The integration of serverless edge computing and the Industrial Internet of Things (IIoT) has the potential to optimize industrial production. However, cold start latency is one of the main challenges in this area, resulting in resource waste. To address this issue, we propose a new machine learning-based resource management framework called MASTER which utilizes an extreme gradient boosting (XGBoost) model to predict the cold start latency for Industry 4.0 applications for performance optimization. Furthermore, we created a new cold start dataset using an IIoT scenario (i.e. predictive maintenance) to validate the proposed MASTER framework in serverless edge computing environments. We have evaluated the performance of the MASTER framework using a real-world serverless platform, Google Cloud Platform for single-step prediction (SSP) and multiple-step prediction (MSP) operations and compared it with existing frameworks that used deep deterministic policy gradient (DDPG) and long short-term memory (LSTM) models. The experimental results show that the XGBoost-based resource management framework is the most successful model in predicting cold start with mean absolute percentage error (MAPE) values of 0.23 in SSP and 0.12 in MSP. It has been also identified that the Linear Regression model (utilized in the MASTER framework) has the least computational time (0.03 seconds) as compared to other deep learning and machine learning models considered in this work. Finally, we compare the energy consumption and $\text{CO}_{2}$ emissions of all models to emphasize resource awareness.

Heavy-vehicle Anti-lock Braking System (ABS) controllers are commonly tested using simulations before vehicle testing, but pneumatic actuation and measurement limitations can affect the results in the field. This paper presents the complete design process of a pneumatic ABS controller. System identification tests, estimation experiments, and reverse engineering methods were used to develop the models used for testing, including a vehicle model, pneumatic circuit model, etc. Model-in-the-loop (MIL) and software-in-the-loop (SIL) simulations were used to test and validate the algorithm in the simulation environment. Hardware-in-the-loop (HIL) simulations were also used to test and tune the ABS algorithm using a physical pneumatic circuit before real vehicle testing. Lastly, the ABS controller was deployed to a custom ECU and tested on the real vehicle on dry and wet asphalt. The developed controller prevented sustained wheel lock and produced repeatable deceleration and stopping-distance results during dry- and wet-asphalt testing. Qualitative assessment by the test driver and engineering team also indicated ABS intervention and modulation characteristics broadly consistent with commercial alternatives.