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Towards the Wikipedia of World Wide Sensors


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Presentation given by Jie Liu, Principal Researcher, Microsoft Research at the 2011 Cybera Summit / Sensor Web Enablement Workshop.

Presentation given by Jie Liu, Principal Researcher, Microsoft Research at the 2011 Cybera Summit / Sensor Web Enablement Workshop.

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  • 1. Towards the Wikipedia of World Wide SensorsJie LiuPrincipal ResearcherMicrosoft ResearchRedmond, WA 98052With thanks to Yan Xu, Suman Nath, Aman Kansal, Heitor Ramos, and Qiang Wang
  • 2. Paradigm Shifts in ComputingConsumer ComputingCloud ComputingCommunity Computing
  • 3. Computing in the Real WorldEnergyClimate ChangeEnvironmentHomeland SecurityDisaster ResponseCritical InfrastructureTransportationAsset ManagementHealthcareAssisted Living...
  • 4. 4th Paradigm of Scientific Discovery Experimental, theoretical, and computational, and data-driven science.
  • 5. Microsoft Sensing Research Collection Collaboration ExtractionWith applications in • Environmental monitoring • Data center operation and energy management • Mobile computing
  • 6. Atlantic Rainforest Micrometeorology Sensor Network in Brazil (University of São Paulo, Microsoft Research, Johns Hopkins University) Serra do Mar N S 50meters Towers(Images courtesy of Humberto Rocha, Rob Fatland, and Andreas Terzis)
  • 7. SwissEx Put all data together for better understanding Share data with other scientistsTemperature Snow Soil Streams Humidity
  • 8. Key Technical Challenges • Sensor networking • Energy management • Data yield improvements • Deployment strategy • Data management • Data interoperability • Data archival • Sensor tasking • Data visualization • Temporal-spatial indexing • Online aggregation and representation
  • 9. Participatory Environmental Monitoring Toolkit  Objectives • Facilitate socially inclusive environmental observation o Time & GPS location o Temperature & Humidity o CO2 o H2S • Leverage existing Microsoft technologies and user communities • Deliver a HW+SW toolkit in open source form  Key technologies • Microsoft Research low energy GPS location sensing and mobile data collection services • OData • World Wide Telescope(WWT) • Windows Azure
  • 10. Sense Web: The Wikipedia of sensors Real-time indexing, aggregation, and tasking.
  • 11. Cypress: Data Stream Compression• Compress data to reduce storage and I/O cost• Answer queries directly on compressed data• 100X compression typical sensor data streams • Take advantage of data types. • Trim data based on sufficient precision.Columns • Spectrum analysis – store data based on frequency bands • Store anomalies separatelyTrickles • Use sketches to compress “noise” – preserve data correlation. • Find correlations among data streams.GAMPS: • Store data as differences or ratios to reference streams.
  • 12. Open Data Sharing Popular Software Packages* Factors Influencing Technology Adoption* The Lowest Common Denominator OData • Easy of use • Additional value SQL • Professional technical support*Cyberinfrastructure for the waters networks: a Survey of AEESP and CUSHAI Members, K.A. Lawrence et al, May 2006,
  • 13. WWT and Geo-Data VisualizationWorldWide Telescope (WWT)• A visualization software environment • Enables a computer to function as a virtual telescope • (astronomers call it “the best VO (virtual observatory) implementation”) • Visualizes geo-data in 4D (space + time) • Integrated with Excel • Allows data sharing with controlled access – WWT Community • Empowers high-quality, intuitive, and interactive visual presentation via “WWT tour”• Datasets under consideration • Seismic event distribution against sbuductionslab slab models (USGS NEIC) • Standardized-format datasets (OGC, WxS, NetCDF, Shapefile, CSV, HDF, …) • Dataset and model output concept: plugging data generators directly into WWT • Draped raster, e.g. MODIS ocean, land and atmospheric products • Alternatice topgraphy, e.g. ice sheet thickness and bathymetry • Climate change thematic datasets, e.g. monthly sea ice extent from NSIDC• Free for research and education use
  • 14. WWT and Dust Storm Simulation• A mutually beneficial case study • Mind-swap, e.g. at Open Data for Open Science Developers Training • Improve science modeling • Improve computer engineering
  • 15. Wikipedia of Environmental Sensing• Open platform, open data• Free participation• Discoverable, searchable, interoperable• Visualized, annotated, built on top of each other
  • 16. Microsoft Environmental InformaticsSince 2010• Vision: facilitate seamless access to environmental data and information• Focus: data discoverability, accessibility, and consumability• Objectives: • advance the technology use in environmental research • create design wins using Microsoft technologies to • Foster innovations in computational environmental research • Advance interoperability of data and information sharing • Facilitate citizen science for environmental research• Build a community among multiple disciplines and stakeholders