CSR-EHS: Collaborative Research: A General, Efficient and Robust Platform for Enabling Control Applications in Sensor Networks

该研究项目的目标是建立一个应用基础设施和一套嵌入式系统软件，使软件控制和监控应用，如加热，通风和空调系统（HVAC），在低功耗，有损无线传感器-执行器网络的快速发展。这种用户偏好驱动的控制应用需要能够适应大规模分布式网络中的噪声、损失和故障，并且需要在获取用户偏好、做出控制决策并将这些决策传播给适当的执行器方面实现高度自动化。正在开发的传感器控制系统（SCS）提供了一个高级平台，允许用户在整个网络的粒度上表达控制应用需求，使用户能够通过抽象低级传感器网络细节来关注其部署的需求。SCS将机器学习和统计学技术与软件系统和数据库中工作的高级抽象相结合，允许在这些系统中固有的损失和不确定性的情况下做出数学上合理的决策。该项目将在高度仪器化的办公环境中演示这种方法。这项研究有可能通过开发一类新的无线控制系统来影响工业，也有可能通过将跨学科的思想注入到EECS的各个子领域来影响学术界。在这项研究过程中开发的数据集、代码和传感器执行器部署将用于一系列教育活动。

The goal of this research project is to build an application infrastructure and a suite of embedded system software that will enable the rapid development of software control and monitoring applications, such as heating, ventilation, and air conditioning systems (HVAC), on low-power, lossy wireless sensor-actuatornetworks. Such user-preference driven control applications require the ability to adapt to noise, loss, and failures in a large-scale distributed network, and demand a high degree of automation inacquiring user preferences, making control decisions and disseminating those decisions to the appropriate actuators.The Sensor Control System (SCS) being developed provides a high-level platform that allows users to express control application requirements at the granularity of the entire network, enabling users to focus on the requirements of their deployments by abstracting away the low-level sensor network details. SCS combines techniques from machine learning and statistics with high-level abstractions inspired by work in software systems and databases, allowing for mathematically-sound decision making despite loss and uncertaintythat is inherent in such systems. The project will demonstrate the approach on a highly instrumented office environment. This research has the potential to impact industry through the development of a new class of wireless control systems, and academia through the infusion of cross-disciplinary ideas into a variety of sub-fields of EECS. The datasets, code and the sensor-actuator deployments developed duringthe course of this research will be used in a range of educational initiatives.