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.

The necessity of processing real-time data at the network edge is growing. Low-power AI accelerators, especially edge GPUs, help meet this demand by mitigating cloud-related latency and bandwidth issues. However, GPUs remain underutilised, even in heavy workloads, due to a limited understanding of resource sharing in edge computing. This work analyses key GPU metrics: utilisation, memory, streaming multiprocessors (SMs), and tensorcores on NVIDIA Jetson devices under concurrent vision-inference workloads. Our findings show that while GPU utilisation can reach 100 % with optimisations, SMs and tensor cores often run at only 15-30 % capacity.

In the era of 5G and the upcoming 6G, current software stacks are ineffective when it comes to intelligent and fast decision-making, which necessitates the need for sustainable yet performant and scalable solutions. A significant amount of research has been done in this area, but an end-to-end solution was lacking. In this scope, we identify that we need a novel software stack which can handle the growing need for a vast amount of data generation with the help of the cloud-edge continuum, swarm programmability, along with secure deployments of applications. In this context, we propose OASEES, an architectural framework for a decentralised AI/ML computing stack that unifies diverse computing resources, peer-to-peer coordination protocols, and secure middleware. Our design outlines modular different compute infrastructures(CPUs, GPUs, TPUs and custom ASICs) orchestrated by a lightweight containerbased runtime and governed by a blockchain-enabled tamperproof data storage, decentralised coordination (decentralised autonomous organisation, voting, etc) to facilitate transparent discovery, allocation, and incentivization. We also propose a detailed performance and scalability metrics framework covering latency, throughput, resource utilisation, and cost-per-inference, intended as the foundation of subsequent evaluations.

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.