Time-efficient offloading for machine learning tasks between embedded systems and fog nodes

The Internet of Things (IoT) and Machine Learning (ML) introduce embedded systems to many new roles and functions, but the current status quo of using these technologies together can be improved. The status quo has embedded systems offloading all of their ML functionality to an external device, but this can lead to unpredictable throughput due to network instability. We propose to run low-complexity ML models on the embedded system itself and distribute the workload when it has been measured to bypass a Worst-Case Execution Time (WCET) threshold.