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

1. Towards the Wikipedia
of World Wide Sensors
Jie Liu
Principal Researcher
Microsoft Research
Redmond, WA 98052
With thanks to Yan Xu, Suman Nath, Aman Kansal, Heitor Ramos, and Qiang Wang

2. Paradigm Shifts in Computing
Consumer Computing
Cloud Computing
Community Computing

3. Computing in the Real World
Energy
Climate Change
Environment
Homeland Security
Disaster Response
Critical Infrastructure
Transportation
Asset Management
Healthcare
Assisted Living
...

4. 4th Paradigm of Scientific Discovery
Experimental, theoretical, and computational, and data-driven science.

5. Microsoft Sensing Research
Collection Collaboration
Extraction
With 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 scientists
Temperature 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 separately
Trickles • 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 Visualization
WorldWide 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 Informatics
Since 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