Creating a Metadata Schema for Portable Smart Building Applications

Affiliations: University of California - Los Angeles, University of California - San Diego
Papers: BuildSys 2016 Paper
Links: Brick Website

Smart building applications rarely experience widespread adoption due to the prohibitive cost of porting them to different buildings. We created a schema, called Brick, that defines an ontology for sensors, subsystems and relationships among them. We demonstrate the effectiveness of Brick by using it to represent six diverse buildings, comprising of 17,700 data points, and running eight unmodified applications on these buildings.

Study of Use of Software Thermostats in Commercial Buildings

Affiliations: University of California, San Diego
Papers: Ubicomp 2016 Paper

Research has shown that contemporary thermostats are inadequate to keep occupants satisfied in offices as they can be difficult to use and provide insufficient control. We provide occupants with a personalized web portal through which they can view HVAC status, change temperature settings and send feedback to building manager. We study the impact of use of our software thermostat by 220 registered occupants across 21 months.

Standardization of Sensor Naming using Machine Learning

Affiliations: University of California, San Diego
Papers: BuildSys 2015 Paper

Sensors already deployed in the field do not have a standard naming convention or ontology to describe them. We collect information for 180,000 sensors across 55 buildings at UCSD, and use their metadata to identify the type of sensor and provide them a uniform name across the system. We first group similar sensors using hierarchical clustering, and then use active learning with random forest classifier to label the sensors.

Fault Analysis and Reporting for Commercial HVAC Systems

Affiliations: University of California, San Diego
Papers: BuildSys 2014 Paper, BuildSys 2014 Demo

Modern fault management systems for HVAC are rudimentary, and fail to detect pertinent faults that may lead to equipment damage or energy wastage. We build a web services based fault management framework for better information integration and flexibility. We also develop a comparative data mining based fault detection technique to identify many faults missed by the traditional system.

BuildingDepot: Data Storage Platform for Building Sensor Information

Affiliations: University of California, San Diego
Papers: BuildSys 2012, BuildSys 2013
Links: API Documentation

Traditional building management systems do not provide adequate support for storing sensor data and contextual metadata. We created BuildingDepot, a RESTful web service based building data store that integrates information from thousands of sensors across a building. We incorporate useful features such as role based access control, sensor tagging for search and contextual information, and RESTful APIs for third party apps.

Better Buildings Case Competition

Affiliations: University of California - San Diego, Department of Energy
Proposals: Picking up the PACE, Experimenting with Efficiency
Presentation: Picking up the PACE
Awards: Most Innovative Award

We participated in a competition hosted by Department of Energy for improved building energy efficiency. We wrote two proposals: (a) Innovative methods for incentivizing building owners and managers to adopt energy efficiency retrofits. (b) Proposal for improving sustainability practices in research laboratories in universities.

Occupancy Detection using WiFi Infrastructure for HVAC Control

Affiliations: University of California - San Diego, Ericsson Research
Papers: BuildSys 2012, SenSys 2013

Occupants of modern buildings typically connect to enterprise WiFi network when they are in the building. We tap in to the authentication logs of the WiFi network to determine when occupants connect to access points near their offices. We use this as a proxy for occupancy, and use it to control the HVAC system.

Power Variability in Modern Computing Platforms

Affiliations: University of California - San Diego
Papers: USENIX HotPower 2012

As CMOS technology continues to shrink, process variation leads to changes in performance from chip to chip. We characterize the variation in power consumption of Intel Core i5-540M processors using specially instrumented Calpella and Sandy Bridge platforms from Intel for accurately measuring CPU power.

Occupancy Based Control of HVAC System

Affiliations: University of California - San Diego
Papers: BuildSys 2010, IPSN 2011

We develop a low cost, battery powered, wireless occupancy sensor that uses a combination of PIR and door sensors. They have 97% accuracy of detection in single room offices. We connect this information to the building management system and control HVAC based on occupancy.

Plug Level Energy Meter

Affiliations: University of California - San Diego
Papers: BuildSys 2011

We created an extremely low cost, non-intrusive, wireless energy meter to monitor and actuate plug loads within buildings. The design is modular, and contains a programmable microcontroller. We exploit the meter for managing plug loads during demand response events and estimating energy wastage in offices.

Power Modeling vs Measurement of Modern PCs

Affiliations: University of California - San Diego
Papers: USENIX ATC 2011

We use a specially instrumented Calpella motherboard from Intel to breakdown power consumption of individual components in a computer - processor, memory, solid state disk and ethernet. We design an in house hardware harness and use National Instruments DAQs for power measurement. We model the power consumption of motherboard components using Linux performance counters.

Sleep over Neighbor Addressed Packets in WiFi 802.11b

Affiliations: University of California - San Diego, California Institute for Telecommunications and Information Technology (Calit2)
Papers: IEEE Globecom 2010

802.11b WiFi clients listen to packets even when they are not addressed to them. We use a software defined radio to show significant power savings can be obtained in crowded spaces when the client sleeps after receiving just the header of the packet. QualNet simulations show promising power savings in different environments.

Prediction of Handoff in Cellular Networks using Rough-Neuro Approach

Affiliations: Visvesvaraya National Institute of Technology (VNIT), Nagpur
Papers: Springer chapter in Advances in Machine Learning and Data Analyses 2010, International Journal of Pattern Recognition and Artificial Intelligence 2010

We use prediction of handoff in cellular network as an example application to show that rough set theory is effective in reducing feature set. We simulate a cellular network on Matlab and extract features for prediction. We use rough set theory to reduce the feature set and neural network for handoff prediction.