1. Slide 1/14 Midsens ’09 – December 1, 2009 Lightweight Tracing For Wireless Sensor Networks Debugging Vinaitheerthan Sundaram*, Patrick Eugster, Xiangyu Zhang School of Electrical and Computer Engineering* Department of Computer Science Purdue University Work supported by: National Science Foundation

2. Slide 2/14 Midsens ’09 – December 1, 2009 Talk Outline Motivation and Problem Setup Lightweight Tracing Compression Techniques Example Traces Evaluation Related Work Summary and Future Work

3. Slide 3/14 Midsens ’09 – December 1, 2009 Introduction to WSNs Wireless Sensor Networks (WSNs) –Consists of many tiny, battery powered, wireless motes –Cooperatively monitors physical or environmental conditions such as temperatures, sound, vibration, pressure, etc. WSNs have many useful applications –Habitat monitoring, Military Surveillance, Volcano Monitoring, Glacier Monitoring However, WSNs have not been widely deployed in large-scale yet mainly because of poor reliability Developing reliable WSN applications is very challenging

4. Slide 4/14 Midsens ’09 – December 1, 2009 WSN Debugging Challenges Challenges in debugging and maintaining WSN applications are –inherent resource constraints –in-situ deployment in harsh environments –lack of run-time support tools Run-time debugging tools are especially required because –Unexpected interaction with the real-world environment lead to complex run-time faults such as race-conditions Replay Debugging is a powerful debugging technique for distributed systems[Geels-Usenix06] To support replay, we need a light-weight tracing mechanism

5. Slide 5/14 Midsens ’09 – December 1, 2009 Lightweight Tracing Framework Novel control flow tracing and encoding scheme –Enables replay debugging –Identifies repetitive computation, thus enabling high compression Lightweight –We show it satisfies the stringent resource constraints Trace Storage –Supports storing the trace in non-volatile Flash memory –Least recent trace is overwritten Trace Query –Support simple query mechanism that sends the trace data to the base station upon request

6. Slide 6/14 Midsens ’09 – December 1, 2009 Ball-Larus Algorithm With Example Label each control- flow path with an unique identifier Inserts increments only when there is a fork in control- flow graph (conditional or loops)

7. Slide 7/14 Midsens ’09 – December 1, 2009 Lightweight Tracing Instrumentation –Annotate procedure P with BL path count –Instrument procedure P to record start of P end of P and pathCount atomically Tracer –Assign unique identifier for procedure P’s every invocation procLabel = procID + sessionID –Record trace in local buffers –When a buffer is full, compress and store it in flash Compression –Simple, Pattern Replacement and Loop Compression

8. Slide 8/14 Midsens ’09 – December 1, 2009 Lightweight Tracing Compression Simple Compression –Combine start and end symbols of a procedure if the procedure was not interrupted Pattern Replacement –WSN applications repeats same sequence of procedures –Represent these sequences with special symbols –Can significantly reduce the trace size –Control-flow encoding helps us identify it Loop Compression –Since loops follow identical paths in many iterations, represent these with path id and repetition count

9. Slide 9/14 Midsens ’09 – December 1, 2009 Trace Example: Surge Application 64, 48, 5, 32, 5, 16, 5, 336, 5, 5, 0, 96, 80, 5, 5, 176, 160, 5, 133, 256, 5, 240, 224, 5, 208, 5, 192, 5, [21, 112, 5, 176, 160, 5, 133, 256, 5, 240, 224, 5, 208, 5, 192, 5], [21, 112, 5, 176, 160, 5, 133, 256, 5, 240, 224, 5, 208, 5, 192, 5], [21, 112, 5, 176, 160, 5, 133, 256, 5, 240, 224, 5, 208, 5, 192, 5], [21, 112, 5, 176, 160, 5, 133, 256, 5, 240, 224, 5, 208, 5, 192, 5], 21, 112, 5 Surge Application –a typical data-collection application in TinyOS that samples light sensor every 2 seconds and sends the sensed data to the base station using multihop routing layer First 20 seconds of trace 64, 48, 5, 32, 5, 16, 5, 336, 5, 5, 0, 96, 80, 5, 5, 176, 160, 5, 133, 256, 5, 240, 224, 5, 208, 5, 192, 5, [200], [200], [200], [200], 21, 112,5 21, 112, 5, 176, 160, 5, 133, 256, 5, 240, 224, 5, 208, 5, 192, 5 => 200

10. Slide 10/14 Midsens ’09 – December 1, 2009 Pretty-Printed Trace start end 0 start end 0 start end 0 start end 0 start end 0 [Continued on the right side] start end 0 end 8 start end 0 start end 0 start end 0 start end 0 end 1 start end 0 start Start up Trace in Surge Repeating Patterns in Surge

11. Slide 11/14 Midsens ’09 – December 1, 2009 Evaluation Case Study - CC1000 Radio Deadlock Runtime Overhead –Benchmark Programs: Blink – Simple application but highlights overhead Surge – Typical WSN application with period 2 seconds Oscilloscope – Timer fires every 125 ms and can generate a lot of trace –Metrics: Trace Size, Program Memory, Data Memory –Compression efficiency is measured using Space-Savings Ratio Space saving ratio = (Uncompressed – Compressed) / Uncompressed Our results indicate –Traces can be highly compressed as much as 91.74% –Trace size varies from 1KB/Hr to 50KB/Hr –Low memory footprint about 300B –Low program memory about (5KB)

12. Slide 12/14 Midsens ’09 – December 1, 2009 Evaluation : Results

13. Slide 13/14 Midsens ’09 – December 1, 2009 Related Work Automating Debugging –Tracing but no control-flow traces ONodeMD [Krunic et al., Mobisys ‘07], EnviroLog [Luo et al., Infocom ‘06], DustMiner [Khan et al., SenSys ‘08], Checkpointing [Osterlind et al., EWSN ‘09] –Focuses on node, link or network failures only Sympathy [Ramanathan et al., SenSys ‘05], PAD [Liu et al., Sensys ‘08] –Debugging interface or error detection but not diagnosis Declarative TracePoints [Cao et al. SenSys ‘08], HSEND [Herbert et al., TAAS ‘06] Visibility –Remote access and control but very expensive Clairvoyant [Yang et al. SenSys ‘08], Marionette [Whitehouse et al., IPSN ‘06] Program Analysis –Testing or program comprehension but not tracing [Lai et al., FSE ‘08], [Kothari et al. IPSN ‘08]

14. Slide 14/14 Midsens ’09 – December 1, 2009 Summary and Future Work We showed program tracing can be done efficiently and accurately in WSN through –Novel trace encoding and compression schemes We are exploring multiple aspects –Trace size reduction through Inter-procedural tracing Unrolling of loops LZW compression schemes –Multi-node Tracing –Query Support for portions of trace