👉 The chosen computing approach for this project is a hybrid model combining edge and cloud computing. Edge computing allows for real-time data processing and decision-making at the device level, reducing latency and bandwidth usage by handling critical tasks locally. This is particularly useful for applications requiring immediate responses, such as autonomous vehicles or industrial automation. Meanwhile, cloud computing provides scalable resources for more complex computations and data storage, enabling efficient handling of large datasets and resource-intensive tasks. By integrating both paradigms, the system achieves optimal performance, balancing low-latency requirements with the need for robust data processing and storage capabilities. This hybrid model ensures that the application can efficiently manage diverse computational demands while maintaining responsiveness and reliability.
