This paper presents a novel hybrid approach to solving real-world drone routing problems by leveraging the capabilities of quantum computing. The proposed method, coined Quantum for Drone Routing (Q4DR), integrates the two most prominent paradigms in the field: quantum gate-based computing, through the Eclipse Qrisp programming language; and quantum annealers, by means of D-Wave System’s devices. The algorithm is divided into two different phases: an initial clustering phase executed using a Quantum Approximate Optimization Algorithm (QAOA), and a routing phase employing quantum annealers. The efficacy of Q4DR is demonstrated through three use cases of increasing complexity, each incorporating real-world constraints such as asymmetric costs, forbidden paths, and itinerant charging points. This research contributes to the growing body of work in quantum optimization, showcasing the practical applications of quantum computing in logistics and route planning.

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.

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.

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.

5G mobile networks introduce the concept of network slicing, the functionality of creating virtual networks on top of shared physical infrastructure. Such slices can be tailored to various vertical services. A single User Equipment (UE) may be served by multiple network slice instances simultaneously, which opens up the possibility of dynamically steering traffic in response to the specific needs of individual applications – and as a reaction to events inside the network, e.g., network failures.This paper presents the PoLicy-based Architecture for Network Slicing (PLANS). In this policy framework, the network slice management entity in the 5G core and the UE can cooperatively optimize the usage of the available network slices via policy systems installed both inside the network and on the UE. The PLANS architecture has been implemented and evaluated in a 5G testbed. For two different case studies, we show how such a system can be leveraged to provide optimized services and increased robustness against network failures. First, we consider a drone autopilot scenario, and demonstrate how PLANS can reduce network-slice recovery time by more than 90%. Second, we illustrate for a 360°video streaming scenario how PLANS can help prevent video quality degradation when a network slice becomes unavailable.

To improve data speed and reliability, hybrid wireless networks combine two different Radio Access Technologies (RATs), such as Visible Light Communications (VLC), millimetre wave (mmWave), Wireless Fidelity (WiFi), 4G Long Term Evolution (LTE), etc. The Internet of Radio Light (IoRL) is a cutting-edge system paradigm to combine three RATs for taking advantage the vast VLC and mmWave spectrum with the ubiquitous coverage of WiFi. In this respect, this work introduces a new convex optimisation-based solution method to optimise the three-dimensional (3D) Access Point Assignment (APA) problem of the IoRL system under individual user positioning, priority and minimum Quality-of-Service (QoS) constraints. We use both the IoRL real-world testbed and large-scale Maltab simulations to evaluate that our solution converges in linear time, and attains higher throughput-vs-fairness trade-off than existing efforts.

Software Defined Networking (SDN) and Network Function Virtualisation (NFV) are transforming modern networks towards a service-oriented architecture. At the same time, the cybersecurity industry is rapidly adopting Machine Learning (ML) algorithms to improve detection and mitigation of complex attacks. Traditional intrusion detection systems perform signature-based detection, based on well-known malicious traffic patterns that signify potential attacks. The main drawback of this method is that attack patterns need to be known in advance and signatures must be preconfigured. Hence, typical systems fail to detect a zero-day attack or an attack with unknown signature. This work considers the use of machine learning for advanced anomaly detection, and specifically deploys the Apache Spot ML framework on an SDN/NFV-enabled testbed running cybersecurity services as Virtual Network Functions (VNFs). VNFs are used to capture traffic for ingestion by the ML algorithm and apply mitigation measures in case of a detected anomaly. Apache Spot utilises Latent Dirichlet Allocation to identify anomalous traffic patterns in Netflow, DNS and proxy data. The overall performance of Apache Spot is evaluated by deploying Denial of Service (Slowloris, BoNeSi) and a Data Exfiltration attack (iodine).

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.

Visible Light Communication (VLC) is a powerful supplement, which has gained tremendous attention recently and has become a favorable technology in short-range communication scenarios for the Fifth Generation (5G) networks. VLC possesses a number of prominent features to address the highly demanding 5G system requirements for high capacity, high data rate, high spectral efficiency, high energy efficiency, low battery consumption, and low latency. However, this prominent performance is limited by the imperfect reception, since line of sight channel condition may not always exist in practice. This paper presents and experimentally validates a SDN-assisted VLC system, which is coupled with WiFi access technology in order to improve the reliability of VLC system, reassuring zero packet loss reception quality due to misalignment or path obstructions or when the user is moving between two consecutive VLC transmitters and experience “dead coverage zones”.

Fifth-generation (5G) envisages a “hyper-connected society” where an enormous number of diverse entities could communicate with each other anywhere and at any time, some of which will demand extremely challenging performance requirements such as sub-millisecond latency, and higher data rates. Cloud-enabled radio access networks (CE-RANs) where intelligence is placed at the edge of the mobile network and in the proximity of end users emerge as a promising solution to improve online experience. To make CE-RAN more flexible and cost-effective, network functions virtualization and software defined networking technologies are employed, enabling features such as resource pooling, scalability, spectral efficiency, and multi tenancy. The accommodation of such technologies in the context of 5G requires multipronged efforts at various levels, in particular at the management and orchestration. Among all topics that falls into this subject, e.g., resource optimization and efficient lifecycle management, this paper focuses on two critical challenges, i.e., service mapping and quality of service assurance. Particularly, the proposed service placement solution takes into account two main constraints, i.e., quality of user experience and limited hardware capabilities available at the network edge. Simulation tools as well as the SESAME testbed have been used to extract results in this paper and a proof-of-concept evaluation is presented.

One of the primary challenges associated with network functions virtualization (NFV) is the automated management of the service lifecycle. In this paper, we present a full software-based management and orchestration (MANO) stack which operates with OpenStack and OpenDaylight controllers and has the in-built functionality to automate the key phases of the NFV service lifecycle, namely resource discovery and matching, service mapping, service deployment, and monitoring. The MANO stack is being implemented by the EU FP7 project T-NOVA, with the components being released as open-source software. Service mapping and service deployment solutions developed in the scope of T-NOVA are presented in detail. As a proof-of-concept, we evaluate the performance of a virtualized traffic classifier network function, demonstrating the gains of virtualized hardware acceleration.

The EU funded FP7 project T-NOVA, with the specific goal of accelerating the evolution of NFV, proposes an open architecture to provide Virtual Network Functions as a Service (VNFaaS), together with a dynamic, and flexible platform for the management of Network Services (NSs) composed by those Virtual Network Functions (VNFs). The proposed architecture allows operators to deploy distinct virtualized network functions, not only for their internal operational needs, but also to offer them to their customers, as value-added services. Virtual network appliances (e.g. gateways, proxies or even traffic analyzers) can be provided on-demand, eliminating the need to acquire, install, and maintain specialized hardware at customer premises. This demo illustrates early work carried out on the deployment of a VNF on a Network Function Virtualization Infrastructure (NFVI) using resource aware scheduling methods to ensure optimal use of resources and performance.

The process of transcoding videos apart from being computationally intensive, can also be a rather complex procedure. The complexity refers to the choice of appropriate parameters for the transcoding engine, with the aim of decreasing video sizes, transcoding times and network bandwidth without degrading video quality beyond some threshold that event detectors lose their accuracy. This paper explains the need for transcoding, and then studies different video quality metrics. Commonly used algorithms for motion and person detection are briefly described, with emphasis in investigating the optimum transcoding configuration parameters. The analysis of the experimental results reveals that the existing video quality metrics are not suitable for automated systems and that the detection of persons is affected by the reduction of bit rate and resolution, while motion detection is more sensitive to frame rate.

This paper presents a novel open-source implementation of a call center, which is compatible with the IP Multimedia Subsystem (IMS) platform, supporting call forwarding and dispatching services, along with video call and chat with file transfer. The proposed solution is called Call Center for IMS (CC4IMS) and following a requirement analysis, the paper discusses the overall architecture and design of the proposed Call Center, focusing on the IMS compatibility and mobility of the implementation. Then, an open-source prototype implementation is presented, which was designed for mobile devices in order to be suitable for distributed and ad-hoc launch at emergency situations by first responder teams near to the location of the emergency incident. Finally, a protocol and message illustration between the CC4IMS and the IMS modules is presented, based on messages that have been actually captured by the CC4IMS prototype and the Open IMS Core, showing that the proposed Call Center confronts with the IMS signaling.