School of Electrical and Computer Engineering, National Technical University of Athens-NTUA, GR 157 80 Zografou, Greece.
Sensors (Basel). 2019 Feb 3;19(3):639. doi: 10.3390/s19030639.
A Cyber-Physical Social System (CPSS) tightly integrates computer systems with the physical world and human activities. In this article, a three-level CPSS for early fire detection is presented to assist public authorities to promptly identify and act on emergency situations. At the bottom level, the system's architecture involves IoT nodes enabled with sensing and forest monitoring capabilities. Additionally, in this level, the crowd sensing paradigm is exploited to aggregate environmental information collected by end user devices present in the area of interest. Since the IoT nodes suffer from limited computational energy resources, an Edge Computing Infrastructure, at the middle level, facilitates the offloaded data processing regarding possible fire incidents. At the top level, a decision-making service deployed on Cloud nodes integrates data from various sources, including users' information on social media, and evaluates the situation criticality. In our work, a dynamic resource scaling mechanism for the Edge Computing Infrastructure is designed to address the demanding Quality of Service (QoS) requirements of this IoT-enabled time and mission critical application. The experimental results indicate that the vertical and horizontal scaling on the Edge Computing layer is beneficial for both the performance and the energy consumption of the IoT nodes.
一个信息物理社会系统（CPSS）将计算机系统与物理世界和人类活动紧密地结合在一起。本文提出了一个用于早期火灾检测的三级 CPSS，以协助公共当局及时识别和应对紧急情况。在底层，系统架构涉及到具有传感和森林监测功能的物联网节点。此外，在这个层次上，利用大众感知范例来聚合在感兴趣区域内的终端用户设备收集的环境信息。由于物联网节点的计算能源资源有限，中间层的边缘计算基础设施便于处理关于可能发生的火灾事件的已卸载数据。在顶层，部署在云节点上的决策服务集成了来自各种来源的数据，包括用户在社交媒体上的信息，并评估情况的危急程度。在我们的工作中，为边缘计算基础设施设计了一种动态资源缩放机制，以满足这个物联网支持的时间关键和任务关键应用的苛刻服务质量（QoS）要求。实验结果表明，边缘计算层的垂直和水平缩放对物联网节点的性能和能耗都有好处。
