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41 mins
ILP Video

Internet of Things: Building the City Central Nervous System for High-Definition Decision Making

John Williams
Professor of Information Engineering, Civil and Environmental Engineering and Engineering Systems
The Internet of Things allows accurate tracking of items and people. Integrating this high-resolution data into a City Nervous System can dramatically improve city-level outcomes. The structure and dynamics of the interactions among city residents shapes quality of life, emergent economic productivity, environmental impact and security. Combining Big Data "reality mining" for sensing human behavior and infrastructure systems with network modeling of physical and social systems will enable a true city 'nervous system' that supports both government decision-making as well as individual and group social influence feedback loops that can dramatically improve city life.
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37 mins
ILP Video

The Importance of Production Eco-systems to Innovation

Martin Schmidt
Associate Provost
MIT Department of Electrical Engineering and Computer Science
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29 mins
ILP Video

Large Scale Modeling Techniques and Systems to Understand Population Behaviors and Patterns from Telecommunication Carrier’s Big Data

Wei Fan
Huawei Noach's Ark Lab
Associate Director
In order to understand the behaviors and patterns of growing population in modern cities, a rich information source is to utilize carrier’s mobile brand band data. However, due to the sheer volume, pattern mining and modeling is a non-trivial task for both systems and statistical analysis. As a typical example of New Moore's law, the amount of 3G mobile broadband (MBB) data has grown from 15 to 20 times in the past two years 50TB per day for typical city in China by December 2012 and it is still growing), large scale processing and mining of these data are becoming increasingly necessary. In this talk, we describe large scale offline analysis over Hadoop, and real-time inline analysis based on Huawei’s Streaming platform (StreamSMART), as well as, data compression techniques that keeps the data in secondary storage. We will describe the set of analyses performed in our lab, such as trajectory prediction, user behaviors, etc. Importantly, StreamSMART can process data in real-time at line rate as data being ingested and the accuracy is identical to offline analysis, based on the set of analysis performed in our lab. We will describe architecture, key components and programming models of StreamSMART. Importantly, we will share the predictive modeling techniques as well as results from a major city in China.
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48 mins
ILP Video

Enabling Next Generation Wireless Networks: Interference Management, Spectrum Sharing, Indoor Localization, and Beyond

Dina Katabi
Professor of Computer Science and Engineering
Director, Center for Wireless Networks and Mobile Computing (Wireless@MIT)
MIT Department of Electrical Engineering and Computer Science
Practical systems offer efficient solutions to the challenges facing wireless communications in dense urban areas. MegaMIMO enables independent devices to act as one humongous MIMO transmitter, delivering 10x higher data rates for WiFi and cellular networks by transmitting as many concurrent streams as the total number of antennas on all devices. Spectrum sharing and cognitive communication leverages the sparse Fourier Transform to sense and decode GHz of sparse spectrum in real-time at low power. New advances in indoor localization achieve centimeter scale accuracy, enabling smart environments and context-aware applications through ubiquitous RFID tracking.
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40 mins
ILP Video

Global, Regional, Local: Energy Challenges for China and Responses at Scale

Robert Armstrong
Director, MIT Energy Initiative (MITEI)
Chevron Professor of Chemical Engineering
MIT Department of Chemical Engineering
From 2000 to 2010, China's energy use grew 130 percent, and it continues to grow along with China’s urban centers. Fueling rapid urban expansion will require a refined and integrated energy program for the country, one which confronts present challenges at multiple scales, considering economic and climate impact, regional transportation networks and smart grids, intermittency and storage of renewables, and the role of individual firms, households and citizens in realizing the greatest efficiency gains for the whole country and for the world.
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