ResTAG: Resilient Event Detection with TinyDB

1. ResTAG: Resilient Event Detection with TinyDB Angelika Herbold Western Washington University Thierry Lamarre ENSEIRB Systems Software Laboratory, OGI Advisor: Dr. Nirupama Bulusu

2. Outline • Part I: Intro to Wireless Sensor Networks • Overview • Part II: TinyAggregation and TinyDB • TinyAggregation • TinyDB • Part III: Resilient Event Detection • Resilient Event Detection • Our Implementation • Preliminary Results • Future Work

3. Part I: Wireless Sensor Networks • The Ideal: A robust, randomly deployed, self-organizing network of small embedded devices. • Each unit (“mote”) has a processor, sensor(s), radio, and limited memory • Operating System: TinyOS • Major issues: • Localization • Power constraints/Network lifetime • Fault tolerance/Security

4. WSN Applications • Princeton ZebraNet • Collar-mounted sensors monitor zebra movement in Kenya • The “Wireless Vineyard” • Sensors monitor temperature, moisture • Roger the dog collects the data

5. Mote Hardware • WeC mote (Berkeley) • September 1999 • Rene (Berkeley) • October 2000 • Mica2 (Berkeley/XBow) • February 2003 • 128kB program memory • 7.3728Mhz ATMEL CPU • 38.4 kBaud data transfer (radio)

6. Part II: TinyAggregation/TinyDB • TinyDB: A query processing system for extracting information from a network of TinyOS sensors • Query the network like a relational DB • SQL-style queries: • SELECT MIN(Temp) FROM sensors • Motivation: • Easy to use • Can easily construct complex queries

7. TinyDB • Embedded: nesC/TinyOS • PC: Java GUI or command window • Applications can use TinyDB API as well

8. Customizing TinyDB • Some support for user-defined aggregates (e.g. MAX, AVG) • Support for user-defined attributes • Creating a new aggregate: • Write/modify existing embedded code • Write Java code

9. TinyAGgregation • Aggregation protocol used in TinyDB • Madden, et al. “TAG: A Tiny AGgregation Service for Ad-Hoc Sensor Networks” • Motivations: • Radio transmission is power-hungry • Not all data needs to be sent to the sink • Ideas: • Fuse data as it moves from source to sink • Eliminate wasted radio transmission • Aggregate using a tree structure

10. SINK node Level 0 32 Level 1 Level 2 30 38 Level 3 35 40 Leaf nodes TinyAGgregation/TinyDB • SELECT MAX(temp) FROM sensors Result = 40 40 40 38 35 40

11. Part III: Resilient Event Detection • Problems: • Motes can be physically compromised • Sensor Networks can be intentionally compromised • Solutions: • Secure every node • Encryption and verification are expensive • May be overkill for some applications • Secure the whole network • High-level fault tolerance/resilience • What confidence do we have in an event report?

12. Previous Work • Corroborative Aggregation Protocol • Yuan et al. “Improving the Reliability of Event Reports in Wireless Sensor Networks” • Exploits redundancy in the network • When an event is reported: • Sensors that report an event send a p-packet • Nodes whose sensing areas overlap may dispute the event if they disagree • Sensors that dispute an event send an n-packet • Probability of a disagreeing node sending dispute: p = area of overlap / total sensing area • Confidence = p-packets / total packets

13. A B A A Level 0 SINK node Level 1 B D E Leaf nodes Corroborative Aggregation Protocol • Probability of dispute is B/A Confidence: 2/3 = 66% corroborate p-pkt p-pkt n-pkt Level 2 Total Sensing Area event report dispute

14. Our Work • Premise: TinyDB is a useful tool, but it offers no resilient event detection. • Can we implement resilient event detection using TinyDB? • Basic Ideas: • Implement resilient aggregate query types • Compute disputes only at aggregation points

15. Implementation & Experiments • What we’ve implemented: • Resilient Average (ResAvg): Returns weighted average and confidence index • Resilient Maximum (ResMax): Returns maximum and a confidence index • Experiments: Simulate a large network with varying percentage and type of failure nodes, examine the performance of the resilient queries. • Additional Tools: • TOSSIM simulator • Java application to automate testing

16. Methodology (ResMAX): • 100 non-sink nodes in a regular grid • Radio model: each node hears up to 12 of its neighbors perfectly • Non-failure nodes report 25 • 3 Failure modalities: • Correlated: • High: Faulty nodes report 50 • Low: Faulty nodes report 0 • Uncorrelated • SELECT ResMAX(TEST) FROM sensors • Record query results for 0%-50% failed nodes

17. Preliminary Results (ResMAX) As % faulty nodes increases… 1) False results are less likely to be detected 2) True results are more likely to be disputed

18. Future Work • Test on a real mote network • Add resilience support for the WHERE clause in TinyDB • Now: Does not send results up the tree if they don’t match the “WHERE” • We need all results to compute disputes • Other implementations of Resilient Event Detection • Basis of comparison

19. References and Links • Princeton ZebraNet (project site): http://www.princeton.edu/~mrm/zebranet.html • Wireless Vineyard (article): http://www.intel.com/labs/features/rs01031.htm • Crossbow Technology, Inc.: http://www.xbow.com/ • TinyOS Community Forum: http://www.tinyos.net/ • TinyDB: http://telegraph.cs.berkeley.edu/tinydb/ …. Questions?