The increasing complexity of cloud-native 6 G networks necessitates intelligent resource management to optimize scalability, energy efficiency, and service reliability. This paper presents an AI-driven self-healing mechanism for dynamic server activation within the a cloud-native system. The proposed framework integrates three key frameworks: the Management and Orchestration Framework (MOF) for policy-based network service orchestration, the Cloud Continuum Framework (CCF) for dynamic resource scaling, and the Artificial Intelligence and Machine Learning Framework (AIMLF) for predictive analytics and anomaly detection. By leveraging AI models, the system continuously monitors workload variations, forecasts resource demand, and dynamically scales computing resources, ensuring optimal energy efficiency and SLA compliance. The proposed self-healing workflow enables proactive server activation and deactivation, addressing load bursts and underutilization scenarios. Numerical evaluations, including real-world traffic data analysis, demonstrate that our approach significantly improves power consumption, load balancing, and resource utilization compared to traditional static resource allocation methods.

Wind energy plays a pivotal role in the global shift toward sustainable energy systems. However, the maintenance of wind turbines remains a significant challenge due to their distributed nature, harsh environmental exposure, and the high cost of unplanned downtime. In this work, a novel architecture for predictive maintenance of wind turbines based on continuous acoustic monitoring is presented, based upon OASEES—a decentralized, intelligent, and programmable edge framework that spans the full computing continuum. The proposed system leverages low-cost recording equipment to capture turbine-generated sound data, which are processed locally at the edge using Federated Learning, thus preserving data privacy and reducing communication overhead. A pre-trained deep learning model based on wav2vec is fine-tuned to classify turbine operational states, using labeled acoustic datasets. The effectiveness of the architecture, which, to the best of the authors' knowledge, is among the first to utilize the said distributed learning paradigm for acoustic-based wind turbine predictive maintenance, is validated in a proof-of-concept experimental setting using a publicly available relevant dataset, where both centralized and federated training methods are evaluated. The results demonstrate promising classification accuracy, with the federated model achieving over 78 % accuracy, closely matching the centralized baseline.

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in inspection and monitoring missions, yet onboard computation and communication impose significant energy burdens that limit flight time and operational scope. In this work, we introduce a novel, blockchain-enabled framework-grounded in the Distributed Autonomous Organization (DAO) paradigm-for orchestrating distributed analytics across a swarm of UAVs. Leveraging the OASEES project's smart-contract architecture, each drone embeds a Metrics Module for real-time power monitoring, a Behavioral Module for adaptive control, and a Blockchain Agent that autonomously proposes, votes on, and executes collective decisions. Three concurrent threads-Proposal Trigger, Voting, and Action Execution-enable fully decentralized governance of swarm behavior: from detecting critical energy thresholds and formulating swarm-wide conservation maneuvers, to executing approved strategies across all members. We validate our framework in a UAV-based infrastructure inspection scenario, employing a YOLOv5 object-detection pipeline to classify four corrosion classes on a telecommunications mast under three video-capture modalities (short-distance, long-distance, and horizontally concatenated streams). Across all configurations, our system achieves near-perfect precision, recall, and mean Average Precision (mAP50-95$\approx 0.995$), demonstrating both the efficacy of distributed workload inference and the feasibility of treating a single drone as a multi-feed processor. These results underscore the potential of DAO-driven UAV swarms for energy-aware, resilient aerial analytics, and pave the way for fully decentralized 5G/6G-enabled airborne networks.

The fifth-generation (5G) mobile network has shifted the paradigm in connectivity, high-speed data transmission, ultra-low latency, ultra-high throughput, multisense transmission, and ultra-high reliability. Today, industries are increasingly adopting 5G private networks, which also handle sensitive data related to business information, trade secrets, and personal data. Attacks on 5G private networks can potentially result in losing a competitive advantage since different security threats and vulnerabilities progressively target these networks. The unique infrastructure of the 5G network architecture and key enabling technologies exposes them to various vulnerabilities that attackers can target to breach sensitive data, steal information, and disrupt critical systems. Therefore, paying special attention to the security issues of 5G private networks is essential. The advancement of future wireless technology came about because of the different and diverse nature of connected devices, in contrast to the previous generation of mobile networks. The B5G network provides support to open network platforms, open interfaces, and the integration of different key enabling technologies helps manage network services and deploy new services needed for diverse requirements. At the same time, it has increased the attack surface compared to previous-generation networks. Therefore, it is imperative to conduct a review that focuses on addressing and classifying the different emerging threats in the private 5G and B5G networks in a distinctive way. In this paper, we have adopted a layered architecture from an industrial use case of 5G private networks to identify and classify different threats in 5G private networks. The study also characterized and modeled the different threats using information on the type of attack, entry points, and impact of the attack on the architecture layer. Moreover, an analysis of key enablers in 5G private and B5G networks and information on security threats and cyber-attacks is also presented. To accommodate the emerging threats in next-generation wireless technology, we have classified and modeled the different threats in the B5G domain using the common involved layer, which includes perception, network, and application layers in Hexa-x E2E and 6G IoT-enabled architecture. Organizations and projects that do not actively engage in cutting-edge technologies will lose their competitive advantage in the evolving technological landscape. This study has mapped the identified threat categories with different types of threats that could occur at different layers and assessed the entry points of the attacker. We have also identified the attack’s impact at each layer using security requirements related to confidentiality, availability, and integrity. Furthermore, this study reviews different AI-enabled solutions that can add value in preserving the security of 5G and B5G networks. Through this review analysis, we can conclude that the layered approach is quite beneficial in identifying the different threats and what security solutions can be offered to mitigate them.

Abstract The evolution from the Edge-Cloud Continuum to the Cognitive Computing Continuum (CCC) has introduced new challenges which necessitate advanced frameworks that integrate cognitive capabilities to enhance interoperability, adaptability, and resource efficiency. Considering insights from ongoing research and initiatives on the cognitive cloud, we identify the core concepts essential for transitioning to the CCC, including cognitive orchestration, distributed AI, and sovereign data management. We then introduce ENACT, a novel framework designed to embrace these concepts aiming to provide cognitive, highly adaptive orchestration to support modern hyper-distributed and data-intensive applications. Furthermore, ENACT employs bespoke mechanisms to enable dynamic continuum modelling and visibility as well as to facilitate application-level automation and adaptability. This paper presents the motivation behind the ENACT approach, reviews the state-of-the-art across its fundamental technological concepts and highlights its key innovations. Overall, it contributes to the formalization of the CCC architectural principles and, ultimately, to the realization of CCC.

The energy consumed by buildings is expected to significantly rise in the upcoming years, necessitating intelligent Home Energy Management Systems (HEMS) that create comfortable conditions for their inhabitants, while also offering sustainable and cost-effective solutions. The building environment, however, includes multiple time-varying parameters that cannot be controlled, such as the output of renewable energy sources, the market-dependent electricity prices, the outdoor temperature, as well as the occupants’ energy habits. To overcome these barriers, we propose a hybrid Machine Learning (ML) algorithm for smart HEMS control, leveraging the properties of a decision-making deep deterministic policy gradient model, enhanced by the predictive capabilities of long short-term memory networks. Hence, the proposed algorithm aims to achieve an optimal balance between energy cost and occupant comfort by continuously adjusting the energy provided to the heating, ventilation, and air conditioning system, as well as controlling the energy storage system of the smart home. The proposed hybrid method is validated with simulations using real-world data and compared against baseline approaches, showcasing its effectiveness to achieve an optimal trade-off between the indoor temperature deviation and the average energy cost.

Current and upcoming data-intensive Mission Critical (MC) applications rely on high Quality of Service (QoS) requirements related to connectivity, latency and network reliability. Beyond 5G networks shall accommodate MC services that enable voice, data and video transfer in extreme circumstances, for instance in occurrence of network overloads or infrastructure failures. In this work, we describe the specifications of the architectural framework that enables the roll-out of MC services over 5G networks and beyond, considering recent technological advancements of cloud-native functionalities, network slicing and edge deployments. The network architecture and the deployment process is described in three practical scenarios, including a capacity increase in the service load that necessitates the scaling of the computational resources, the deployment of a dedicated network slice for accommodating the stringent requirement of a MC application and a service migration scenario at the edge to cope with critical failures and QoS degradation. Furthermore, we illustrate the implementation of a Machine Learning (ML) algorithm that is used for overload prediction, validating its ability to predict the capacity increase and notify the components responsible to trigger the appropriate actions, based on a real dataset. To this end, we mathematically define the overload detection problem, as well as generalized prediction tasks in emergency situations and examine the key parameters (proactiveness ability, loockback window, etc.) of the ML model, also comparing its predictions abilities (~93% accuracy in overload detection) against multiple baseline classifiers. Finally, we demonstrate the flexibility of the ML model to achieve reliable predictions in scenarios with diverse requirements.

Maritime and shipping are unambiguously the cornerstones of the global economy and transportation. To improve efficiency, maritime sector activities are focused on the realization of Smart Shipping (SMS), leveraging 6G Communications, Energy Efficiency (EE) and Machine Learning (ML). However, conventional Centralized Machine Learning (CML) cannot be easily applied in the maritime, mainly due to the drawbacks: (i) prohibitive data communication overhead and bandwidth limitations, since CML requires centralization of massive data through transmissions from heterogeneous sources, (ii) excessive energy consumption associated with massive data transfers, (iii) remarkable transmission errors due to harsh propagation conditions, and (iv) data privacy violation, since the data carries sensitive and commercial information. This article proposes a two-fold Federated Learning (FL) scheme (FedShip) to improve the privacy, EE and communication-efficiency of future 6G maritime networks. FedShip uses the Over-the-Air computation (AirComp) principles to exploit the signal superposition property and ensure that local models are accurately and efficiently combined. Using real data regarding the fuel consumption of multiple cargo ships, we compared the FL performance, building multiple timeseries forecasting models, with collaborative ML baselines. AirComp performance was also assessed using simulation data about channel measurements. After optimizing the hyperparameters of the local models, extensive results revealed that: (i) FL shows enhanced fuel prediction accuracy (95.5% relative to the CML), while ensuring data privacy and (ii) AirComp can be adopted to combine the local models with low computation error, offering significant EE and spectrum efficiency improvements, especially when dense 6G scenarios are considered.

Parkinson’s Disease is the second most prevalent neurodegenerative disorder, currently affecting as high as 3% of the global population. Research suggests that up to 80% of patients manifest phonatory symptoms as early signs of the disease. In this respect, various systems have been developed that identify high risk patients by analyzing their speech using recordings obtained from natural dialogues and reading tasks conducted in clinical settings. However, most of them are centralized models, where training and inference take place on a single machine, raising concerns about data privacy and scalability. To address these issues, the current study migrates an existing, state-of-the-art centralized approach to the concept of federated learning, where the model is trained in multiple independent sessions on different machines, each with its own dataset. Therefore, the main objective is to establish a proof of concept for federated learning in this domain, demonstrating its effectiveness and viability. Moreover, the study aims to overcome challenges associated with centralized machine learning models while promoting collaborative and privacy-preserving model training.

In this paper the performance of an adaptive beamforming framework is evaluated, when deployed in fifth-generation massive multiple-input multiple-output millimeter wave cellular networks. To this end, active beams are formed dynamically according to traffic demands, in order to maximize spectral and energy efficiency (SE, EE) with reduced hardware and algorithmic complexity. In the same context, a machine learning (ML) approach is considered as well, where the configuration of the active beams per cell is directly related to the requested throughput in the cell’s angular space. According to the presented results, the ML-assisted beamforming framework can improve EE with reduced algorithmic complexity compared to the non-ML case, depending on the tolerable amount of blocking probability.

Food security is currently considered a huge societal challenge which technology providers, technology adopters, policy makers and consumers altogether are facing. A first step towards more secure and safe food has been attempted through the deployment mainly of Internet of Things based solutions and applications that gather information and provide it to the users. However, these systems are susceptible to attacks e.g. data leakage and information modification, keeping our societies away from the target of secure food. This paper explores the requirements of the farm to fork supply chain with respect to security and proposes a platform that aims at alleviating and mitigating a wide set of attacks.

In this paper the performance of realistic massive multiple input multiple output configurations operating at the millimeter wave frequency band is evaluated. Performance is evaluated statistically by executing a sufficient number of Monte Carlo simulations with the help of a developed semi-static simulator in a fifth-generation multicellular orientation. According to the results, although the increase in the number of radiating elements potentially leads to improved performance metrics (e.g., increased throughput, reduced blocking probability and transmission power), the deployment of an increased number of highly directional beams in predefined angular locations can lead to performance degradation. In particular, for 128 radiating elements and 8 directional beams per base station (BS), total throughput can reach 2320 Mbps with an equivalent transmission power per BS less than 2 W. However, system performance in terms of accepted users and blocking probability can deteriorate significantly when considering an equivalent grid of beams with 16 active sectors.

Power allocation is strongly related to the coverage and capacity of wireless networks, playing a critical role in the development of 5G networks. This paper proposes a Demand-Driven Power Allocation (DDPA) algorithm aiming to fulfill the requested throughput of individual users and accommodate their needs. DDPA is based on model-free Deep Reinforcement Learning (DRL) approaches and has the ability to proactively adjust the power levels of network transmitters. The performance of the developed algorithm is evaluated for a variety of simulation parameters and variable user demands. According to the presented results, the DDPA scheme exhibits a near-optimal performance for up to 50 users in the network area (i.e. satisfaction percentage exceeds 95%), with each one requesting 1 Mbps. Moreover, performance comparison between DDPA and two typical baseline methods reveals that the former results into enhanced total allocated throughput solutions (i.e. a performance increase by a factor of approximately 9% against baseline methods).

The Industry 4.0 sector is evolving in a tremendous pace by introducing a set of industrial automation mechanisms tightly coupled with the exploitation of Internet of Things (IoT), 5G and Artificial Intelligence (AI) technologies. By combining such emerging technologies, interconnected sensors, instruments, and other industrial devices are networked together with industrial applications, formulating the Industrial IoT (IIoT) and aiming to improve the efficiency and reliability of the deployed applications and provide Quality of Service (QoS) guarantees. However, in a 5G era, efficient, reliable and highly performant applications' provision has to be combined with exploitation of capabilities offered by 5G networks. Optimal usage of the available resources has to be realised, while guaranteeing strict QoS requirements such as high data rates, ultra-low latency and jitter. The first step towards this direction is based on the accurate profiling of vertical industries' applications in terms of resources usage, capacity limits and reliability characteristics. To achieve so, in this paper we provide an integrated methodology and approach for benchmarking and profiling 5G vertical industries' applications. This approach covers the realisation of benchmarking experiments and the extraction of insights based on the analysis of the collected data. Such insights are considered the cornerstones for the development of AI models that can lead to optimal infrastructure usage along with assurance of high QoS provision. The detailed approach is applied in a real IIoT use case, leading to profiling of a set of 5G network functions.

In pursuit of a flexible, resource efficient and high- performant 5G infrastructure, many operators, vendors and research consortia are currently developing, testing and inte­grating their NFV platform with associated management and orchestration (MANO) functionality. The SONATA NFV platform follows a micro-service design, which involves a tight coupling between an SDK, monitoring and MANO functionality, targeting a secure and stable software foundation. This experience paper gives a thorough overview on the encountered challenges, insights and resulting learnings when implementing and integrating the SONATA Service Platform using a continuous integration and delivery DevOps methodology. This is the result of a strong cooperation between prominent equipment vendors, network operators, software companies and universities, providing a set of constructive recommendations in hope of catalysing the development and deployment of NFV platforms.

The massive deployment of IoT devices, broadband and mission critical services are paving the way for 5G communication networks, which will enable massive capacity, zero delay, elasticity and optimal deployment, enhanced security, privacy by design and connectivity to billions of devices with less predictable traffic patterns. This paper targets a very important and demanding application the Preventive Maintenance as a Service in Critical Infrastructures and more precisely in the energy (electricity and gas) transmission and distribution network that combines the 5G technology with secure IoT and drones flight control. In more details, it addresses the 5G advances at the edge network and proposes a number of VNFs to support surveillance using swarms of drones.

ABSTRACT As the applications of wireless sensor networks proliferate, the efficiency in supporting large sensor networks and offering security guarantees becomes an important requirement in the design of the relevant networking protocols. Geographical routing has been proven to efficiently cope with large network dimensions while trust management schemes have been shown to assist in defending against routing attacks. Once trust information is available for all network nodes, the routing decisions can take it into account, i.e. routing can be based on both location and trust attributes. In this paper, we investigate different ways to incorporate trust in location‐based routing schemes and we propose a novel way of balancing trust and location information. Computer simulations show that the proposed routing rule exhibits excellent performance in terms of delivery ratio, latency time and path optimality. Copyright © 2010 John Wiley & Sons, Ltd.

Security has been recognised as a key issue for the expansion of wireless sensor network applications. To defend against the wide set of security attacks, legacy security solutions are not applicable due to the very limited memory and processing resources of the sensor nodes as well as due to the reason that sensor networks are required to operate in an autonomous infrastructureless manner. Trust management schemes consist a powerful tool for the detection of unexpected node behaviours (either faulty or malicious). Once misbehaving nodes are detected, their neighbours can use these information to avoid cooperating with them either for data forwarding, data aggregation or any other cooperative function. We propose a novel trust management system based on both direct and indirect trust information, which allows for fast detection of a wide set of attacks, including those addressing the reputation exchange scheme, while energy awareness is also incorporated in our approach.