# Edge Computing Architecture for the Management of Underwater Cultural Heritage
## 5 Conclusions and Future Work  
The conservation of UCH is a matter of preserving not only our past but also resources of immense value for understanding our history. With climate change posing one of the most significant threats to UCH, it becomes imperative to harness available data and direct our research towards intelligent solutions. The proposed platform and its implementation provide a robust, real-time approach to monitoring and managing UCH. Integrating IoUT with edge computing technologies allows for the effective processing and monitoring of UCH sites, improving system reliability and responsiveness even in environments with intermittent cloud connectivity. The case study of the buoy system highlights the effectiveness of this approach in achieving precise underwater localisation, overcoming the limitations of traditional GPS-based systems and validating the platform's capability to track underwater assets in real-time. However, while the initial results are promising, further work is needed to refine the platform's capabilities. Future research should focus on extending the scalability of the system, allowing it to manage larger UCH sites with more complex environments and a greater number of deployed assets, while ensuring energy efficiency through the optimisation of power consumption. Enhancing the robustness and accuracy of the localisation algorithms, especially in highly dynamic underwater conditions, is another key area for improvement. Testing the system in more diverse and challenging marine environments will also be critical to ensuring its effectiveness and adaptability across different heritage sites. Additionally, integrating machine learning models to automate data processing and risk assessment at the edge will further advance the platform's ability to proactively manage and conserve UCH. The CNN-VAE model for risk assessment proposed in this work is currently theoretical and requires practical validation. Future efforts will focus on implementing and fine tuning the model using data collected from pilot sites. This will include optimising hyperparameters, evaluating the model's performance in real-world conditions, and exploring alternative generative AI approaches. This practical validation is essential to ensure the applicability and robustness of the proposed AI-driven risk assessment tool. Future work is targeted towards the practical application of this platform at the pilot sites of the TECTONIC project: the Bengala shipw next Isola di Capo Rizzuto in Italy, the ancient harbour of Aegina in Greece, and the shipw site of the HMS Swift in Argentina. Conceptualisation: J.H.-S., M.P.-H., S.L.-F., J.P.T., V.D. and E.S.C. [PERSON]; methodology: [PERSON], [PERSON] and [PERSON]; software: [PERSON]; validation: [PERSON], [PERSON], [PERSON], [PERSON], V.D. and [PERSON]; formal analysis: [PERSON], [PERSON] and [PERSON]; investigation: [PERSON], [PERSON] and [PERSON]; data curation: [PERSON]; writing--original draft: [PERSON], [PERSON] and [PERSON]; writing--review and editing: [PERSON], V.D. and [PERSON]; visualisation: [PERSON]; project administration: [PERSON]. All authors have read and agreed to the published version of this manuscript. This research has was supported by the project \"Technological Consortium TO develop sustainability of underwater Cultural heritage (TECTONIC)\", financed by the European Union (Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement 873132). The original data presented in the study are openly available in GitHub at [[https://github.com/jorgehsmp/TECTONIC-Underwater-Location-System/](https://github.com/jorgehsmp/TECTONIC-Underwater-Location-System/)]([https://github.com/jorgehsmp/TECTONIC-Underwater-Location-System/](https://github.com/jorgehsmp/TECTONIC-Underwater-Location-System/)). Author [PERSON] was employed by the company H2O Robotics Ltd.