## 3 Methodology
### Digital Twin Architecture Evaluation Using Modified AHP
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level 1 Criterion** & **Level 2 Criterion** & **Local Weighting** & **Global Weighting** \\ \hline \multirow{4}{*}{Value added services} & Fish growth model & 0.16666667 & 0.02207222 \\  & Fish in-stock evaluation & 0.16666667 & 0.02207222 \\ \cline{1-1}  & Weight conversion ratio & 0.16666667 & 0.02207222 \\ \cline{1-1}  & Net hole detection & 0.16666667 & 0.02207222 \\ \cline{1-1}  & Aquatic plants detection & 0.16666667 & 0.02207222 \\ \cline{1-1}  & Water quality alarming & 0.16666667 & 0.02207222 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Level 2 criteria priority evaluation results for Level 1 ’value added services’ criterion using the modified AHP. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level 1 Criterion** & **Level 2 Criterion** & **Local Weighting** & **Global Weighting** \\ \hline \multirow{4}{*}{Real-time monitoring} & Water quality & 0.33333333 & 0.04461534 \\  & Climate data & 0.3333333 & 0.04461534 \\ \cline{1-1}  & Underwater video & 0.3333333 & 0.04461534 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Level 2 priority evaluation results for Level 1 ’real-time monitoring’ criterion using the modified AHP. management, such as the fish feed prediction as an example. This solution can add or integrate multiple functionalities by integrating water quality inspection, the camera system (sonar or RGB camera), and the cloud system. We build a function for the fish feed prediction for each component, and each module has a priority value based on the analysis result. Since there are different solutions for different functions for intelligent fish farm management, their priority value may differ. For example, if all modules were included in the system, the priority should be 1, where the priority is the sum of the underlying modules. Basically, the results of our analysis provide suggestions, solutions, or architecture on what kind of system features should be constructed as part of the physical objects in the cloud to provide function-specific fish farm management. Higher priorities are given higher regard or inclusion into the system. Although we only have a prototype in terms of implementation, we were able to test the transmission speed and several of our sensors in one of our aquaculture farm sites in Penghu, Taiwan. Our transmission speed depends on the services or capacity of our mobile network. Using 4G (5G is prioritized if available), the camera sensors can perform image transmissions with an uplink bandwidth of 8 megabits per second. For complete video surveillance, half an hour of video is required, and it will take around 30 min to transmit the data from the aquaculture site to the cloud. The architecture for the wireless communication network depends solely on the commercially available network in the aquaculture site. In our case, a 4G communication network is utilized, which uses a standard communication network to send data packets, which is Internet Protocol (IP) based. Our system followed the event-driven type of monitoring. Our land-based monitoring system sends a command to activate our sensors (e.g., RGB or sonar camera devices) to perform surveillance by enabling our camera sensor device, primarily if we perform fish feeding, metric estimation, environment monitoring, or if the temperature is beyond the specified threshold to make sure that the fish are still at their best or have good vitality. The event-driven type is adopted due to two reasons. First, aquaculture farms located offshore don't have an unlimited or continuous power supply, and they solely depend on battery packs as their power source. Therefore, performing surveillance when only needed is a form of power-saving mechanism. Second, the transmission system depends most of the time on the 4G network, and it will take time to transmit data, especially video surveillance data, to the cloud.