BU-Led Team Wins $1M NSF Grant for Smart City Research
By Mark Dwortzan
The closed-loop system defining a Smart City entails not only collecting data from sensors, but also implementing control actions through devices based on intelligent decision making.
Professor Christos Cassandras (ECE, SE) and systems engineering graduate student Yanfeng Geng (PhD'13) developed a preliminary version of a Smart Parking system that enables a driver to enter a desired destination and price range into a mobile device and reserve a vacant, appropriately priced parking space that's closest to the destination.
Imagine driving in a city where you never have to search for a parking spot, traffic tie-ups are rare, and information on nearby accidents is displayed on your dashboard almost instantaneously. If a research team led by Professor Christos Cassandras (ECE, SE) achieves its goals, such “smart cities” could become commonplace across the U.S. in the coming decade.
The team—which includes Professors Yannis Paschalidis (ECE, SE), Azer Bestavros (CS) and Assaf Kfoury (CS, SE) from Boston University; University of Massachusetts-Amherst Professor Weibo Gong (ECE); and University of Connecticut Professor Robert Gao (ME)—has received a $1 million grant from the National Science Foundation to create the technological infrastructure for a wide range of Smart City applications aimed at reducing the congestion, pollution, fossil fuel consumption, accidents, cost, and sheer inconvenience associated with operating motor vehicles in urban environments.
“Our Smart City focus has the potential of revolutionizing the way we view the city in the future: from a passive living and working environment to a highly dynamic one with new ways to deal with transportation, energy and safety,” said Cassandras.
These new ways include a Smart Parking system that assigns and reserves parking spaces based on a driver’s requested destination and price range, a traffic regulation system that dynamically controls traffic lights based on real-time road conditions to improve the flow of vehicles throughout a city, and electric vehicle charging stations where drivers can pay to download electric power to their vehicle from a smart grid—or get paid to upload excess electric power from their vehicle to the grid.
To create an infrastructure for these and other Smart City applications, the team plans to design a mobile sensor network of motor vehicles, each equipped to collect data from its onboard sensor and quickly transmit it across the network from one vehicle to the next. Using the network, a driver who comes across an accident scene could, for instance, punch a dashboard menu button and transmit the accident location to every other motor vehicle in the network.
The mobile sensor network that the researchers envision will collect and exchange data such as accident locations or hazardous road conditions; dynamically allocate resources such as available parking spaces or electric vehicle charging stations; ensure secure and reliable data exchange across the network; and make real-time decisions, such as coordinating sets of traffic lights, without compromising the safety of drivers, bikers or pedestrians. To achieve those objectives, they will advance new sensing, data acquisition, decision-making and dynamic resource allocation capabilities.
The team will test these capabilities via the Sustainable Neighborhood Lab (SNL), a BU-organized living laboratory for sustainable urban development in Boston’s Back Bay in cooperation with the Neighborhood Association of Back Bay, local commercial groups, the City of Boston and the local electricity distribution utility. At BU, a garage is already partially instrumented and will be fully equipped to implement the Smart Parking system, which will also be tested with on-street parking in collaboration with the SNL and the City of Boston.
“The whole concept of a Smart City is beginning to gain prominence in the U.S. and abroad,” said Cassandras. “Our approach is unique in its focus on sensor network infrastructure, its use of optimization techniques for dynamic resource allocation, and its development of a new software framework for real-time, Smart City applications.”
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