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

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

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.