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

XPS:FULL:SDA：反射树 - 未来智能城市的新计算机和通信架构

• 批准号: 1439011
• 负责人: Tao Wei
• 金额: $ 85万
• 依托单位: University of Rhode Island
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1439011&HistoricalAwards=false
• 关键词: XPS; FULL; SDA; Reflex; Tree

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.

该项目研究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，旨在应对未来智能城市带来的挑战。这种新颖的反射树架构的中心特征受到人类神经系统的基本元素的启发-反射弧，或神经肌肉反应和本能运动，以应对不需要大脑直接干预的紧急情况。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。该项目的跨学科和合作性质将激发相关研究领域的更广泛参与。在人体内，神经反射弧能够使个体立即对不适的来源做出反应，而不需要大脑的直接控制。反射树架构模仿此类人类神经电路，使用大量中间计算节点、边缘设备和传感器来收集、处理以及最重要的是对有关关键基础设施元素的数据做出反应。所提出的反射树架构的关键创新包括：1）一种新颖的，4级，大规模，和应用特定的分层计算和通信结构，能够执行基于传感器的决策过程。所需的计算和节点计算能力在层次结构中的每个连续阶段都有所增加，其中1级云执行最复杂的任务。2)将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，是专门针对新的反射树系统architecture.To证明的反射树架构的可行性和性能，概念验证原型将利用小型化，实验室规模的市政燃气管道系统构建。该原型将包含一个完整的4级反射树-一个分布式光纤传感网络，部署在管道旁边，边缘设备使用并行SVM执行数据分类，中间节点执行并行空间和时间机器学习，云作为根节点运行复杂的并行行为分析和决策任务。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了基础的传感和计算机架构。