XPS:FULL:SDA: Reflex Tree - A New Computer and Communication Architecture for Future Smart Cities

该项目研究了一种新的计算和通信架构，反射树，具有大规模并行传感，数据处理和控制功能，旨在应对未来智慧城市带来的挑战。这种新颖的反射树结构的核心特征的灵感来自于人类神经系统的一个基本元素——反射弧，或神经肌肉反应和本能运动，以应对紧急情况，不需要大脑的直接干预。该项目建立的科学基础和工程框架将为加强对关键智慧城市基础设施的监测和管理铺平道路，从天然气/石油管道、水管理、通信网络和电网，到公共交通和医疗保健。该项目的跨学科和协作性质将激发更广泛的参与相关领域的研究。在人体内，神经反射弧能够使个体在不需要大脑直接控制的情况下立即对不适感做出反应。反射树架构模仿这样的人类神经回路，使用大量的中间计算节点、边缘设备和传感器来收集、处理，最重要的是，对有关关键基础设施元素的数据做出反应。提出的反射树架构的关键创新包括：1)一种新颖的、4级的、大规模的、特定于应用的分层计算和通信结构，能够执行基于传感器的决策过程。在层次结构的每个连续阶段，所需的计算和节点计算能力都在增加，第一级云执行最复杂的任务。2)针对智慧城市应用，开发密集分布的光纤传感网络和并行机器学习算法。3)将开发新的、互补的机器智能算法，通过多层自适应学习、时空关联和复杂系统行为分析提供准确的控制决策。4)新的并行算法和软件运行时环境将被提出和开发，专门针对新的反射树系统架构。为了证明反射树架构的可行性和性能，将利用小型实验室规模的市政天然气管道系统构建概念验证原型。该原型将包含一个完整的4级反射树——部署在管道旁的分布式光纤传感网络，使用并行支持向量机执行数据分类的边缘设备，执行大规模并行空间和时间机器学习的中间节点，以及运行复杂并行行为分析和决策任务的云作为根节点。由此产生的系统是一个跨层、高性能和大规模并行计算平台，为未来的智慧城市提供基础的传感和计算机架构。

This project studies a new computing and communication architecture, reflex-tree, with massive parallel sensing, data processing, and control functions designed to meet the challenges imposed by future smart cities. The central feature of this novel reflex-tree architecture is inspired by a fundamental element of the human nervous system -- reflex arcs, or neuro-muscular reactions and instinctive motions in response to urgent situations that do not require the direct intervention of the brain. The scientific foundation and engineering framework built by this project will pave the way for enhanced monitoring and management of critical smart city infrastructure, from gas/oil pipelines, water management, communication networks, and power grids, to public transportation and healthcare. The interdisciplinary and collaborative nature of the project will inspire broader participation in related areas of research.Within the human body, a neural reflex arc is able to cause an individual to immediately react to a source of discomfort without the need for direct control from the brain. The reflex-tree architecture mimics such human neural circuits, using massive numbers of intermediate computing nodes, edge devices, and sensors to gather, process, and, most importantly, to react to data concerning critical infrastructure elements. Key innovations of the proposed reflex-tree architecture include: 1) A novel, 4-level, large scale, and application-specific hierarchical computing and communication structure capable of carrying out sensor-based decision-making processes. The required computation and nodal computing power increases at each successive stage in the hierarchy, with the level-1 cloud performing the most complex tasks. 2) A densely distributed fiber-optic sensing network and parallel machine learning algorithms will be developed targeting smart city applications. 3) Novel, complementary machine intelligence algorithms will be developed, providing accurate control decisions via multi-layer adaptive learning, spatial-temporal association, and complex system behavior analysis. 4) New parallel algorithms and software run-time environments will be proposed and developed that are specifically tailored to the novel reflex-tree system architecture.To demonstrate the feasibility and performance of the reflex-tree architecture, a proof-of-concept prototype will be constructed utilizing a miniaturized, laboratory-scale municipal gas pipeline system. The prototype will incorporate a complete 4-level reflex-tree--a distributed fiber-optic sensing network deployed alongside pipelines, edge devices performing data classification using parallel SVM, intermediate nodes performing massively-parallel spatial and temporal machine learning, and the cloud as the root node running sophisticated parallel behavioral analysis and decision making tasks. The resulting system is a cross layer, high performance, and massively parallel computing platform, providing a foundational sensing and computer architecture for future smart cities.