论文标题
最佳计划,以最大化信息的新鲜度和系统性能在工业网络物理系统中
Optimal Scheduling for Maximizing Information Freshness & System Performance in Industrial Cyber-Physical Systems
论文作者
论文摘要
信息时代是一个新引入的度量标准,以衡量实时网络中信息的新鲜感而生动地关注。此参数已演变为保证从任何实时应用程序中收到的最新状态更新中的及时信息接收。在本文中,我们研究了用于网络物理生产系统的集中式,闭环,网络控制的工业无线传感器 - 实用网络。在这里,我们共同解决了传感器更新的传输调度问题以及在任何实时更新都以硬性降低的实时更新后的信息流线的恢复,从而导致循环中断。与仅确保及时更新的现有实时调度策略不同,这项工作旨在根据信息时代来完成新的和再生实时更新的时间敏感性和数据新鲜度。在这里,网络和物理单位的共存及其个人要求为系统提供服务质量的个人要求似乎是应对的主要挑战之一。在这项工作中,彻底研究了最大程度的关键更新,以最大程度地研究其信息内容及其对其他网络性能的影响。首先使用了一种称为“截止日期意识最高延迟”的贪婪调度策略来解决此问题。其性能最佳性已在分析上证明。最后,通过将我们的算法获得的结果与其他流行的调度策略的结果进行比较,我们的主张得到了验证。
Age of Information is a newly introduced metric, getting vivid attention for measuring the freshness of information in real-time networks. This parameter has evolved to guarantee the reception of timely information from the latest status update, received by a user from any real-time application. In this paper, we study a centralized, closed-loop, networked controlled industrial wireless sensor-actuator network for cyber-physical production systems. Here, we jointly address the problem of transmission scheduling of sensor updates and the restoration of an information flow-line after any real-time update having hard-deadline drops from it, resulting a break in the loop. Unlike existing real-time scheduling policies that only ensure timely updates, this work aims to accomplish both the time-sensitivity and data freshness in new and regenerative real-time updates in terms of the age of information. Here, the coexistence of both cyber and physical units and their individual requirements for providing the quality of service to the system, as a whole, seems to be one of the major challenges to handle. In this work, minimization of staleness of the time-critical updates to extract maximum utilization out of its information content and its effects on other network performances are thoroughly investigated. A greedy scheduling policy called Deadline-aware highest latency first has been used to solve this problem; its performance optimality is proved analytically. Finally, our claim is validated by comparing the results obtained by our algorithm with those of other popular scheduling policies through extensive simulations.