## 3 Results
### Overcoming IoT Device Compatibility Challenges via Azure IoT Hub
Simultaneously, a Windows-based C# application retrieved these messages using the Azure IoT SDK for.NET and processed them in real-time. Real-time is defined in relation to the speed of the internet connection. The internet connection speed for both the sender and receiver devices influences the average transmission delay. One of the authors discussed hardware compatibility challenges in detail in [63], describing the groundwork for understanding the complexities of integrating heterogeneous  
Figure 16: The proposed infrastructure solution for communication between incompatible devices. IoT devices. As a novel contribution, this article proposes the use of Azure IoT Hub, which, in the authors' view, revolutionizes the concept of remote communication in the context of automation systems. Azure IoT Hub enables seamless interoperability between diverse devices. The Azure IoT provides a modality to integrate decision-making and action-taking processes, which is specific to the dynamic requirements of modern automated systems. In IoT ecosystems, the devices require a common communication protocol to interact. For example, the ESP32-CAM programmed in C++ using the Arduino IDE cannot directly communicate with an MSStack device running Bash scripts unless both devices adopt a standardized protocol, such as MQTT. However, establishing such compatibility is challenging when the devices rely on different programming languages and serialization methods. These differences create communication barriers. Remote devices cannot be directly connected through physical interfaces, such as USB or serial connections, and must rely on internet-based protocols to exchange information. In our scenario, the ESP32-CAM detecting defects in a manufacturing facility may need to send alerts to a Windows-based C# application running in a centralized control layer. In this case, the ESP32-CAM sends telemetry data to Azure IoT Hub using MQTT, while the C# application retrieves it via the Azure IoT SDK. Such a framework ensures interoperability when devices are programmed in incompatible languages. The flow from image capture by the ESP32-CAM to defect detection via Azure Custom Vision and subsequent actions facilitated through Azure IoT Hub demonstrates the effectiveness of our solution. Several factors, such as network speed, influence the proposed prototype's overall performance. To address this issue, we perform two types of test. In the first test, the ESP32-CAM (sender) was connected to a network with an upload speed of 10 Mbps, while the Windows-based C# application (receiver) operated on a network with a download speed of 50 Mbps. Under these conditions, the average transmission delay, measured from when the ESP32-CAM sent a defect detection alert to the Azure IoT Hub and the C# application retrieved it, was approximately 150 ms. In the second test, when the ESP32-CAM was connected to a slower network with an upload speed of 6 Mbps, while the receiver's download speed remained constant, the average transmission delay increased to 250 ms. Figure 17 displays the inverse relationship between connection speed and transmission time. This behavior demonstrates that the time required for data transmission decreases as upload speeds increase. This behavior emphasizes the importance of a fast connection in IoT systems for implementing modern cloud infrastructures. Figure 17: Impact of upload speed on transmission time for IoT systems. The average transmission delay is influenced by the internet connection speeds of both the sending and receiving devices, as these directly affect the time taken to transmit and process data through the Azure IoT Hub. The authors assume that the two tests provided are irrelevant to the proposed infrastructure's speed field. However, this prototype serves as a conceptual demonstration, and future work will include testing under real-world conditions to assess the impact of these delays on the overall system performance.