The NSF awarded $2.5M for our work on dMist. Sept, 2017.
	The NSF awarded $800K for our work on Safety-critical Wireless Sept, 2017.
	The NSF awarded $500K for our work on Building Analytics. August, 2017.
	Nipun Batra defended his Ph.D., March, 2017
	"Occupancy State Detection using WiFi Signals" won the Best Poster Award in MobiSys Women's Workshop at ACM MobiSys'17, June 2017
	We were awarded $4.81M to launch the Link Lab. Construction begins in March! Nov, 2016.
	General Chair for ACM BuildSys'17, Nov 2016
	The NSF awarded $1M for our work on HomeSHARE. November, 2016.
	Juhi Ranjan defended her Ph.D. and is joining the faculty at IIIT-Delhi, 2017
	WalkSense won the Best Presentation Award at ACM BuildSys'16, June 2016
	Brick won the Audience Choice Award and the Best Demo Award and was Best Paper Nominee ACM BuildSys'16, June 2016
	Trane is funding our research on Metadata Inference in commercial buildings. Oct, 2016
	The NSF awarded $425K for our work on Collaborative Sensing. September, 2016.
	We were awarded $500K by CIT and Northrop Grumman to sponsor our Faculty search in CPS. July, 2016.
	Reactive Control for Autonomous Drones won the Best Paper Award at ACM MobiSys'16, June 2016
	We officially launched the Link Lab to enhance excellent in CPS research here at UVa. Feb, 2016.
	We officially launched the Link Lab to enhance excellent in CPS research here at UVa. Feb, 2016.
	The Wall Street Journal covered our Smart Home projects. June, 2014
	Invited Talk at the MIT Technology Review Digital Summit. June, 2014
	The Times of India covered the Double Hoo Research Grant. May, 2014
	Juhi Ranjan and Rahim Islam was awarded the Double Hoo Research Grant. May, 2014
	Awarded the Commonwealth endowed chair. August, 2013
	TPC Chair for ACM SenSys, Nov 2013
	NSF Invited Workshop on "Future Directions of Wireless Networking"”, Nov 2013
	The NSF awarded $600K for our work on The BetaHome Network: A Multi-Home Testbed for Smart Building Technology. August, 2013.
	Our Smart Water Heater was selected for the DOE's Max Tech and Beyond design competition for ultra-low-energy-use appliances and equipment. July, 2013.
	High school students at Tandem Friends School used our occupancy sensing system to publish a VJAS article. May, 2013.
	Invited Talk at the Microsoft Faculty Summit, July 2013
	Our FixtureFinder paper received the IP Track's Best Paper Runner Up award at IPSN'13, April, 2013
	IPSN Panel Member: "The Intellectual Core of Sensor Networks", Apr 2013
	Juhi Ranjan was awarded the NSF Graduate Fellowship Award. Apr, 2013
	CONET Panel Member: "Future Directions for Smart Cities", Apr 2013
	Juhi Ranjan won the NSF graduate fellowship, March 2013.
	Invited talk at Rutgers. October, 2012
	Jing Yang defended his Ph.D. and is headed to Citadel, June, 2012
	Tamim Sookoor defended his Ph.D. and is headed to the US Army Research Lab, May, 2012
	Keynote speaker at UbiComp's 1st ACM Workshop on Systems and Infrastructure for the Digital Home, Sept. 2012
	Keynote speaker at CPS Week's 3rd Workshop on Networks of Cooperating Objects, April 2012
	Vijay Srinivasan defended his Ph.D. and is headed to Samsung's pervasive computing group, December, 2011
	CBC Radio interview with Jie Liu and I, Nov, 2011
	Ginger Smith received honorable mention for the CRA Outstanding Undergraduate Researcher Award. Nov, 2011
	TPC Chair for ACM/IEEE IPSN, 2012
	Turn On the Server. It's Cold Inside. Nov 26, 2011
	Steering Committee Chair for ACM BuildSys, Nov 2011
	Trane is funding our research on occupancy sensing in commercial buildings. Nov, 2011
	TPC Chair for ACM BuildSys, 2011
	Jiakang Lu defended his Ph.D. and went to Nest Labs. November, 2011
	Jargon Watch: Pitstops, War-Texting, Data Furnace. Nov 1, 2011
	Tim Hnat defended his Ph.D. and is joining the faculty at U. Memphis. October, 2011
	TPC Chair for EWSN, 2011
	Invited talk at Brown University, July 2011
	Steamy Cloud Servers Installed In Homes and Businesses Could Be Used As Furnaces. July 25, 2011
	ACM TechNews email covered the Data Furnace. August 1, 2011
	Microsoft Suggests Heating Homes With "Data Furnaces". July 26, 2011
	Why Waste Servers' Heat?. July 23, 2011
	Will Your Next Furnace Be A Server Farm? July 2010
	"Data Furnace" Would Heat Homes While Flipping Bits. July 25, 2011
	How to heat your home with a data furnace. July 29, 2011
	Microsoft suggests heating your home with Data Furnaces. July 26, 2011
	Data furnace idea warms homes, crunches bits and bytes. July 25, 2011
	Cloud computing is a lot of hot air (and it can heat your house). July 25, 2011
	Where There's Smoke, There's a Signal. June, 2011
	"Breadcrumb" Routers Are A Firefighter's New Best Friend. June, 2011
	Tracking And Rescuing Firefighters With Disposable "Bread Crumb" Routers. June 23, 2011
	Wireless 'Breadcrumbs' That Won't Become Toast When Baked...or Soggy When Hosed. June, 2011
	Invited talk at Washington and Lee University, March 2011
	U.Va. Researchers Secure $2 Million National Science Foundation Grant to Smart Energy Systems. September, 2010
	UVa gets grant for 'smart building' research. Sept, 2010
	'Smart building' team earns grant. September 24, 2010
	Our energy conservation project was on the evening news (video). September, 2010
	The NSF awarded $2M for our work on smart buildings. July, 2010
	The NSF funded our work on body sensor networks. July, 2010
	Tamim Sookoor was awarded the SMART fellowship. June, 2010
	The NSF funded our work on run-time assurance. May, 2010
	NSF Invited Workshop on Future Directions of Networked Sensing Systems, Nov 2009
	Jiakang Lu accepted an internship at Philips Research. October, 2009
	Invited talk at CONET SENIOT, Italy, July 2009
	Invited talk at Berlusconi Workshop, Italy, July 2009
	The NSF funded my CAREER proposal on MacroLab. January, 2009
	Invited talk at UCLA, Jan 2009
	Invited talk at USC, Jan 2009
	Sensing the 'Ground Truth'. July 10, 2007
	An eye on downtown merchants?. July, 2007
	The NSF funded our research on dynamic binary translation for sensor networks. August, 2008
	HotEmNets Panel Member: "The Future of WSNs", June 2008
	Invited workshop on Theory and Practice in Wireless Networks at USC, May 2008
	Invited Panel: DoD ISAT Study on Engineering Ensemble Effects (E3), Oct 2007
	Invited Panel: "Tomorrow's Technology" at Darden Business Forum, Sept 2007
	Microsoft Research funded my research on embedded debugging. August, 2007
	The NSF funded our research on dynamic binary translation for embedded devices. August, 2007
	Microsoft Research funded our research on the Internet of Things. August, 2007
	I was awarded the UVa Excellence in Diversity Fellowship. September, 2006
	I was awarded the Siebel Foundation fellowship. September, 2005
	I was awarded the UC Bekeley GOF fellowship. September, 2004
	I was awarded the NDSEG graduate fellowship. September, 2000
	I was awarded the NSF graduate fellowship. September, 2000
	I was awarded the James Dickson Carr 4-year scholarship. September, 1996

Smart Buildings Buildings account for 40% of the total US energy budget, the largest energy consuming sector in the country, and many government and public organizations agree that a national grand challenge is to achieve a 70% reduction in building energy by 2030. However, energy retrofits are extremely costly. We are developing computational alternatives to conventional retrofits that use a combination of embedded sensing, intelligence, and control to save energy at 10x to 100x lower cost than conventional approaches. A cornerstone of this work is new technology to identify and track individual people, recognize common activity patterns, and detect object usage. These sensing technologies allow buildings to provide heating and cooling, lighting, and water heating services that respond to the dynamics of occupant presence, activities, and goals. Our current data predicts that, if they were deployed in every home, these techniques would reduce total US energy consumption by almost 3%, more than the energy used by the entire commercial airline industry.

Network Tasking: Many forecast that the Internet of Things will grow to over 1 trillion objects in the next two decades. However, people cannot practically coordinate the 1000s of connected objects they will encounter every day to help with their daily tasks. Networks of coordinating devices are too complex to operate with conventional human controls such as dials, switches and knobs. We are creating new tools and techniques to dynamically compose objects in a goal-driven fashion, adapting in real-time to changes in either network resources or high-level objectives. We have demonstrated these techniques in both static networks and networks that include aerial drones. In addition to goal-oriented tasking, we have shown that these tools enable system-level analysis, run-time visibility and debugging, and testing and verification, even in the face of severe limitations on energy, memory, and bandwidth.

The Data Furnace: In joint work with Microsoft researchers, we propose installing servers into homes, apartment buildings, and office buildings, and to use the exhaust heat as a primary heat source for the building. We call this the Data Furnace. This approach saves energy, saves money, and improves performance of Web applications. Read about this project in The New York Times.

Heating and Cooling Control: Heating and cooling account for 45% of all energy used in the average US home. Our work on heating and cooling control advanced the state of the art from reactive thermostats to learning thermostats. We showed that reactive thermostats actually increase energy usage in many homes. At the same time, we showed that learning thermostats could reduce energy usage by up to 28%, with as little as $25 in additional hardware. Since 2008, we evaluated four different incarnations of learning thermostats. One of these incarnations is now undergoing commercial trials.

Room-level Occupant Tracking: Many smart home applications would like to know the room location of occupants, but tracking people in a convenient and practical way is notoriously difficult. We are developing several new techniques for room location tracking by scanning a person's identity at the instant they cross the doorway threshold. One solution called the Doorjamb tracking system requires 1 sensor per doorway and can track the room location of multiple occupants with over 90% accuracy without requiring any RF transmitters or wearable tags, and without the use of privacy-invasive sensors such as cameras or microphones.

Wireless Communication: How do we achieve reliable, fast, and low-power communication for sensors and actuators? We created new techniques to improve physical layer, link layer, and network layer performance, particularly for nodes subject to building dynamics. Our techniques are now used by millions of devices worldwide.

Water Heating Control: Heating water is the second largest energy consumer in the average US home, after heating and cooling. We developed new algorithms to more intelligently mix and circulate how water, reducing energy consumption by over 10% on average. Our Smart Water Heater was selected for the DOE's Max Tech and Beyond design competition for ultra-low-energy-use appliances and equipment.

System Reliability Just like any computer system, reliability is paramount for the systems deployed in intelligent buildings. We designed several techniques to build and deploy robust systems, as well as to identify when the sensors, network, and applications are failing. Our results indicate that these techniqes can dramatically reduce maintenance costs.

Appliance and Fixture Monitoring: Many smart home applications would like to know which electrical and water fixtures are being used in the home, but this information can only be monitored in homes that have special hardware installed. We aim to address a larger number of homes by leveraging sensors that are already installed in many homes, including power meters, water meters, and home security sensors. Our algorithms can automatically learn which fixtures are in the home, recognize when they are used, and estimate the energy used by each one.

Lighting Control: Daylighting systems can save energy by dimming indoor lighting when enough sunlight is available outdoors, but when they make mistakes these systems can cause glare or annoy users by opening/closing the blinds too frequently. Studies show that, as a result, 50% of daylighting systems are disabled and the other 50% save only half as much energy as expected. We attempt to solve this problem with new prediction and control algorithms that can achieve a better balance between comfort and energy savings. Results indicate that our system can improve energy savings by 65% while simultaneously improving comfort by 40%.

We demonstrated that we can eavesdrop on wireless devices in a home and extract private information, even when all of the wireless data is encrypted, with a Fingerprint and Timing-based Snooping attack, or a FATS attack. Experiments on four houses demonstrate that we can infer when and how often the bathroom and kitchen are visited, when the person is sleeping, and when the home is occupied with 90-100\% accuracy. More...

MetroNet The MetroNet project will consist of sensors deployed in the storefront windows of downtown Charlottesville. The sensors can be used by the store owners, city customers, city planners, real estate customers, etc. More...

Wireless Localization: Our work in localization has several components, including hardware for acoustic and ultrasonic ranging, collections of acoustic, ultrasound, and RSSI ranging data from multiple different environments, a simulator called "silhouette" that simulates a localization deployment given an empirical ranging profile, and quantitative comparisons of localization with different models of ranging, different algorithms, and different environments.

In the pursuer-evader game (PEG), a vehicle (the evader) is driven through a sensor field that tracks its location and routes it to another device (the pursuer) that tries to autonomously capture the evader. In August 2005, we deployed PEG using passive infrared (PIR) sensors to track any moving object in an area covering 50,000 square meters with over 550 solar-powered sensor nodes. More...

This sensor network calibration technique allows each node to calibrate its sensor using the actuators of its neighbors. This creates an over-constrained system from which calibration coefficients can be inferred even in the face of some noise from both the sensors and actuators. More...