Design of a Wireless Sensor Network Platform for Detecting Rare, Random, and Ephemeral Events

1. Design of a Wireless Sensor Network Platform for Detecting Rare, Random, and Ephemeral Events Prabal Dutta with Mike Grimmer (Crossbow), Anish Arora, Steven Bibyk (Ohio State) and David Culler (U.C. Berkeley)

2. Origins : “A Line in the Sand” Put tripwires anywhere – in deserts, or other areas where physical terrain does not constrain troop or vehicle movement – to detect, classify, and track intruders

3. Evolution : Extreme Scale (“ExScal”) Scenarios ExScal Focus Areas: Applications, Lifetime, and Scale • Border Control • Detect border crossing • Classify target types and counts • Convoy Protection • Detect roadside movement • Classify behavior as anomalous • Track dismount movements off-road • Pipeline Protection • Detect trespassing • Classify target types and counts • Track movement in restricted area

4. Common Themes • Protect long, linear structures • Event detection and classification • Passage of civilians, soldiers, vehicles • Parameter changes in ambient signals • Spectra ranging from 1Hz to 5kHz • Rare • Nominally 10 events/day • Implies most of the time spent monitoring noise • Random • Poisson arrivals • Implies “continuous” sensing needed since event arrivals are unpredictable • Ephemeral • Duration 1 to 10 seconds • Implies continuous sensing or short sleep times • Robust detection and classification requires high sampling rate

5. The Central Question How does one engineer a wireless sensor network platform to reliably detect and classify, and quickly report, rare, random, and ephemeral events in a large-scale, long-lived, and wirelessly-retaskable manner?

6. The eXtreme Scale Mote Platform ATmega128L MCU (Mica2) Chipcon CC1000 radio Sensors Quad passive infrared (PIR) Microphone Magnetometer Temperature Photocell Wakeup PIR Microphone Grenade Timer Recovery Integrated Design XSM Users OSU, Berkeley, MIT, UIUC, UVa, Vanderbilit MITRE/NGC/Kestrel/SRI Others (now sold by Xbow) Why this mix? Easy classification: Noise = PIR  MAG  MIC Civilian = PIR  MAG  MIC Soldier = PIR  MAG  MIC Vehicle = PIR  MAG  MIC Our Answer

7. The Central Question : Quality vs. Lifetime How does one engineer a wireless sensor network platform to reliably detect and classify, and quickly report, rare, random, and ephemeral events in a large-scale, long-lived, and wirelessly-retaskable manner?

8. Quality High detection rate Low false alarm rate Low reporting latency Lifetime 1,000 hours Continuous operation Limited energy Two ‘AA’ batteries < 6WHr capacity Average power < 6mW A potential budget crisis Processor 400% (24mW) Radio 400% (24mW on RX) 800% (48mW on TX) 6.8% (411W on LPL) Passive Infrared 15% (880W) Acoustic 29% (1.73mW) Magnetic 323% (19.4mW) Always-on requires ~1200% of budget Quality vs. Lifetime : A Potential Energy Budget Crisis

9. Quality vs. Lifetime : Duty-Cycling Processor and radio • Has received much attention in the literature • Processor: duty-cycling possible across the board • Radio: LPL with TDC = 1.07 draws  7% of power budget • Radio needed to forward event detections and meet latency

10. Quality vs. Lifetime : Sensor Operation Power Consumption (with respect to budget) Startup Latency (with respect to event duration)

11. Quality vs. Lifetime : Sensor Selection Key Goals: low power density, simple discrimination, high SNR 2,200 x difference! Power density may be a more important metric than current consumption

12. Quality vs. Lifetime : Passive Infrared Sensor • Quad PIR sensors • Power consumption: low • Startup latency: long • Operating mode: always-on • Sensor role: wakeup sensor

13. Quality vs. Lifetime : Acoustic Sensor • Single microphone • Power consumption: medium (high with FFT) • Startup latency: short (but noise estimation is long) • Operating mode: duty-cycled “snippets” or triggered

14. Quality vs. Lifetime : Magnetic Sensor • Magnetometer • Power consumption: high • Startup latency: medium (LPF) • Operating mode: triggered

15. Quality vs. Lifetime : Passive Vigilance Energy-Quality Hierarchy Low High Multi-modal, reasonably low-power sensors that are Duty-cycled, whenever possible, and arranged in an Energy-Quality hierarchy with low (E, Q) sensors Triggering higher (E, Q) sensors, and so on… False Alarm Rate Energy Usage • Trigger network includes hardware wakeup, passive infrared, microphone, magnetic, fusion, and radio, arranged hierarchically • Nodes: sensing, computing, and communicating processes • Edges: < E,  PFA>  <  E,  PFA> High Low

16. Quality vs. Lifetime : Energy Consumption • How to Estimate Energy Consumption? • Power = idle power + energy/event x events/time • Estimate event rate probabilistically: p(tx) = from ROC curve and decision threshold for H0 & H1 • How to Optimize Energy-Quality? • Let x* = (x1*, x2*,..., xn*) be the n decision boundaries between H0 & H1. for n processes. Then, given a set of ROC curves, optimizing for energy-quality is a matter of minimizing the function f(x*) = E[power(x*)] subject to the power, probability of detection, and probability of false alarm constraints of the system.

17. The Central Question : Engineering Considerations How does one engineer a wireless sensor network platform to reliably detect and classify, and quickly report, rare, random, and ephemeral events in a large-scale, long-lived, and wirelessly-retaskable manner?

18. Engineering Considerations: Wireless Retasking • Wireless multi-hop programming is extremely useful, especially for research • But what happens if the program image is bad? No protection for most MCUs! • Manually reprogramming 10,000 nodes is impossible! • Current approaches provide robust dissemination but no mechanism for recovering from Byzantine programs

19. No hardware protection Basic idea presented by Stajano and Anderson Once started You can’t turn it off You can only speed it up Our implementation: Engineering Considerations: Wireless Retasking

20. Engineering Considerations: Logistics • Large scale = 10,000 nodes! • Ensure fast and efficient human-in-the-loop ops • Highly-integrated node • Easy handling (and lower cost) • Visual orientation cues • Fast orientation • One-touch operation • Fast activation • One-listen verification • Fast verification • Some observations • One-glance verification • Distracting, inconsistent, time-consuming • Telescoping antenna • “Accidental handle”

21. Engineering Considerations: Packaging

22. Evaluation • Over 10,000 XSM nodes shipped • 983 node deployment at Florida AFB • Nodes • Survived the elements • Successfully reprogrammed wirelessly • Reset every day by the grenade timer • Put into low-power listen at night for operational reasons • Passive vigilance was not used • PIR false alarm rate higher than expected • 1 FA/10 minutes/node • Poor discrimination between person and shrubs

23. Conclusions • Passive vigilance architecture • Energy-quality tradeoff • Beyond simple duty-cycling • Extend lifetime significantly (72x compared to always-on) • Optimize energy, quality, or latency • Scaling Considerations • Wirelessly-retaskable • Highly-integrated system • One-touch • One-listen • DARPA classified the project effective 1/31/05 • Crossbow commercialized XSM (MSP410) on 3/8/05

24. “Perpetual” Deployment Evaluate year-long deployment 1,000 node sensor network Areas surrounding Berkeley Trio Mote Telos platform XSM sensor suite Grenade timer system Prometheus power system Future Work

25. Data Collection Phenomena Omni-chronic Signal Reconstruction Reconstruction Fidelity Data-centric Data-driven Messaging Periodic Sampling High-latency Acceptable Periodic Traffic Store & Forward Messaging Aggregation Absolute Global Time Event Detection Rare, Random, Ephemeral Signal Detection Detection and False Alarm Rates Meta-data Centric (e.g. statistics) Decision-driven Messaging Continuous “Passive Vigilance” Low-latency Required Bursty Traffic Real-time Messaging Fusion, Classification Relative Local Time Closing Thoughts vs.            

26. Discussion