## 3 Methodology
### Architecting Digital Twins Using Literature Reviews
The resulting importance and easiness indexes for each component are then used to compute the corresponding importance and easiness of individual criteria of the analytic hierarchy that specifies the key factors to construct an AIoT information system for intelligent fish farming. \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Layer 1: Physical Object** & \(\mathbf{I}\) & \(\mathbf{E}\) \\ \hline \multirow{4}{*}{Data collection (D)} & D1. water quality & 0.8 & 0.8 \\  & D2. optical RGB camera & 0.8 & 0.8 \\  & D3. sonar camera & 0.8 & 0.6 \\  & D4. acoustic sensor & 0.6 & 0.8 \\  & D5. climate open data & 0.5 & 0.8 \\ \hline \multirow{4}{*}{Machinery (M)} & M1. fish feeding machine & 0.8 & 0.2 \\  & M2. net cleaner & 0.6 & 0.2 \\  & M3. sorting machine & 0.5 & 0.2 \\  & M4. heater/colder & 0.8 & 0.5 \\  & M5. oxygen pump & 0.8 & 0.5 \\  & M6. drone & 0.5 & 0.2 \\  & M7. underwater drone & 0.4 & 0.2 \\ \hline \multirow{4}{*}{Environment (E)} & E1. indoor fish pond & 0.8 & 0.8 \\  & E2. outdoor fish pond & 0.8 & 0.7 \\  & E3. offshore cage & 0.8 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Layer 1 physical objects used in fish farm management and their feasibility evaluation in terms of indexes \(I\) and \(E\). \begin{table}
\begin{tabular}{l l l c c} \hline \hline
**Layers 2 and 3: End-System Digital Twin** &
\begin{tabular}{c} **Network** \\ **Communications** \\ \end{tabular} & \(\mathbf{I}\) & \(\mathbf{E}\) \\ \hline \multirow{4}{*}{Data networking (N)} & N1. water quality & LoRa, NB-IoT & 0.8 & 0.8 \\  & N2. optical RGB camera & 4G/5G/WiFi & 0.8 & 0.7 \\  & N3. sonar camera & 4G/5G/WiFi & 0.8 & 0.7 \\  & N4. acoustic sensor & 4G/5G/WiFi & 0.8 & 0.7 \\  & N5. climate open data & 4G/5G/WiFi & 0.8 & 0.8 \\ \hline \multirow{4}{*}{Action Execution (A)} & A1. fish feeding & LoRa, NB-IoT & 0.8 & 0.8 \\  & A2. food usage & LoRa, NB-IoT & 0.8 & 0.8 \\  & A3. net cleaning & LoRa, NB-IoT & 0.8 & 0.8 \\  & A4. fish sorting & LoRa, NB-IoT & 0.8 & 0.8 \\  & A5. heater & LoRa, NB-IoT & 0.8 & 0.8 \\  & A6. air pumper & LoRa, NB-IoT & 0.8 & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Layers 2 and 3 of the Digital Twins send sensor data to the cloud storage or execute actions defined by decision-making models. Next, the virtualization technique is applied to implement physical objects as end-system digital twins, which take the responsibility of sending sensor data to the cloud storage or executing actions defined by decision-making models. The cloud plays a vital role in managing the collected big datasets and performing embedded AI functions to determine the feedback actions for operating aquaculture tasks, e.g., fish feeding processes. Table 2 shows the importance and easiness evaluation results for the digital twins (logic objects) in Layers 2 and 3. The fourth layer of our AIot model is the basic services that use pre-trained machine learning or deep learning models to analyze the sensor data for predicting environmental conditions, fish metrics estimation, and fish behavior recognition. Again, the cloud architecture facilitates the implementation of plug-in services because all the AI functions are maintained at the server site. Furthermore, such a mechanism increases the possibility of offering on-the-fly services when the farm manager sends a new requirement. Table 3 shows the digital twins for basic services and their AI functions. Finally, the last layer of the fish farm data applications is the user interface. The fish farmer can use the user interface to access the decision-making results to efficiently and effectively manage the fish farm. Individual decision-making models access different results of basic services to optimize the action sequence for a specific aquaculture management task. Figure 3 shows the proposed reinforcement learning-based digital twin for controlling the smart feeding machine to minimize fish food usage. With the water veloc