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.