## 3 The Proposed AIoT Smart Sensing Solution  
An AIoT smart sensing solution has been proposed and developed to detect the external interference threat and alert the operator to avoid potential accidents. The proposed solution aims to monitor the remote site using a camera (see Table 2 for the NANO-H136 camera's specifications by Leopard Imaging Fremont, CA, USA), detect the external interference threat using the AI model (i.e., YOLOv4, DINO) on the edge computing device, and transmit the alert information (i.e., type of threat, time) using the LoRaWAN  
Figure 1: Distribution of Pipe-VisTA across different categories.  
Figure 2: A few annotated samples from the Pipe-VisTA dataset.  
gateway [25] to a dashboard accessible by the operator. Figure 3 shows the visualization of the proposed solution. Given the utility of the solution at remote sites, the edge computing paradigm is followed in which images/data are processed on the device and only the relevant results are transmitted. The low network bandwidth requirement is one of the main advantages of the edge computing approach, which makes it suitable for Low-Power Wide-Area (LPWAN) networks such as LoRaWAN (i.e., a protocol offering long-range communication at a low bit rate and low power consumption).  
The AloT smart sensing device and solution consists of the following core components (as identified in Figure 3):  
* A standard web camera to capture the remote site images to be processed by the AI model for potential threat detection.
* An NVIDIA Jetson Nano 4G edge computer (i.e., ARM-based embedded device to accelerate the AI computations) equipped with the trained AI model (e.g., YOLOv4, DINO) to detect external interference threats.
* A radio module to handle the wireless LoRaWAN communications and transmit the results of the AI model.
* A solar-based battery system to power the AloT sensing device in remote areas.
* A Fleet Portal to transmit the alert via Fleet's satellite network, which enables connectivity between the cloud and terrestrial network elements and enables coverage in areas with no other connectivity options.
* A dashboard to host the transmitted alert(s) accessible by the operators and managers to be informed in a timely manner about any potential threat. The alters reach Nebula from the Fleet network, the control platform where the data are aggregated and enabling all network management operations to be performed.  
As the operational flow, the AloT device iterates through the following steps every fifteen minutes:  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Focal Length** & 1.58 mm & Pixel Size & 1.12 \(\upmu\)m \(\times\) 1.12 \(\upmu\)m \\ \hline FOV & 136 Degree & Operating Temperature & \(-\)20 \({}^{\circ}\)C\(\leftrightarrow\)60 \({}^{\circ}\)C \\ \hline Active Pixels & 3280 (H) \(\times\) 2464 (V) & Operating Voltage & 3 V \\ \hline Dimensions (LWH) & 150 mm \(\times\) 25 mm \(\times\) 15.3 mm & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the Leopard Imaging NANO-H136 camera.  
Figure 3: Visual illustration of the proposed AloT solution for external interference threat detections.  
Turn the power on, and wake up the device.
2. Acquire the image from the camera
3. Process the image using the trained AI model on the edge computer to identify the potential external interference threat.
4. If a threat is detected, transmit the alert (i.e., type of threat, timestamp, device_id) to the Fleet Portal using the LoRaWAN protocol.
5. Transit to hibernation mode to save power.