Sep 29, 2005 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)
Feb 24, 2016
Sep 29, 2005 1
Design of a Wireless Sensor Network Platformfor 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, 2005 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
Sep 29, 2005 3
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, 2005 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
Sep 29, 2005 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?
Sep 29, 2005 6
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, 2005 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?
Sep 29, 2005 8
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% (411W on LPL)
– Passive Infrared• 15% (880W)
– Acoustic• 29% (1.73mW)
– Magnetic• 323% (19.4mW)
• Always-on requires ~1200% of budget
Sep 29, 2005 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
Sep 29, 2005 10
Quality vs. Lifetime : Sensor Operation
Low(<< Pbudget)
Medium(< Pbudget)
High( Pbudget)
Short(<< Tevent)
Duty-cycleor
Always-onDuty-cycle Duty-cycle
Medium(< Tevent)
Duty-cycleor
Always-on? ?
Long( Tevent) Always-on ? Unsuitable
Power Consumption(with respect to budget)
Star
tup
Late
ncy
(with
resp
ect t
o ev
ent d
urat
ion)
Sep 29, 2005 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
Sep 29, 2005 12
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, 2005 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
Sep 29, 2005 14
Quality vs. Lifetime : Magnetic Sensor
• Magnetometer– Power consumption: high– Startup latency: medium (LPF)– Operating mode: triggered
Sep 29, 2005 15
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: < E, PFA> < E, PFA>
FalseAlarmRate
EnergyUsage
HighLow
LowHigh
Energy-Quality Hierarchy
Multi-modal, reasonably low-power sensors that areDuty-cycled, whenever possible, and arranged in anEnergy-Quality hierarchy with low (E, Q) sensorsTriggering higher (E, Q) sensors, and so on…
Sep 29, 2005 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.
Sep 29, 2005 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?
Sep 29, 2005 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
Sep 29, 2005 19
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, 2005 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”
Sep 29, 2005 21
Engineering Considerations: Packaging
Sep 29, 2005 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
Sep 29, 2005 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
Sep 29, 2005 24
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, 2005 25
Closing Thoughts
Data Collection
Phenomena Omni-chronic Signal Reconstruction
Reconstruction FidelityData-centric
Data-driven MessagingPeriodic Sampling
High-latency AcceptablePeriodic Traffic
Store & Forward MessagingAggregation
Absolute Global Time
Event Detection
Rare, Random, EphemeralSignal DetectionDetection and False Alarm RatesMeta-data Centric (e.g. statistics)Decision-driven MessagingContinuous “Passive Vigilance”Low-latency RequiredBursty TrafficReal-time MessagingFusion, ClassificationRelative Local Time
vs.
Sep 29, 2005 26
Discussion