10/14/11. Big data in science Application to large scale physical systems

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1 Big data in science Application to large scale physical systems Large scale physical systems Large scale systems with spatio-temporal dynamics Propagation of pollutants in air, Water distribution networks, transportation networks Structures interacting with wind or seismic loads, Characterization of the behavior of these systems Aude Hofleitner (advisor Alexandre Bayen) Electrical Engineering and Computer Science UC Berkeley Estimation/control of large scale physical systems Abstraction (modelling) of the physical world: Partial differential equation (PDE) Statistical model Integration of sensor data into the mathematical model Big data provides a better understanding of the physical world Online estimation and forecast from streaming data requires real time computation Closing the loop on the physical system: Control theory Feedback and automatic control sensor data Algorithms Distributed parameters system abstraction Physical world control Classical source of traffic information Dedicated traffic monitoring infrastructure: Self inductive loops Wireless pavement sensors FasTrak, EZ-pass transponders Cameras Radars License plate readers Issues of today s dedicated infrastructure Installation costs Maintenance costs Reliability Coverage Privacy intrusion 1

2 Mobile data available for transportation networks Mobile data available for transportation networks 0.5% of Mobile Millennium data (SF taxis) What we can infer from Big data 2007 (511.org) 2010 (UC Berkeley, Mobile Millennium) 0.5% of Mobile Millennium data (SF taxis) Mobile Millennium An early instantiation of participatory sensing Funded by California DOT (DRI), US DOT, Nokia, NAVTEQ, NSF 5000 downloads of the FIRST Nokia traffic app worldwide 60 million data points / day from dozen of sources (smartphones, taxis, fleets, static sensors, public feeds) Real-time nowcast (soon forecast) of highway and arterial traffic, Routing and data fusion tools. 2

3 Privacy issues for location based services Temporal sampling: GPS tracks Precise location Individual patterns User generated content Personal info Activity Data collection: spatially aware traffic monitoring Probabilistic Always Sampling strategy uses Virtual Trip Lines (VTLs): geographic markers deployed at privacy aware locations which probabilistically trigger GPS updates for a proportion of the phones crossing them. Phone anonymizes data GPS update is encrypted and sent Spatial sampling with Virtual Trip Lines (VTLs) Virtual Trip Line (VTL): virtual trigger to send measurements step 1: download VTLs to cell phone (automatic) step 2: check: does my GPS trajectory intersect a VTL? if yes, send VTL measurement update VTL database VTL updates request database VTLs receive VTLs [VTL_ID=003, time=11:16:42, [VTL_ID=002, time=11:16:01, speed=52mph] speed=53mph] [VTL_ID=001, time=11:15:32, speed=54mph] Reconstruction capabilities 10 measurement points Physical model and data assimilation enable state estimation Sparse velocity sensing State estimation 3

4 Learning in urban networks Going further: Integrated Corridor Management Hybrid approach of statistical model and queuing theory Learn the probability distribution of travel times Learn the characteristics of the network (signal timing) Detects signal location Parking Field test experiment 20 drivers 3 hours of field test on 3consecutive days 2 distinct loops (1.9 and 2.3 miles) Bluetooth travel time measurements Trajectory measurements (GPS device) Ramp Metering Smartphone enabled reroute BART CMS Local Arterial Traffic Signals Express Lanes e-wellness e-wellness Noise levels inferred from traffic From static noise maps to dynamic hourly maps Noise levels inferred from traffic From static noise maps to dynamic hourly maps. Emission levels inferred from traffic Emission and atmospheric dispersion models Next generation: sensor based. Today: noise map (static) Tomorrow: hourly noise map Today: pollution map Tomorrow: sensor based data Courtesy NASA/DHS 4

5 The emergence of the human as a sensor Floating sensor network Best known sensor for earthquakes: accelerometer USGS has dedicated array of embedded accelerometers Human is faster than USGS by posting on Twitter All smartphones have accelerometers, UCLA already succeeded in capturing a P-wave from a smartphone (CENS) Information could be enhanced by having additional accelerometer information available. UC Berkeley ishake app and shake table testing procedure Fall2011: deployment of 100 floating / submersible units in the San Francisco Bay / Sacramento Delta All units include GSM (soon: Android), GPS, linux gumstix, Zigbee, water quality sensor platform Interfaced with static sensor infrastructure in the Delta 500 nodes of the Magellan / NERSC cluster at LBNL, IBM cluster USGS shakemap (from static USGS sensors) Big data in science Challenges and interests of big data. Interests of big data and mobile sensing Application to large scale physical systems Leverages the existing sensing infrastructure No installation and maintenance cost Better understanding of long term trends and evolutions from large amounts of historical data Challenges and open questions How shall we approach the data management issues? Who should be responsible for the data collection, processing and storage? (State vs. University vs. industry) What level of privacy? Aude Hofleitner aude@eecs.berkeley.edu Alexandre Bayen bayen@berkeley.edu 5