# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 2 Related Work
### Learning Based Adaptive Scheduling  
Learning-based scheduling approaches have gained attention for their ability to support runtime adaptation in dynamic and heterogeneous environments. These techniques offer scalability and responsiveness by learning scheduling policies from observed behavior rather than relying on exhaustive design-time computation. However, despite their promising results in terms of performance and adaptability, they generally lack deterministic timing guarantees and formal verifiability, making them unsuitable for safety-critical systems.  
A Deep Reinforcement Learning (DRL)-based framework for wireless time-sensitive networking (WTSN) was presented in [20]. The method utilizes proximal policy optimization (PPO) to dynamically assign transmission slots, adapting to changing wireless conditions. Although the framework achieves high latency satisfaction, it does not provide worst-case latency bounds or deterministic behavior, limiting its applicability to certified real-time systems.  
In fog-based IoT environments, [PERSON] et al. [21] proposed an RL-based task scheduler using a two-level neural architecture. Tasks are first classified as either fog or cloud using feed-forward and convolutional neural networks, followed by an RL-based allocation across fog devices. While this approach improves response time and reduces communication overhead, it lacks system-wide consistency and temporal guarantees required for safety-critical deployments.  
[PERSON] et al. [22] introduced HeterPS, a distributed RL-based framework for scheduling deep neural network (DNN) training tasks across heterogeneous platforms. The scheduler aims to minimize cost and training time under elastic resource constraints. However, the absence of formal scheduling guarantees and predictable fault handling restricts its use in systems that require verifiable runtime behavior.  
Beyond RL, [PERSON] et al. [23] proposed a GNN-based meta-scheduling framework for adaptive time-triggered systems. Their model is trained to approximate the scheduling behavior of a Genetic Algorithm (GA), enabling schedule reconstruction at runtime.  
Although this approach offers improved adaptation efficiency compared to traditional heuristics, it does not ensure bounded response times or deterministic schedule switching, factors that are essential in safety-critical applications.  
While these learning-based approaches are effective in handling dynamic environments, their model-free or inference-driven nature introduces unpredictability and limits traceability, both of which are unacceptable in safety-certified systems. In contrast, our proposed framework generates a multi-schedule graph (MSG) offline, where each node corresponds to a schedule adapted to a specific context event. Transitions between schedules are triggered deterministically based on explicitly defined context changes, and context exchange is co-scheduled with application tasks to ensure global consistency and bounded overhead. This design guarantees formally verifiable behavior, timing predictability, and system-wide determinism, addressing the critical limitations of learning-based methods in safety-critical domains.