In cyber-physical systems, sensor transactions should be effectively scheduled to maintain the temporal validity of real-time data objects. Previous studies on sensor transaction scheduling mainly focus on uniprocessor systems. In this paper, we study the problem of data quality-based scheduling of sensor transactions on multiprocessor platforms. The data quality is defined to describe the validity degree of real-time data objects. Two methods, named the Partitioned Scheduling for Quality Maximization (P-QM) and the improved P-QM scheduling (IP-QM), are proposed. P-QM maximizes the data quality by judiciously determining the preallocated computation time of each sensor transaction and assigns the transactions to different processors. IP-QM improves the data quality obtained from P-QM by adaptively executing transaction instances on each processor based on the current status of the system. It is demonstrated through experiments that IP-QM can provide higher data quality than P-QM under different system workloads.

中文翻译：

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用于实时数据质量维护的传感器交易的多处理器调度

在网络物理系统中，应该有效安排传感器事务，以维护实时数据对象的时间有效性。以前有关传感器事务调度的研究主要集中在单处理器系统上。在本文中，我们研究了基于数据质量的多处理器平台上的传感器事务调度问题。定义数据质量以描述实时数据对象的有效性程度。提出了两种方法，分别是质量最大化分区调度（P-QM）和改进的P-QM调度（IP-QM）。通过明智地确定每个传感器事务的预分配计算时间，P-QM可以最大程度地提高数据质量，并将事务分配给不同的处理器。IP-QM通过根据系统的当前状态在每个处理器上自适应地执行事务实例来提高从P-QM获得的数据质量。通过实验证明，在不同的系统工作负载下，IP-QM可以提供比P-QM更高的数据质量。