1. Fuzzy Data Collection in Sensor Networks Lee Cranford Marguerite Doman July 27, 2006

2. Overview Overview of Sensor Networks Sensor Network Applications Research Objective Prototype Platform Proposals Our Modifications Ongoing Work

3. Wireless Sensor Networks A collection of small hardware devices that collect data from their environment Research challenges  Energy efficiency  Data collection  Communications overhead

4. Wireless Sensor Networks Common application: Environmental monitoring  Example: Controlled prairie burning Sensors can report major temperature changes The spread of a fire can be monitored

5. Research Objective Long-term: Use fuzzy query and database management approach to data collection This summer: Modify the operating system of prototype sensor motes to support an approximate (“fuzzy”) attribute (value ± margin)

6. Prototype Hardware: MICA Motes Prototype sensors developed by UC Berkeley to support sensor networking research Sensors: Light, temperature, barometric pressure, seismic, sound, magnetic, GPS, and others RF Communications TinyOS: “Lite” embedded OS TinyDB: “Lite” DBMS

7. Prototype Platform: TinyDB TinyDB is a sensor network data collection system Allows for polling of sensors through Structured Query Language (SQL)  The sensor network is therefore abstracted to resemble a relational database in its interface to the user TinyDB's SQL dialect is in a very stripped-down, “working proof of concept” form called TinySQL Benefits: Ease of use, eliminates the “API approach” sensor polling, can poll the whole network easily

8. Problem In the prairie fire scenario, we want to know where dramatic rises and falls in temperature occur TinySQL supports polling of a mote's temperature and network averaging However, it relies on central processing to identify local trends The result is unnecessary transmission of data from areas not undergoing a change

9. Proposals What if we could tell the network to only return results that were outside of ordinary trends?  Push data processing to the mote  Develop local “threshold” values based on long-term node measurements  Extend TinySQL to support fuzzy queries This allows us to ask the network, “Where is it hotter than usual? Where is it cooler?”

10. Development and Simulation Installed and customized TinyOS on a Linux platform Installed and evaluated six simulators  Selected PowerTOSSIM  Set up a simulation environment to evaluate the energy efficiency of queries Designed TinyFSQL’s syntax and methods of operation

11. Code Modifications Operating system additions  Utilized data storage at the mote level  Implemented mote routines to return data only if present values are outside the current range

12. Code Modifications Extended TinyDB  Added an attribute to TinySQL to interface with local mote trends  Implemented the “UPDATE” keyword to force changes of local averages  Added the “fuzzy equal” operator

13. Work in Progress Completion of TinyFSQL  Add a greater range of fuzzy operands to the TinyDB parser generator source Modification to the Java GUI to include user- friendly selection of fuzzy attributes Extensive tests using the PowerTOSSIM simulator  Compare the energy efficiency of TinyFSQL to TinySQL