Sep 29, 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)

Sep 29, 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

Sep 29, Evolution : Extreme Scale (“ExScal”) Scenarios 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 ExScal Focus Areas: Applications, Lifetime, and Scale

Sep 29, 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

Sep 29, 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?

Sep 29, Our Answer 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

Sep 29, 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?

Sep 29, Quality vs. Lifetime : A Potential Energy Budget Crisis 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

Sep 29, 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 T DC = 1.07 draws  7% of power budget –Radio needed to forward event detections and meet latency

Sep 29, Quality vs. Lifetime : Sensor Operation Low (<< P budget ) Medium (< P budget ) High (  P budget ) Short (<< T event ) Duty-cycle or Always-on Duty-cycle Medium (< T event ) Duty-cycle or Always-on ?? Long (  T event ) Always-on?Unsuitable Power Consumption (with respect to budget) Startup Latency (with respect to event duration)

Sep 29, 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

Sep 29, Quality vs. Lifetime : Passive Infrared Sensor Quad PIR sensors –Power consumption: low –Startup latency: long –Operating mode: always-on –Sensor role: wakeup sensor

Sep 29, 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

Sep 29, Quality vs. Lifetime : Magnetic Sensor Magnetometer –Power consumption: high –Startup latency: medium (LPF) –Operating mode: triggered

Sep 29, Quality vs. Lifetime : Passive Vigilance Trigger network includes hardware wakeup, passive infrared, microphone, magnetic, fusion, and radio, arranged hierarchically Nodes: sensing, computing, and communicating processes Edges:  False Alarm Rate Energy Usage HighLow High Energy-Quality Hierarchy 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…

Sep 29, 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 H 0 & H 1 How to Optimize Energy-Quality? –Let x* = (x 1 *, x 2 *,..., x n *) be the n decision boundaries between H 0 & H 1. 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.

Sep 29, 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?

Sep 29, 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

Sep 29, Engineering Considerations: Wireless Retasking 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:

Sep 29, 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”

Sep 29, Engineering Considerations: Packaging

Sep 29, 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

Sep 29, 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

Sep 29, Future Work “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

Sep 29, Closing Thoughts 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 vs. 

Sep 29, Discussion