CSR: Small: Collaborative Research: EDS: Systems and Algorithmic Support for Managing Complexity in Sensorized Distributed Systems

CSR：小型：协作研究：EDS：管理传感器化分布式系统复杂性的系统和算法支持

• 批准号: 1526237
• 负责人: Yuvraj Agarwal
• 金额: $ 30万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526237&HistoricalAwards=false
• 关键词: CSR; Small; Collaborative; Research; EDS

Commercial buildings, the energy grid and transportation systems are examples of emerging distributed systems that are beginning to be instrumented with a large number of sensors and actuators for sensing ambient environmental conditions, user occupancy, state of energy use etc. The goal of such instrumentation is to improve safety, utility and reduce costs. This is a hard problem due to interaction of humans, devices and networks in an operating environment with uncertainties regarding veracity, timeliness, meaning and value of sensor data. A large number of sensors must be provisioned, monitored and maintained by system operators. This is currently a manual and error prone task. Deploying, managing and adapting a sensorized system at scale become nearly impossible. In the micro-grid testbed of networked buildings used by this project, there are over a hundred thousand alarms raised per day by the first fifty buildings under observation. In reality, despite thousands of reported sensors there are only a few hundred distinct types of sensors. The key is to reduce the complexity of sensorized distributed systems using automated or semi-automated methods to characterize sensors, determine their type based on the sensor data streams and make inferences about the quality of sensor data with minimal operator effort. This project will apply advances in unsupervised machine learning methods to compose, aggregate and interpret sensory data spatially and over time in order to enable robust derivation of semantically useful sensory information for applications and users resulting in better-utilized and robust systems. The intellectual merit of the project lies in building an information flow model, with a systematic capture and use of sensor meta-data that enables algorithmic approaches to data composition and building inferences. Using the proposed learning based automation approach along with programming and runtime support, the project will devise a data-to-decision flow for distributed systems operating across timing and reliability constraints. The project outlines smart buildings as an application driver for the envisioned sensorized distributed system with a working real-life testbed. This research will directly contribute to methods for discovery of tele-connections, such as dependence and causal relationships, between various sensory data streams which are crucial for devising effective control of devices connected to these distributed systems.The broader impacts of the project include advances in the design, deployment, management and programming methodologies for a new class of distributed computing systems that can deal with changing characteristics and topologies of the underlying sensor network. The particular testbed will demonstrate, how such methods can create energy-efficient, sustainable, and comfortable buildings for occupants. A number of educational and outreach activities have been planned to train the next generation talent for the emerging area of a data-driven internet of things. For the broader research community, the project will make available, SensorDepot, an open-source extensible architecture for implementing applications for sensorized distributed systems.

商业建筑、能源网和交通系统是新兴分布式系统的例子，这些系统开始配备大量传感器和执行器，用于感知周围环境条件、用户占用、能源使用状态等。此类仪表的目标是提高安全性、实用性并降低成本。这是一个难题，因为操作环境中人类、设备和网络的交互存在传感器数据的准确性、及时性、意义和价值的不确定性。系统操作员必须配置、监控和维护大量传感器。目前这是一项手动且容易出错的任务。大规模部署、管理和调整传感系统几乎变得不可能。在该项目使用的联网建筑微电网测试台中，观察到的前50栋建筑每天发出超过十万个警报。事实上，尽管报道了数千个传感器，但只有几百种不同类型的传感器。关键是要降低传感器化分布式系统的复杂性，使用自动化或半自动化方法来表征传感器，根据传感器数据流确定其类型，并以最少的操作员努力推断传感器数据的质量。该项目将应用无监督机器学习方法的进步来组合、聚合和解释空间和时间上的感知数据，以便为应用程序和用户可靠地导出语义上有用的感知信息，从而形成更好利用和强大的系统。该项目的智力优势在于构建信息流模型，系统地捕获和使用传感器元数据，从而实现数据组合和构建推理的算法方法。使用所提出的基于学习的自动化方法以及编程和运行时支持，该项目将为跨时间和可靠性约束运行的分布式系统设计数据到决策流程。该项目将智能建筑概述为设想的传感分布式系统的应用驱动程序，并具有工作的现实测试台。这项研究将直接有助于发现远程连接的方法，例如各种传感数据流之间的依赖性和因果关系，这对于设计连接到这些分布式系统的设备的有效控制至关重要。该项目的更广泛影响包括新型分布式计算系统的设计、部署、管理和编程方法的进步，该系统可以处理底层传感器网络不断变化的特征和拓扑。特定的测试平台将展示这些方法如何为居住者创造节能、可持续且舒适的建筑。计划开展许多教育和外展活动，为数据驱动的物联网新兴领域培训下一代人才。对于更广泛的研究社区，该项目将提供 SensorDepot，这是一种开源可扩展架构，用于实现传感器化分布式系统的应用程序。