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

University Of Rhode Island, Kingston RI

Investigators

Abstract

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.