Title: Enabling the SmartGrid with IoT Sensors and Edge-Cloud Analytics

8:30 am – 9:30 am, November 13, 2019


Wireless sensors and edge-cloud analytics have the potential to gather and process vast amounts of data about the physical world, offering radical new insights about everything from critical infrastructure to interpersonal interactions. But designing, deploying, and operating geographically-distributed systems consisting of a hierarchy of sensing, storage, compute, and communication elements raises interesting new challenges across the system stack. In this talk, we will discuss our experiences designing IoT systems to address several power and power grid monitoring problems. In particular, this talk will focus on three systems—PowerBlade, Triumvi, and GridWatch—and their motivation, design, and deployment. PowerBlade explores how to cost-effectively characterize, capture, and classify widespread plug-load energy usage—representing the fastest growing and least understood segment of end-use energy consumption—across hundreds of homes and offices representing tens of thousands of sensors.

Triumvi explores how to make circuit level energy metering, useful for a variety of facilities trending, energy savings, and fault detection & diagnostics applications, more efficient and scalable. Finally, GridWatch explores how to scalably and cost-effectively detect and respond to the power outages that stymie residential and business activity in under-developed power grids using mobile and fixed sensors, data analytics, and reporting systems in Sub-Saharan Africa, finding that conventional approaches to outage detection vastly underreport customer experiences. The systems all share similar architectures, require new sensor devices and edge-cloud data processing, and wrestle with power management and networking. But they ultimately demonstrate both the tremendous potential and significant challenges of this nascent computing class.

Bio of the speaker

Prabal Dutta is an Associate Professor of Electrical Engineering and Computer Sciences at the University of Calfornia at Berkeley, where he co-directs the CONIX Research Center. Previously, he was a Morris Wellman Faculty Development Associate Professor of Electrical Engineering and Computer Science at the University of Michigan. His interests span circuits, systems, and software, with a focus on mobile, wireless, embedded, networked, and sensing systems with applications to health, energy, and the environment. His work has yielded dozens of hardware and software systems, has won five Top Pick/Best Paper Awards, two Best Paper Nominations, and a Potential Test of Time 2025 Award, as well as several demo, design, and industry competitions. His work has been directly commercialized by a dozen companies and indirectly by many dozens more, has been utilized by thousands of researchers and practitioners worldwide, and is on display at Silicon Valley’s Computer History Museum. His research has been recognized with an Sloan Fellowship, a CAREER Award, a Popular Science Brilliant Ten Award, an Intel Early Career Faculty Fellowship, and as a Microsoft Research Faculty Fellowship Finalist. He has served as chair or co-chair of MobiSys’18, BuildSys’17, IPSN’17, ESWEEK’17 IoT Day, HotMobile’16, SenSys’14, and HotPower’11, and on the DARPA ISAT Study Group from 2012-2016, where he co-chaired numerous studies. He holds a Ph.D. in Computer Science from UC Berkeley (2009), where NSF and Microsoft Research Graduate Fellowships supported his research. He received an M.S. in Electrical Engineering (2004) and a B.S. Electrical and Computer Engineering (1997), both from The Ohio State University. Website:

Title: Context Driven Analytics and AI for Infrastructure and Facility Management

8:30 am – 9:30 am, November 14, 2019


Engineers and managers involved in facility/infrastructure operations need situational awareness and assessment about as-is conditions when making daily decisions and developing short- and long-term plans. Yet, currently situational awareness of engineers is often challenged due to lack of useful and actionable information that are relevant to specific facilities and infrastructure systems in their purview. Advances in sensing and reality capture technologies, such as 3D imaging either on stationary platforms or on drones and in-situ sensing, streamline capturing of data depicting as-is conditions. Data collected from these technologies, integrated with building information models, enable context-driven analyses of as-is conditions, generation of actionable information related to specific facilities/infrastructure systems, and development of algorithms that help support proactive and predictive operations. This presentation will provide an overview of opportunities and research approaches associated with integration of sensor data with building/infrastructure information models and with development of context-driven algorithms. It will demonstrate applications of these approaches through specific deployments done in several facilities and other infrastructure systems, and highlight specific research projects being conducted at Carnegie Mellon University with a vision towards self-aware autonomous facilities and infrastructure systems.

Bio of the speaker

Dr. Burcu Akinci is Paul Christiano Professor of Civil & Environmental Engineering and Associate Dean for Research at the College of Engineering at Carnegie Mellon University. She was also co-director of Smart Infrastructure Institute which conducts research on data-driven awareness about infrastructure systems, advanced information models and visualization approaches, and proactive decision support.

Dr. Akinci’s research interests modeling and reasoning about information rich histories of buildings and infrastructure systems, to streamline construction and infrastructure operations and management. She specifically focuses on investigating utilization and integration of building information models with data capture technologies, such as 3D imaging and embedded sensors, to capture semantically-rich as-built histories of construction projects and infrastructure operations and to support proactive and predictive operations and management.

Dr. Akinci is the recipient of the Professor of the year award in 2011 from the ASCE Pittsburgh section, the CETI Outstanding Early Career Researcher award from FIATECH in 2008 and Walter L. Huber Civil Engineering Research Prize from ASCE in 2007. She has best paper awards from Journal of Computing in Civil Engineering in 2002, and from Construction Research Congress, ISARC and ICCCBE in 2009, 2011 and 2014 respectively.

Dr. Akinci has one patent, two provisional patents and over 70 referred journal publications and 100 conference publications. She has given over 100 invited presentations and co-edited books on CAD/GIS Integration and on Embedded Commissioning. She co-founded and is the Chief Innovation Officer at LeanFM Technologies, which won 2017 Pittsburgh Business Times Innovation Award.