Publications
Here you can find the list of all the publications of OASEES!
| Num | Title | How OASEES contributed | DOI |
|---|---|---|---|
| J1 | Energy Efficiency in Agriculture through Tokenization of 5G and Edge Applications | The authors tokenized sensor data flows and actuation commands via OASEES’s ERC‑based smart‑contract layer, using the D3.2 implementation to automate incentive payouts for energy‑saving behaviours on distributed farm IoT nodes | |
| J2 | Neuromorphic computing based on halide perovskites | This experimental perovskite-based neuromorphic accelerator was designed and evaluated under OASEES requirements and its SNN specifications. | |
| J3 | Towards Continuous Development for Quantum Programming in Decentralized IoT environments | Building on the OASEES SDK’s CI/CD pipeline for edge‑deployed quantum workloads, the authors demonstrate live firmware and model updates across a device swarm | |
| J4 | INCHAIN: a cyber insurance architecture with smart contracts and self-sovereign identity on top of blockchain | INCHAIN’s insurance workflows mapped directly onto OASEES’s SSI‑and‑smart‑contract framework, using the portable ID federation and ERC‑tokenization enablers designed in D2.1 and D3.2 to underwrite risk automatically | |
| J5 | Enhancing 5G performance: A standalone system platform with customizable features | 5G OASEES testbed evaluation of metrics using network slicing for the provision of different types of services | |
| J6 | Edge Computing Cybersecurity standards: protecting infrastructure and applications | Specification aspects of Privacy and security standardization aspects for MEC. | |
| J7 | CRASHED: Cyber risk assessment for smart home electronic devices | ||
| J8 | Leveraging the DAO for Edge-to-Cloud Data Sharing and Availability | Analysis of the DAO solution of OASEES | |
| J9 | Leveraging 5G RedCap and Spiking Neural Networks for Energy Efficiency in Edge Devices | 5G RedCap measurements in the frame of UC3 | |
| J10 - To be published | 10. Advancing Seismic Risk Prediction with Quantum-Inspired and Hybrid Classical–Quantum Deep Learning | Quantum inspired deep learning for simulation of Seismic prediction | |
| C1 | Applying Hybrid Quantum Lstm For Indoor Localization Based On Rssi | The authors built their hybrid quantum–classical LSTM model atop the OASEES programmable‑swarm framework, to execute qubit‑offloaded routines at the network edge, while OASEES’s decentralized orchestration ensured secure data federation for RSSI measurements across devices. | |
| C2 | 5G High Mast Inspection based on a Decentralized Autonomous Organization in the Framework of the OASEES Project | This work used OASEES’s DAO and smart‑contract modules to coordinate autonomous inspection drones over a private 5G link, with on‑chain proposals and voting determining maintenance process coordination | |
| C3 | Emergency communications leveraging decentralized swarm computing | Researchers demonstrated that OASEES’s private 5G connectivity continuum allow ad hoc device swarms to reconfigure and relay emergency messages without central points of failure, building on the OASEES D4.2 connectivity framework for heterogeneous IoT protocols | |
| C4 | Exploring Federated Learning for Speech-based Parkinson’s Disease Detection | Federated learning routines and privacy‑preserving voice analytics were orchestrated by the OASEES DAO and SSI layers, as defined in the project’s overall architecture | |
| C5 | Integration of Drone Connectivity in 5G: An Examination of the OASEES Framework | In their UAV‑as‑base‑station experiments, the authors relied on OASEES’s swarm architecture and its lightweight 5G system integration to deploy airborne nodes that seamlessly join and leave the edge continuum—features prototyped in the OASEES connectivity deliverable. | |
| C6 | OASEES: An Innovative Scope for a DAO-Based Programmable Swarm Solution, for Decentralizing AI Applications Close to Data Generation Locations | This foundational chapter is the formal exposition of the OASEES framework itself, describing how the project unifies DAO governance, cloud‑edge continuum, and programmable swarms to decentralize AI workloads. | |
| C7 | Quantum Backtracking in Qrisp Applied to Sudoku Problems | The Qrisp backtracking algorithms were deployed using OASEES’s quantum accelerator interface, with the SDK’s job‐submission API handling containerized quantum workloads on edge‐attached quantum simulators—an integration point defined by the OASEES quantum‑accelerator roadmap | |
| C8 | Eclipse Qrisp QAOA: description and preliminary comparison with Qiskit counterparts | This comparison leveraged OASEES’s plug‑in for the Eclipse Qrisp QAOA module developed under the project, running experiments with Qiskit | |
| C10 | Conceptualising a Benchmarking Platform for Embedded Devices | The authors benchmarked edge‑device performance using the OASEES orchestration APIs and telemetry collectors from the oasees-sdk | |
| C11 | Utilizing Distributed Machine Learning Environments for Earthquake Detection | Application to UC4 | |
| C12 | UAV Swarm Management Platform for Autonomous Area and Infrastructure Inspection | Swarm aspects of UAV deployments over 5G enabled testbeds | |
| C13 | Leveraging Self-sovereign Identity for e-Health Applications | Leverages OASEES SSI approach for the e-Health app of the project. | |
| C14 | A Versatile 5G Standalone Testbed Based On Commodity Hardware | Application and testbed requirements for UC3 | |
| C15 | Quantum Neural Networks: A Path to Lower Emissions Through Fuel Consumption Prediction in Shipping | Using Quantum neural networks developed under OASEES for the shipping sector | |
| C16 | Profiling Concurrent Vision Inference Workloads on NVIDIA Jetson | OASEES platform edge scalability | |
| C17 | Solving the Product Breakdown Structure Problem with constrained QAOA | Quantum software benchmarking | |
| C18 | A distributed UAV analytics framework for DAO-based swarm systems | UC3 pilot integration measurements regarding high mast rust detection | |
| C19 | Federated Learning at the Edge for Wind Turbine Predictive Maintenance | UC6 measurements regarding federated learning | |
| C20 | Designing Swarm-based Decentralised Systems: Requirements for Performance and Scalability | OASEES stack evaluation in relation to edge | |
| C21 | OASEES: Leveraging DAO-Based Programmable Swarms for Optimized Edge-to-Cloud Data Processing | It delves into the core features of the OASEES approach, taking into account technological challenges anticipated in system development | |
| C22 | Solving drone routing problems with quantum computing: A hybrid approach combining quantum annealing and gate-based paradigms | Leveraging quantum computation for routing problems, using OASEES drone data |