This paper addresses the optimization of embedded platforms to meet the computing and real-time requirements of cyber-physical systems and IoT applications, including embedded intelligence. In this context, schedulers are vital in enhancing processor utilization in industrial contexts. Although existing research has focused primarily on the schedulability of periodic tasks, event-driven tasks better represent these new embedded intelligence scenarios in the real world. This work explores static and dynamic scheduling policies within a general scenario and a specific case study based on an actual industrial application. The proposed dynamic scheduler has been integrated into the FreeRTOS kernel and has been employed to conduct all of our experiments on industrial products within the smart home domain. Our results show that, while we can respect real-time requirements, our proposed dynamic scheduling can improve the performance of event-driven applications by reducing missed task deadlines by up to 60 %. Moreover, we have also developed a lightweight version of our dynamic scheduler for industrial products that reduces average timing overhead for task selection and insertion by up to 34.7% and memory overhead for task creation and list scheduling by up to 74.7 % compared to state- of-the-art static alternatives.