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.

商业建筑物，能源网和运输系统是新兴分布式系统的示例，这些系统开始使用大量传感器和执行器来仪器，以感知环境环境条件，用户占用，能源使用状态等。这种仪器的目的是提高安全性，公用事业和降低成本。这是一个严重的问题，这是由于人类，设备和网络在操作环境中的相互作用而与真实性，及时性，含义和价值数据的不确定性相互作用。必须由系统操作员对大量传感器进行配置，监视和维护。目前，这是一项手动和错误的任务。在大规模上部署，管理和适应传感器系统几乎是不可能的。在该项目使用的网络建筑物的微网格测试床中，前五十座建筑物每天都会发出超过十万个警报。实际上，尽管有成千上万的传感器，但只有几百种不同类型的传感器。关键是使用自动化或半自动化方法来降低传感器化系统的复杂性，以表征传感器，根据传感器数据流确定其类型，并以最小的运算符的努力来推断传感器数据的质量。该项目将在无监督的机器学习方法中应用进步，以空间和随着时间的流逝组成，汇总和解释感觉数据，以便为应用程序和用户提供强大的语义有用的感官信息，从而实现更好和强大的系统。该项目的智力优点在于建立信息流模型，并通过系统的捕获和使用传感器元数据，该传感器元数据可以实现算法方法来进行数据组成和建筑推论。使用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限制的分布式系统设计一个数据到否定流。该项目概述了智能建筑物作为设想的传感器分布式系统的应用程序驱动程序，并具有工作现实的测试床。这项研究将直接有助于发现电视连接（例如依赖关系和因果关系）的各种感觉数据流之间的方法，这对于设计与这些分布式系统相关的设备有效控制至关重要。项目的更广泛的影响包括设计，部署，管理和编程方法，这些范围内的分配器网络具有变化的特征和替换性特征的新型类别。特定的测试床将证明，这种方法如何为居住者创造节能，可持续和舒适的建筑物。计划进行许多教育和推广活动，以培训数据驱动的物联网的新兴领域的下一代人才。对于更广泛的研究社区，该项目将提供，SensorDepot是一种可扩展的架构，用于实施传感器分布式系统的应用程序。