# Edge Computing Architecture for the Management of Underwater Cultural Heritage
## 3 Conceptual Framework
### Edge Layer  
The edge layer consists of edge nodes, which have the functions of processing and storage of the data that have been collected from the sensor layer. These nodes are installed on ocean buoys and conveniently situated locations near UCH sites. Such nodes have processing power for filtering, preprocessing, and analysing data, which reduce transmission delay and network bandwidth utilisation. The central unit (or de edge node) is a buoy that communicates via LoRa with the surface elements deployed in the area, such as node buoys, the catamaran,and the ASV. It incorporates equipment for data processing at the \"edge\" before being retransmitted to a cloud service. By processing information close to the source, the edge layer significantly reduces the need for high-bandwidth data transmission and improves the overall responsiveness of the system. Additionally, these nodes can cache critical data until cloud connectivity is restored, ensuring continuous monitoring even in remote areas. The processing of the received data allows the obtaining of the absolute position of the deployed underwater elements through triangulation algorithms, data preprocessing and filtering, anomaly identification, surface drone guidance, and so many more. Combined with autonomous power solutions such as solar panels and batteries, these units ensure continuous operation in remote areas. The processing units of the edge nodes are based on mini-computers such as Raspberry Pi 4 (Raspberry Pi Ltd., Cambridge, England) or NVIDIA Jetson Nano (Nvidia Corporation, Santa Clara, CA, USA), which were chosen for their balance between computational power and low energy consumption [50], meeting the demands of the proposed architecture. Their modular design supports scalability, allowing for the integration of additional nodes as the system expands to larger or more complex UCH sites. Risk Assessment Tool for Climate-Related Stressors  
The edge layer features a risk assessment tool that is aimed at determining the consequences of climate stressors on UCH sites. This is achieved through AI models deployed at the edge to analyse the real-time data obtained from the sensor layer. By examining key environmental parameters such as water temperature, salinity, pressure, and pH levels, the system can assess the risks posed by factors like rising sea temperatures, sediment disruption, and ocean acidification. The risk assessment tool employs a generative AI model that runs on the edge nodes to provide on-site evaluation without relying on cloud connectivity. The initial focus is on elements with materials particularly vulnerable to corrosion, such as steel, which is significantly affected by climate change factors. The model processes key environmental parameters, including water temperature, pressure, salinity, and pH levels, as these directly influence the corrosion rate, an essential metric for assessing the condition of submerged heritage assets [51; 52]. Additionally, databases with information on the thermohaline circulation and the sea level are combined (Table 1). The National Oceanic and Atmospheric Administration (NOAA) buoys are valuable for sourcing reliable and high-granularity hydroclimatic data. These buoys provide a stream of critical variables, such as water temperature, pressure, dissolved oxygen, CO\({}_{2}\), salinity, and pH. To build the degradation scale, images capturing the state of UCH are gathered. These images are subsequently annotated by a panel of experts in underwater archaeology or heritage conservation, who classify the state of degradation on a scale from 1 to 10. The combination of these climate variables and expert-annotated images aims to produce a robust, multidimensional model capable of assessing the current state of submerged heritage and offering predictive insights that could guide future conservation efforts. Convolutional variational autoencoders (CNN VAEs) stand out as effective analytical tools for this problem.