Reflex-tree: A biologically inspired parallel architecture for future smart cities
Document Type
Conference Proceeding
Date of Original Version
12-8-2015
Abstract
We introduce a new parallel computing and communication architecture, Reflex-Tree, with massive sensing, data processing, and control functions suitable for future smart cities. The central feature of the proposed Reflex-Tree architecture is inspired by a fundamental element of the human nervous system: reflex arcs, the neuromuscular reactions and instinctive motions of a part of the body in response to urgent situations. At the bottom level of the Reflex-Tree (layer 4), novel sensing devices are proposed that are controlled by low power processing elements. These 'leaf' nodes are then connected to new classification engines based on machine learning techniques, including support vector machines (SVM), to form the third layer. The next layer up consists of servers that provide accurate control decisions via multi-layer adaptive learning and spatial-temporal association, before they are connected to the top level cloud where complex system behavior analysis is performed. Our multi-layered architecture mimics human neural circuits to achieve the high levels of parallelization and scalability required for efficient city-wide monitoring and feedback. To demonstrate the utility of our architecture, we present the design, implementation, and experimental evaluation of a prototype Reflex-Tree. City power supply network and gas pipeline management scenarios are used to drive our prototype as case studies. We show the effectiveness for several levels of the architecture and discuss the feasibility of implementation.
Publication Title, e.g., Journal
Proceedings of the International Conference on Parallel Processing
Volume
2015-December
Citation/Publisher Attribution
Kane, Jason, Bo Tang, Zhen Chen, Jun Yan, Tao Wei, Haibo He, and Qing Yang. "Reflex-tree: A biologically inspired parallel architecture for future smart cities." Proceedings of the International Conference on Parallel Processing 2015-December, (2015): 360-369. doi: 10.1109/ICPP.2015.45.