A collaborative and distributed task management system for real-time systems

This paper discusses the benefits of a distributed and collaborative approach for optimizing real-time intelligent systems with complex task scheduling requirements. We focus on the specific example of implementing car platoons in urban traffic, which requires efficient task mapping and scheduling to maximize efficiency and ensure optimal performance. To meet the demands of a car platoon environment, a collaborative task management system, EDFHC-ML, is proposed for connected autonomous vehicles using edge, fog, and cloud computing. We also evaluated our approach with three others and found that our method had the best performance in executing tasks within the deadline. Our proposed approach is beneficial for developing intelligent systems that require high-performance computing and real-time response.