A smart city will become an inevitable trend of social development. It includes many Cyber Physical Systems (CPSs) that can be endowed with different levels of intelligence. With the combination of CPS and cloud computing, smart sensor cloud system becomes popular in many areas where the physical nodes can be shared with multiple users, which can obtain high performance computing. In view of this idea, this paper studies the specific application of smart city‐online task scheduling. We treat each task as a physical node, and the scheduling of multiple computing tasks is to achieve sustainable services. However, when a physical sensor node receives commands from multiple tasks simultaneously, namely, resource conflict management problem. This conflicting resource management problem leads to the failures of services. To solve the resource conflict problem, we propose an extended Hungarian algorithm based on edge computing, which can obtain smaller delay and achieve sustainable service. Edge computing can be viewed as a buffer and controller between CPS and the cloud and can build a sustainable service system by handling malicious attacks. The experimental results show that our proposed method can effectively reduce resource conflicts, improve resource utilization, and make services more sustainable.

中文翻译：

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智慧城市中使用边缘计算系统的任务调度

智慧城市将成为社会发展的必然趋势。它包括许多网络物理系统（CPS），可以赋予它们不同程度的智能。随着CPS和云计算的结合，智能传感器云系统在许多可以与多个用户共享物理节点并获得高性能计算的领域中变得越来越流行。有鉴于此，本文研究了智能城市在线任务调度的具体应用。我们将每个任务视为一个物理节点，并且调度多个计算任务是为了实现可持续的服务。但是，当物理传感器节点同时从多个任务接收命令时，即出现资源冲突管理问题。此冲突的资源管理问题导致服务失败。为了解决资源冲突问题，我们提出了一种基于边缘计算的扩展匈牙利算法，该算法可以获得较小的时延，可以实现可持续的服务。边缘计算可以看作是CPS与云之间的缓冲区和控制器，并且可以通过处理恶意攻击来构建可持续的服务系统。实验结果表明，该方法可以有效减少资源冲突，提高资源利用率，使服务更具可持续性。