1. Virtual Sensor-Powered Spatiotemporal Aggregation and Transformation A Case Study Analyzing Near-Real-Time NEXRAD and Precipitation Gage Data Yong Liu Senior Research Scientist National Center for Supercomputing Applications University of Illinois at Urbana-Champaign yongliu@ncsa.uiuc.edu Co-authors: D. Hill, T. Abdelzaher, J. Heo, J. Choi, B. Minsker, D. Fazio Sept. 10, 2008 Environmental Information Management Conference 2008

2. Outline Motivation Goals of Our Work Exploring Virtual Sensor Abstraction CyberInfrastructure Technology Highlights Virtual Sensor Middleware NCSA Streaming Data Toolkit Query and Processing Capability Workflow Implementation using CyberIntegrator An Integrated Prototype System for Upper Illinois Watershed Further Research and Development Conclusions Acknowledgement

3. Motivation Near-real-time environmental sensor network applications Anomaly detection of sensor data in real-time E.g. Comparison of ground-based in situ gage rainfall data and NEXRAD Data Forecasting and optimal control of coupled engineered and natural systems in real-time E.g.: reduce and control combined sewer overflows (CSO) events in metropolitan areas such as the City of Chicago NSF WATERS (Water and Environmental Systems Research) Network’s goal of forecasting water quantity and quality everywhere at all times Lack of rainfall data in many locations Not enough rain gages

4. Goals Of Our Work Definition of virtual sensors Virtualization and re-purpose of existing sensor networks NEXRAD derived official products (Level III or MPE) not always suitable for environmental research usage Designed to serve National Weather Service’s needs Re-purpose NEXRAD for hydrological and environmental engineering research by creating near-real-time virtual sensors Enabling community participation of virtual sensor creation/sharing and associated workflows Similar to Microsoft Research SensorWeb vision with an extension from physical sensors to virtual sensors Similar to the “Many Eyes” approach used in wikipedia and many other Web 2.0 applications

5. The Concept of Virtual Sensors Our definition of a virtual sensor is the product of thematic, spatial, and/or temporal transformation and aggregation of one or multiple raw sensor measurement(s) An example of a virtual sensor From WATERS Network SEDS Draft (Chapter 5, p108) Signals from arrays of individual sensors and clusters of such arrays would be combined to provide higher-level information. For example, an array of soil moisture and temperature sensors might be coupled to a microclimate array to provide a virtual soil moisture flux sensor.

6. Exploring Virtual Sensor Abstraction: Three Primitive Types of Virtual Sensors Error correction and QA/QC filtering Intensive cleaning: suitable for numerical models, but not for extreme events research Spatiotemporal coordinate transformations Spatial: Local Plain, State Plain, WGS-84 Temporal: Julian calendar  Gregorian calendar Spatiotemporal measurement aggregations and transformations Simple Aggregation Wind Vector Virtual Sensor = Wind Direction + Wind Speed Up/Down Scaling Sensor Measurements fusion

7. Creating a Virtual Precipitation Sensor? Need near-real-time measurements of 30-minute rainfall accumulations in specific locations with WGS-84 latitude/longitude coordinates (X,Y) There are no rain gauges in or near the locations The Next Generation Radar (NEXRAD) system provides near real-time spatial measurements of radar reflectivity, which are correlated with rainfall. How can we use NEXRAD to give us rainfall virtual sensor? Needs spatial, temporal and thematic transformation!

8. NEXRAD Data Stage I: Single radar site Level II Three original measurements Reflectivity, radial velocity and spectrum Level III 41 products (virtual sensors) Stage II Fuse Level III with rain gage data (4kmx4km) Stage III A mosaic of Stage II products from multiple radar sites MPE (Multi-sensor Precipitation Estimator) Hourly rainfall accumulations that fuse Stage III and GOES (Geostationary Operational Environmental Satellite) satellite products

9. Technology Approach Enable the creation of virtual precipitation sensors using NEXRAD Level II data in near real-time at User-specified locations and time intervals (point-and-click on Google Map interface) Our technology includes Streaming data and virtual sensor management middleware and ontology General spatiotemporal and thematic workflow capability An intuitive Google Map-based web interface

10. NCSA Streaming Data Toolkit Can model arbitrary time-series data Has implementations for RBNB, ActiveMQ JMS and standalone-mode (i.e., directly writing to repository). Can support complex temporal approximation/range queries An example shown in the next two slides using NEXRAD data

11. Streaming Data Query Example: September 04, 2007 Level II Measurements from Chicago (KLOT) Station 15:56:2416:09:1716:00:3316:15:0416:23:0716:34:1516:54:3616:57:5018:36:2818:46:1018:56:4619:06:3119:16:1319:25:5619:35:3919:45:2320:50:1520:53:0620:55:4420:58:5321:08:3521:27:0021:38:0021:47:4221:57:2322:07:08 Clear-Air Mode Precipitation Mode Note: Radar switches between clear-air and precipitation mode Sensor goes off-line 4 times Time between measurements is never exactly 5, 6, or 10 minutes 8:4411:0810:362:51

12. Streaming Data Query Example (contd.): What is the Measurement at 17:30? 15:56:2416:09:1716:00:3316:15:0416:23:0716:34:1516:54:3616:57:5018:36:2818:46:1018:56:4619:06:3119:16:1319:25:5619:35:3919:45:2320:50:1520:53:0620:55:4420:58:5321:08:3521:27:0021:38:0021:47:4221:57:2322:07:08 Due to the sensor going off-line, the nearest measurements to 17:30 are 16:57:50, and 18:36:28. The temporal variability of rainfall processes is too great to use linear interpolation between these two times (esp. since it is raining at 16:57 and not at 18:36. When did it stop raining?) Return “NA” measurement – the streaming data toolkit “knows” the limitations of the data when creating a virtual sensor

13. Virtual Sensor Middleware Functionality Registers/de-registers virtual sensors metadata in the registry Dynamically triggers back-end workflow execution through the workflow RESTful web service to produce new streaming data Dynamically generates an input file needed for the workflow execution which provides a list of virtual sensor coordinates and unique IDs

14. Scientific Workflows to Produce Virtual Sensor Streams Two scientific workflows Has been Implemented in NCSA CyberIntegrator workflow tool Will provide NEXRAD Level II-based virtual sensor data stream in near-real-time 0. NEXRAD data stream 1. Spatial transformation to points 2. Thematic transformation to rainfall rates 3. Publish one data stream per point of interest 4. Temporal aggregation to produce n-minute rainfall accumulation at one point 5. Publish one virtual sensor data stream with n-minute rainfall accumulation Workflow 1: step 0,1,2,3 Run periodically at the arrival rate of the NEXRAD Level II data stream Workflow 2: step 4,5 run at the user-specified rain accumulation interval E.g.: every 20 minutes

15. An Integrated Prototype System for Upper Illinois Watershed NEXRAD KLOT Station Coverage Area Upper Illinois Watershed Virtual Sensor Locations KML Layer Management Creating Virtual Sensors & Scheduling Execution of Workflows In final Integration/testing stage Accepted as a Demo in ACM GIS 2008 conference on Nov.7, 2008

16. Further Research and Development Rainfall Virtual Sensors Anywhere in the US at Near-Real-Time A potential collaboration project with Microsoft Research A potential service provided by NCSA Virtual sensors across multiple sensor networks Polygon, Grid-based rainfall virtual sensors using one or multiple NEXRAD stations Fusion with rain gages if needed Cloud computing technology for virtual sensors creation There are about 160 NEXRAD stations in the US Lots of data, just-in-time processing A perfect pilot project for cloud computing (In collaboration with the Computer Science Department at UIUC) Provenance-aware Virtual Sensors Critical for community participation, validation and contribution

17. Conclusions The introduction of virtual sensors and the concept of virtualization are useful for re-purpose of existing sensor network We have successfully developed generic streaming data and virtual sensor middleware, implemented spatiotemporal and thematic transformation workflows using NCSA CyberIntegrator toolkit, and prototyped an integrated system (in final integration/testing stage) Our ongoing and further work will enable us to provide a participatory CyberEnvironment where community participation/contribution is promoted A Wiki-style virtual sensor creation, sharing and validation

18. Acknowledgments AESIS (Adaptive Environmental Sensing and Information Systems) Initiative at NCSA/UIUC Barbara Minsker, David Hill, Tarek Abdelzaher, Jin Heo, Seon Kim, David Fazio, Murugesu Sivapalan, etc. NCSA/Office of Naval Research TRECC Digital Synthesis Framework for Virtual Observatory Project Jim Myers, Luigi Marini, Joe Futrelle, Alejandro Rodriguez, Terry McLaren, Bob McGrath, Peter Bajcsy, Rob Kooper, Wenwu Tang, etc. Illinois IACAT (Institute of Advanced Computing Applications and Technology) Project Don Wuebbles, Barbara Minsker, Praveen Kumar, Jim Myers, Xiaowen Wu, etc.