Cryptography, i.e., Smart Grids, 5G and Ledgers will be deployed to validate the defined framework.

With the ongoing integration of machine learning models into critical infrastructure, the resilience of these systems against adversarial attacks is important for all domains. This paper introduces an adversarial attack generator framework against a network dataset that is part of OCPP Dataset using CI-CFlowMeter parser. We conduct a comprehensive evaluation of various prominent adversarial attacks, including FGSMA, JSMA, PGD, C&W, and more to assess their efficacy on the OCCP dataset. The Adversarial Generator is meticulously evaluated, demonstrating a significant impact in the models performance to detect potential perturbations. The results showcased the impact of the different type of adversarial attacks, contributing to a critical advancement in future defense strategies that need to be utilised in order to protect industrial control systems.

Nowadays, numerous devices are utilizing the IoT world, connecting and providing access to data and sensor measurements in vast networks of interconnected objects and devices. Considering the great communication distances that need to be covered occasionally, the LoRaWAN network was proposed as it employs Low Power (LP) and Long Range (LoRa) protocols that reduce device energy consumption while maximizing communication range. A gateway to the cloud authenticates LoRaWAN IoT devices before data transmission. This procedure begins with an unencrypted Join Request. A Join Request includes, among others, a Message Integrity Code (MIC), which is the result of encrypting the unencrypted contents of the message using an AppKey that is securely stored both in the cloud and the IoT device. However, malicious actors acting as Man-In-the-Middle (MITM) can interfere in the communication channel, reverse engineer the MIC value, and derive the AppKey. They can then initiate a Join Request that is misinterpreted as coming from a legitimate device and gain access to the communication channel. This paper introduces a novel approach that focuses on the continuous regeneration of the AppKey, necessitating frequent re-joining and re-authentication of IoT devices within the network. The suggested method, which can be added as an extra layer of security in LoRaWAN networks, uses a key rolling technique similar to the one used in automobile central locking systems, and is developed as an optimised and scalable microservice for various LoRaWAN installations and versions. Through the evaluation process, significant findings emerged, demonstrating the effectiveness of the proposed security solution in mitigating replay attacks. The system successfully prevented the server from getting flooded by malicious packets, distinguishing it from a system lacking the proposed mechanism. Remarkably, this accomplishment was made without causing any noticeable delay to the communication process. In addition, the timeframe required by the proposed mechanism to generate the new AppKey is assumed to be too short for attackers to execute a replay attack, considering the computational resources currently accessible.

Smart spaces integrate advanced technologies like the Internet of Things (IoT), Machine Learning, and Artificial Intelligence (AI) to enhance automation and control within various environments. Effective deployment of IoT nodes is crucial for maximizing coverage, minimizing costs, and ensuring network stability in these spaces. This paper presents a novel approach combining 3D Voronoi diagrams with a modified Genetic Algorithm (GA) to optimize IoT node placement in three-dimensional environments. The proposed method starts with node placement using a homogeneous Poisson Point Process (PPP) and partitions the space into Voronoi cells, followed by iterative adjustments using the modified GA. The method achieves a 15% improvement in coverage ratio, a 10% reduction in deployment effort, and a 20% increase in network stability compared to existing algorithms, with results statistically significant at 5%. Moreover, optimising sensor placements indirectly enhances network security by reducing redundant data paths and strengthening network resilience. This study provides a scalable, efficient solution for IoT network deployment in complex environments, addressing key challenges in smart space optimization and paving the way for more secure and robust IoT infrastructures.

Swarm of UAVs (S-UAVs) is the term used to describe an unplanned gathering of un-manned aerial vehicles (UAVs) that collaborate to complete predefined missions. Any UAV has a risk of being damaged in crisis situations like fire. Clustering is one of the most often used routing algorithms in S-UAVs. The clustering scheme divides the UAVs into clusters, with a cluster head (CH) and cluster members (CM) in each cluster. The CH plays a critical role within inter-cluster communication, and because of this, its selection is an ongoing area of study. A clustered weighted scheme with redundancy and dynamic weight adjustment is proposed in this paper. The selection of the principal CH, redundant CHs, and CMs is based on a weighted formula composed of distance, speed, and rewarding index. Whenever the primary CH is damaged, the redundant CH takes its place immediately. After the first clustering process, the proposed scheme dynamically and autonomously adjusts the weights to optimize the UAV role selection. According to the outcomes of the carried-out simulation, this is a promising scheme that reduces data loss in a crisis-case scenario and optimizes the time delay through the dynamic adjustments of the weights.

Integrating Unmanned Aerial Vehicles (UAVs) into the emerging sixth-generation and beyond (6G+) cellular networks as aerial base stations represents a significant technological advancement. This integration offers numerous benefits, including widespread accessibility, enhanced navigation, and simplified monitoring and management. A key element of this integration involves the instantaneous distribution of vital information throughout the transportation infrastructure. Characterized by their agility, mobility, and flexibility, UAVs play a crucial role in relieving data traffic loads, thereby offering additional access points. This function is essential for making prompt, precise, and well-informed decisions in Intelligent Transportation Systems (ITS), utilizing data-centric insights. Deploying versatile Road Side Units (RSUs) for secure data collection and dissemination requires a robust framework for safe data transfer. Ensuring data governance in the Internet of Vehicles (IoV) network relies heavily on specific interactions between trusted parties. In response, we introduce an advanced encryption approach to promote secure data exchange in ITS, thus supporting the confidential transfer of information in IoV communications. This innovative encryption method can also perform encryption and decryption of ciphertexts, encompassing confidential data and facilitating secure communication.

This study presents an innovative approach to enhancing the efficiency of Unmanned Aerial Vehicles (UAV) in IoT networks. Employing UAVs as flying relays focuses on their role in data collection and support for terrestrial cellular networks. The central innovation lies in the application of Federated Learning (FL), which processes data while ensuring user privacy and reducing communication overhead. Addressing the challenge of UAVs’ limited battery life, which restricts sustained FL operations, we introduce the Enhanced UAV Network optimization Algorithm with Adaptive Spatial Play (ENUO-ASP). ENUOASP incorporates a modified Particle Swarm optimization (PSO) technique to determine optimal UAV placements, enhancing data collection by focusing on the Signal-to-Interference Ratio (SINR). Additionally, the paper utilizes the Deep Deterministic Policy Gradient (DDPG) method for dynamic resource allocation, optimizing energy consumption and reducing link latency between the UAV network and users. The findings indicate that the ENUO algorithm outperforms existing methods by achieving higher data rates and balanced SINR. Furthermore, the ASP resource allocation strategy improves FL execution, significantly lowering latency and energy use. This research contributes to the UAV-enabled communication field, offering a more efficient and performance-driven solution for advanced IoT applications.

This paper explores the integration of Quantum Communication Networks (QCNs), semantic communication, and covert communication within the context of 6G and future wireless networks. Introducing a new Quantum Semantic Communications (QSC) framework that capitalizes on advancements in quantum machine learning and semantic representations, this framework dramatically enhances resource efficiency in QCN s. It does so by embedding only relevant classical data into compact, high-dimensional quantum states for transmission, achieving a potential resource reduction of 50-75% while boosting quantum semantic fidelity. The paper further examines Artificial Intelligence's (AI) transformative role in upgrading tra-ditional communication paradigms into more efficient semantic communication systems, utilizing deep learning and end-to-end methodologies to ensure precise conveyance and interpretation of meanings in transmitted information. Additionally, it explores incorporating covert communication strategies within systems supported by a Reconfigurable Intelligent Surface (STAR-RIS) and Non-Orthogonal Multiple Access (NOMA), emphasizing the enhanced security and stealth necessary for modern networks. By merging these sophisticated communication strategies, the paper anticipates a new era of telecommunications that significantly surpasses existing security, efficiency, and semantic accuracy capabilities, marking a progressive step towards future networks optimized for secure, efficient, and meaning-focused communication in the quantum and AI era.

Digital transformation is a method where new technologies replace the old to meet essential organisational requirements and enhance the end-user experience. Technological transformation often improvises the manner in which a facility or resources are delivered to the recipient. Data is one of the key assets of every organisation which influences significantly reaching the long-term objective. Thus, the entities, as well as technologies involved in the data management process, have a significant role to play to secure different data types. However, the traditional data governance process often follows a centralised approach and thus resulting in various cyber attacks, whereas the distributed approaches are mostly research prototypes and often comprise various security challenges. Security incidents such as data theft fabricate the integrity of confidential data and thus the consequences are often disastrous. To address the challenges, we introduce TRUSTEE, a data-driven platform which aims to provide a secure and privacy-by-design framework to empower companies, organisations, and individuals to access different data domains, use and re-use the data and metadata to extract knowledge with trust and confidentiality. In this paper, we assess the effectiveness of the platform by reviewing the potential challenges and threats associated with the incorporated technologies. Our research emphasises the efficacy of distributed technologies to indicate their significance in data integrity and security.

The revolution of Artificial Intelligence (AI) has brought about a significant evolution in the landscape of cyberattacks. In particular, with the increasing power and capabilities of AI, cyberattackers can automate tasks, analyze vast amounts of data, and identify vulnerabilities with greater precision. On the other hand, despite the multiple benefits of the Internet of Things (IoT), it raises severe security issues. Therefore, it is evident that the presence of efficient intrusion detection mechanisms is critical. Although Machine Learning (ML) and Deep Learning (DL)-based IDS have already demonstrated their detection efficiency, they still suffer from false alarms and explainability issues that do not allow security administrators to trust them completely compared to conventional signature/specification-based IDS. In light of the aforementioned remarks, in this paper, we introduce an AI-powered IDS with explainability functions for the IoT. The proposed IDS relies on ML and DL methods, while the SHapley Additive exPlanations (SHAP) method is used to explain decision-making. The evaluation results demonstrate the efficiency of the proposed IDS in terms of detection performance and explainable AI (XAI).

The three pillars of health data exchange, confidentiality, integrity and availability, pose a significant challenge to the efficiency and robustness of the healthcare ecosystem. By utilising fragmentation, sensitive attributes dissociate, and thus, data security can be enhanced, and data utility can be improved. Throughout this research, confidentiality was performed by deploying polynomials and Newton-Gregory's divided difference interpolation to enable encryption of confidential data values such as patients' IDs. The fragmentation technique was utilised to achieve integrity, and the utility method enabled end-user availability. Extensive evaluations show that the precision, recall, and Fl-score under different values of correlation index ϒ of the proposed methodology outperform state-of-the-art approaches. Also, a time complexity comparison for overhead tasks was implemented between these approaches.

A swarm of unmanned aerial vehicles (S-UAVs) consists of UAVs flying together with the target of accomplishing a certain task in a faster and more reliable way as compared to a single UAV. In a crisis scenario, UAVs have been widely used in rescue missions. Clustering is one of the most reliable routing schemes for S-UAVs. The UAVs are grouped into clusters with a cluster-head (CH) and cluster-members (CM). The CH plays a major role in clustering schemes as it handles all inter-cluster communication. In a crisis case, any UAV is at risk of getting non-functional, thus resulting in a disconnected cluster. This paper proposes a new clustering scheme based on K-means and weighted formulas. The K-means protocol is applied to generate pilot phase clusters. Afterward, whenever the metrics of the networks are established, the weighted formula is applied for cluster formation and CH selection. The weighted formula is based on the performance index, the relative movement, and the remaining energy. To ensure end-to-end communication despite CH non-functionality, our proposed protocol selects a redundant CH for every CH. This protocol had been simulated using MATLAB. The results obtained and analyzed towards the end of this paper demonstrate that the proposed scheme is very promising.

The endeavours to interpret the acquired data are combined with the efforts to strengthen the smart city's multidimensional framework. As the name implies, smart cities are built atop more intelligent data. However, it is a significant challenge because Big Data needs to be evaluated to provide interpretation for a posterior evolution of the current technology. Therefore, using the right building blocks is vital, aligned with clear and convincing guidelines on best practices. To achieve a scale of evaluation, we need standards. The intertwining development drivers need to define how we see and measure the world around us and how this Big Data in the era of IoT informs the decision-making processes. Hence, we are introducing an evaluation model for Big Data obtained from the assessment of Quality of Service (QoS) and Quality of Experience (QoE) delivery in an urban environment. Using the Best-Worst Method (BWM) combined with the orientation of Intuitionistic fuzzy sets. We obtained intuitive preference information based on various criteria. Thus, by prioritizing these end-user predilections and transmitting them into adaptable technological improvements, we achieved a significant step toward sustainable Smart Cities.

A spontaneous group of unmanned aerial vehicles (UAVs) is denoted as a swarm of UAVs (S-UAVs). The UAVs communicate wirelessly and cooperate to accomplish tasks. In crisis scenarios such as flooding or earthquakes, all UAVs are at risk of getting damaged and thus non-functional. A non- functional UAV will result in a disconnected network, especially if that UAV is highly responsible for packet forwarding. S- UAV s are dynamic networks; clustering is one of the most adopted routing schemes in S-UAVs. The clustering scheme groups the UAVs into clusters where each cluster is formed of a cluster head (CH) and cluster members (CMs). Only the CH can handle inter-cluster communication. Due to the crucial role played by the CH, its selection is a continuous field of research. This paper proposes an enhanced clustered weighted scheme with redundancy to ensure end-to-end communication. The proposed scheme is based on a weighted formula for the primary CH, redundant CH, and CMs selection. The weighted formula calculates a cluster index based on the distance, the speed, and the reward index. A new component is added to the reward index which is performance. The redundant CH is selected to automatically replace the primary CH whenever it is damaged. If the redundant CH becomes inoperable, the second redundant CH will take over. Each cluster is formed of n CMs and will have n-2 redundant CHs. The results obtained from the conducted simulation experiments concluded that this promising scheme decreases data loss in a crisis case scenario.

Smart spaces are a rapidly emerging concept in technology. They result from the convergence of various novel technologies, such as the Internet of Things, Machine Learning and Artificial Intelligence, which allow for greater levels of automation and control within physical environments. The devices which are connected to the IoT network are equipped with sensors to acquire and exchange data. As a result, the IoT has transformed how we live, work, and play. However, the deployment in smart spaces is not always the best due to the issues arising from network node positioning. Therefore, we are investigating solutions to this problem with a novel approach which utilises Voronoi diagrams in conjunction with the algorithmic genetic technique. First, the initial positions of the IoT nodes will be determined by simulating a homogeneous Poisson point process in the smart space environment. Then, after dividing the area into the Voronoi cells, the genetic algorithm will optimise the position towards achieving full network coverage within the smart space. Experimental results prove the 100% network coverage within the specified area.

Sixth-generation networks and vehicular Ad hoc networks advancement brought us to the cusp of a new era. Autonomous Vehicles with a plethora of advanced applications require a substantially enhanced vehicle-to-everything communication network. The infrastructure should efficiently support hyper-fast, ultra-reliable, and low-latency massive data exchange. Roadside units were initially exploited as a promising communication solution to overcome this challenge. However, the challenging integration with the infrastructure led to the investigation of additional solutions. Unmanned aerial vehicles are one of the most dominant assistive solutions due to their inherent advantage of mobility. Moreover, air-to-air and ground-to-air networks are more efficient than ground-to-ground. Nevertheless, it is a prerequisite to leverage the potential of unmanned aerial vehicles to attain nationwide Vehicle-to-Everything connectivity. Therefore, we focused our research orientation on developing a strategy to optimize the network’s coverage within the intelligent transportation systems framework. In particular, we have deployed an innovative algorithmic technique that constructs Voronoi diagrams using circles. Besides, we applied the poison point process to determine the optimum locations of the transceivers’ establishment. Simulation results illustrate full network coverage for the tested area after the required iterations. Also, time complexity evaluation proved the simplicity of the proposed algorithm.

The rapid growth of computer networks in various sectors has led to new services previously hard or impossible to implement. Internet of Things has also assisted in this evolution offering easy access to data but at the same time imposing constraints on both security and quality of service. In this paper, an SDN fault tolerant and resilient SDN controller design approach is presented. The proposed solution is suitable for a wide range of environments. Benefits stemming from actual scenarios are presented and discussed among other solutions.

The transformation of the conventional electrical grid into a digital ecosystem brings significant benefits, such as two-way communication between energy consumers and utilities, self-monitoring and pervasive controls. However, the advent of the smart electrical grid raises severe cybersecurity and privacy concerns, given the presence of legacy systems and communications protocols. This paper focuses on False Data Injection (FDI) cyberattacks against a low-voltage distribution system, taking full advantage of Man In The Middle (MITM) actions. The first cyberattack targets the communication between a smart meter and an Active Distribution Management System (ADMS), while the second FDI cyberattack targets the communication between a smart inverter and ADMS. In both cases, the cyberattacks affect the operation of the distribution transformer, thus resulting in devastating consequences. Moreover, this paper provides an Artificial Intelligence (AI)-based Intrusion Detection System (IDS), detecting and mitigating the above cyberattacks in a timely manner. The evaluation results demonstrate the efficiency of the proposed IDS.

According to the current unprecedented pandemic, we realise that we cannot respond to every contagion novel virus as fast as possible, either by vaccination or medication. Therefore, it is paramount for the sustainable development of antiviral urban ecosystems to promote early detection, control, and prevention of an outbreak. The structure of an antivirus-based multi-generational smart-city framework could be crucial to a post-COVID-19 urban environment. Humanitarian efforts in the pandemic's framework deployed novel technological solutions based on the Internet of Things (IoT), Machine Learning, Cloud Computing and Artificial Intelligence (AI). We aim to contribute by improving real-time detection using data mining in collaboration with machine learning techniques through our research work. Initially, for detection, we propose an innovative system that could detect in real-time virus propagation based on the density of the airborne COVID-19 molecules-the proposal based on the detection through the isothermal amplification RT-Lamp [1]. We also propose real-time detection by spark-induced plasma spectroscopy during the internal airborne transmission process [17]. The novelty of this research work, called characteristic subset selection, is based on identifying irrelevant data. By deducting the unrelated information dimension, machine learning algorithms would operate more efficiently. Therefore, it optimises data mining and classification in high-dimensional medical data analysis, particularly in effectively detecting COVID-19. It can play an essential role in providing timely detection with critical attributes and high accuracy. We elaborate the teaching-learning method optimisation to achieve the optimal set of features for the detection.

Flying Ad hoc Network (FANET) is the result of Unmanned Aerial Vehicles (UAVs) that collaborate to perform tasks in several applications such as rescue, military, and others. The unmanned aerial vehicle which is also known as a drone improves routing as the line-of-sight probability is increased due to its three-dimensional movement capability. Drones can be used as aerial relays in complex communication scenarios. Routing protocols are necessary for FANETs to establish communication between UAVs. Various routing protocols have been reported for FANETs targeting to find the routing protocol with minimal overhead while establishing a reliable end-to-end transmission. Wireless Sensor Network (WSN) consists of sensor nodes and base stations. The wireless sensors collect different types of information for their surroundings. Then, this information is uploaded to the internet through the base station. In a crisis case scenario, the base station might be nonfunctional resulting in a data upload failure. This paper proposes to use the UAV as an aerial base station to compensate for the nonfunctional terrestrial base station. As delay is one of the major metrics in routing protocols this paper targets to study theoretically the transmission time of packets traveling from the sensors in the WSN to the UAV. Besides, a simulation had been carried using MATLAB to study the total transfer time with respect to several network variables such as the coverage radius, the number of levels in WSN, the angle of propagation of the UAV, and the UAV's speed.

Coronavirus disease 2019 (COVID-19) is currently the most crucial emerging virus in the world. The absence of licensed medication or vaccination leads to alternative strategies. A fundamental response plan implemented by all countries was the detection and isolation of infected cases. Contact tracing of infected citizens and testing every suspected case is a prerequisite to avoid new quarantine measures. Infected cases called ‘Orphan cases’ with no epidemiological connection are more worrying. The initial method to identify them should be knowing the probability for a citizen to be infected, given that presents specific symptoms, to be tested as a suspected case and not as random. This article proposes a cloud-based identification system that studies suspected cases to increase the likelihood that a positive result is correct. Also, it introduces an innovative solution to prevent and control the further spread of Corona-virus disease based on smartphones through the deployment of cutting-edge computing systems in the framework of a Naive Bayesian Network (NBN). Furthermore, the integration of Google Maps could provide geolocation risk assessment and early inferences to government health authorities to raise the test rates in risk- prone areas.

This paper presents a novel DVB/IP infrastructure that exploits P2P technology for optimised resource exploitation in interactive services' provision. Building upon a prototype DVB-T regenerative platform, it presents a decentralised architecture that exploits the broadcasting stream as part of the core/backbone network, providing interactive IP services to rural/urban citizens. Users access the provided IP services via intermediate communication nodes (access network), which are responsible for managing/controlling both uplink and downlink flows. Towards enhancing the scalability as well as the performance of the entire network, the paper studies the realisation of IP overlays by exploiting P2P technology, and proposes a prototype configuration for optimum resource exploitation and increased Service/Bandwidth gain both at the core and access segments. Performance evaluation experiments carried-out under real transmission/reception conditions verified the validity of the proposed architecture, besides outlining fields for future research.

The paper discusses a converged DVB/IP environment capable to provide differentiated services at a guaranteed quality. Towards this the paper presents the design, implementation and integration of QoS aware mechanisms in the DVB/IP environment in order to enhance its capability as an IP networking infrastructure, optimizing the system's for the provision of IP heterogeneous services. The capability of the proposed QoS aware DVB/IP network environment is validated through experimental tests that were conducted under real transmission/reception conditions at a prototype infrastructure that conforms to the discussed architectural design issues.

This paper introduces and validates an experimental infrastructure of a fusion IP/DVB networking environment for the provision of triple play services at remote areas exploiting the synergy of Broadcasting, Internet and Telecommunication sectors technologies. This synergy constitutes a challenge for creating a networking platform which exploits the particularities and complementarities of these sectors for the support of ubiquitous services and always-on connectivity enabling passive rural citizens to become active Information Society participants. This paper describes important directions for exploiting the proposed unified platform alleviating the digital divide that currently exists not only among countries but also within most regions of the same country.