The demand for real-time data processing has become increasingly attractive in Cyber-Physical Systems(CPSs), especially for data-intensive embedded real-time applications. In order to timely perceive and respond to environmental changes, the basic design requirement in such systems is to provide data service with high freshness. As modern CPSs become more complex, there are a broad set of system mode switch behaviors, some unforeseen, in a dynamic computational environment. However, conventional control algorithms can hardly handle such new scenarios, since most of them assume that the operational behavior is fixed. In this paper, we study the problem of how to maximize the freshness of data in multi-modal systems. We first use a recently proposed new conception, namely Age of Information (AoI) to quantify the freshness of data by combining the AoI metric with real-time constraints. Then, we propose, to our knowledge, the first freshness-aware scheduling solution to settle the problem via deep reinforcement learning(RL). To be specific, we develop an RL framework that can continuously update its scheduling strategies and maximize the freshness of data in the long term. Extensive simulation experiments are conducted and the results demonstrate that the proposed FAS-DQN outperforms other traditional state-of-the-art methods in terms of data freshness.

Response time analysis (RTA) is a kind of effective methods to test the schedulability of real-time tasks under the global earliest deadline first (G-EDF) scheduling. The main idea of the RTA methods is to judge the schedulability of the task set by comparing the worst-case response time (WCRT) and deadline of each task. For obtaining a safe WCRT, existing RTA methods assume that the response times of carry-in jobs are equal to the WCRTs of the tasks generating these jobs. This assumption causes the low accuracy of existing RTA methods, since the busy period lengths of carry-in jobs are usually limited and the response times of these jobs thus cannot always reach the assumed WCRTs. This article proposes a new RTA method that has a higher accuracy than existing RTAs. The main idea of the new method is to limit the busy period lengths of carry-in jobs in the WCRT calculation. We also propose an efficiency improvement scheme for the new method. A set of experiments is conducted to evaluate the performances of the new method and the efficiency improvement scheme. The experimental results show that the new method has a higher acceptance ratio than the state-of-the-art RTA methods and the efficiency improvement scheme can significantly reduce the execution time of the new method.

Scheduling real-time update transactions to maintain data freshness is an important issue in real-time database system design, especially for data-intensive embedded applications. Despite years of active study, almost all the past work assumes that the jitter of sensor update transactions can be ignored. But actually, this assumption is not practical in real Cyber-Physical Systems (CPSs), and the existing methods cannot be applied in the design of distributed CPSs. In this paper, we propose, to our best knowledge, the first approach to maintain temporal consistency of real-time data objects for dynamic-priority scheduling when transmission delays are considered. We first propose a method called JB-EDF which calculates periods and deadlines for update transactions with significantly lower workload than that by existing approaches. Next, in order to handle more update transactions, we further propose an enhanced solution called JB-EDF* which use JB-EDF as a building block. JB-EDF is more suitable for small scale applications, while JB-EDF* is more attractive for large scale applications. Finally, extensive simulation experiments are conducted. The results demonstrate that JB-EDF and JB-EDF outperform existing solutions in terms of both acceptance ratio and system workload, and the proposed methods are also time-efficient.