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Nov 1-2 2005: Review Meeting ACCLIMATE
Instrumenting Wireless Sensor Networks for
Real-Time Surveillance
Songhwai Oh Advisor: Shankar Sastry
EECSUC Berkeley
Nov 1-2 2005: Review Meeting ACCLIMATE
Building Comfort,Smart Alarms
Great Duck Island
Elder Care
Fire Response
Factories
Wind ResponseOf Golden Gate Bridge
Vineyards
Redwoods
Instrumenting the world
Soil monitoring
Nov 1-2 2005: Review Meeting ACCLIMATE
Challenges = Research Opportunities
applications
service
network
system
architecture
data mgmt
Monitoring & Managing Spaces and Things
technology
MEMSsensing Power
Comm. uRobotsactuate
Miniature, low-power connections to the physical world
Proc
Store
Nov 1-2 2005: Review Meeting ACCLIMATE
• Limited capabilities of a sensor node– Limited supply of power– Short communication range– High transmission failure rates– High communication delay rates– Limited amount of memory and computational power
• Inaccuracy of sensors– Short sensing range– Low detection probabilities– High false detection probabilities
• Inaccuracy of sensor network localization
Challenges = Research Opportunities
mica2dot
mag ultrasound
acoustic
Nov 1-2 2005: Review Meeting ACCLIMATE
Tracking in Sensor Networks
• Representative application of sensor networks– Event detection– Communication– Sensor fusion and estimation– Sensor management– Decision making, etc.
• Applications– Surveillance and security– Search and rescue– Disaster and emergency response system– Pursuit evasion games – Inventory management – Spatio-temporal data collection – Visitor guidance and other location-based services
Nov 1-2 2005: Review Meeting ACCLIMATE
Multiple-Target Tracking (MTT) in Sensor Networks
• Model uncertainty– Unknown number of targets– Unknown target initiation and termination times
• Measurement noise and inconsistency– Noise, False alarms, Packet losses, Delays
• Data association problem• Real-time
– Timely outputs required for control applications (e.g., pursuit evasion games)
Nov 1-2 2005: Review Meeting ACCLIMATE
Yet Another Complication: Binary Sensors
• Why binary sensors?– Sensor output is too noisy to
correlate signal strength with range
– Simple detection code
– 1-bit to communicate
• Provides coarse measurements• Difficult to use them directly to
initiate, maintain and terminate tracks
• We use spatial correlation to fuse binary measurements into finer position measurements
• Needs an efficient fusion algorithm
Nov 1-2 2005: Review Meeting ACCLIMATE
Previous Work: Multiple-Target Tracking (MTT) in Sensor Networks
• Traditional – computationally intensive– [Chong et al. 1990] Distributed multitarget multisensor tracking
• Classification-based – multiple single-target tracking problems– [Li et al. 2002] Detection, classification and tracking of targets– [Shin et al. 2003] A distributed algorithm for managing multi-
target identities in wireless ad-hoc sensor networks– [Liu et al. 2004] Distributed state representation for tracking
problems in sensor networks
• Ad-hoc – not robust– [Liu et al. 2003] Distributed group management for track
initiation and maintenance in target localization applications
• No general algorithm suited to sensor networks
Nov 1-2 2005: Review Meeting ACCLIMATE
Outline
• Multiple-target tracking (MTT) algorithm– Multi-sensor fusion algorithm– Markov chain Monte Carlo data association
(MCMCDA)
• Results from the final experiment of the Network Embedded Systems Technology (NEST) project
Nov 1-2 2005: Review Meeting ACCLIMATE
Multi-Sensor Fusion: Threshold
Likelihood after threshold
• But it requires detections from all sensors to account false alarms
• Instead we compute the likelihood if there are at least nd detections
Likelihood
Nov 1-2 2005: Review Meeting ACCLIMATE
Multi-Sensor Fusion: Position Estimation
Black circle: position estimate
Nov 1-2 2005: Review Meeting ACCLIMATE
Solution Space of Data Association Problem
(a) Observations Y
(b) Example of a partition of Y
Nov 1-2 2005: Review Meeting ACCLIMATE
Markov Chain Monte Carlo (MCMC)
• A general method to generate samples from a complex distribution
• For some complex problems, MCMC is the only known general algorithm that finds a good approximate solution in polynomial time [Jerrum, Sinclair, 1996]
• Applications:– Complex probability distribution integration problems– Counting problems (#P-complete problems)– Combinatorial optimization problems
• Data association problem has a very complex probability distribution
Nov 1-2 2005: Review Meeting ACCLIMATE
MCMC Data Association (MCMCDA)*
• Start with some initial state 1 2
*[Oh, Russell, Sastry 2004]
Nov 1-2 2005: Review Meeting ACCLIMATE
MCMC Data Association (MCMCDA)
• Propose a new state ’ » q(n,’)
• q: £ 2 ! [0,1], proposal distribution q(n,’) = probability of proposing ’ when the chain is in n
proposen
’
• q(n,’) is determined by 8 moves:
Nov 1-2 2005: Review Meeting ACCLIMATE
MCMC Data Association (MCMCDA)
• If accepted,
• If not accepted,
n+1=’
n+1=n
• Accept the proposal with probability
() = P(|Y), Y = observations
Nov 1-2 2005: Review Meeting ACCLIMATE
Polynomial-Time Approximation to Joint Probabilistic Data Association*
*[Oh, Sastry 2005]
Nov 1-2 2005: Review Meeting ACCLIMATE
Overall Architecture
MTT
Fusion MCMCDA
Controller
Multi-agent coordination algorithm
• Minimize time to capture all evaders
• Robust Minimum Time Control (MTC)
Nov 1-2 2005: Review Meeting ACCLIMATE
Simulation: Multiple-Target Tracking & Pursuit Evasion Games in Sensor Networks
Nov 1-2 2005: Review Meeting ACCLIMATE
NEST Final Experiment: MTT Demo
• Goal– Track an unknown number of multiple targets
using a sensor network of binary sensors without classification information
– Coordinate multiple pursuers to chase and capture multiple evaders in minimum time using a sensor network • Done in simulation due to physical and time constraints
Nov 1-2 2005: Review Meeting ACCLIMATE
NEST Final Experiment: Sensor Node
Telos B mote•8MHz TI MSP430 microcontroller •RAM: 10kB; Flash: 48kB •Chipcon CC2420 Radio: 250kbps, 2.4GHz, IEEE 802.15.4 standard compliant
•Radio range of up to 125 meters
Trio Sensor Board•Features a microphone, a piezoelectric buzzer, x-y axis magnetometers, and four passive infrared (PIR) motion sensors
•Solar-power charging circuitry
Trio Node
Nov 1-2 2005: Review Meeting ACCLIMATE
NEST Final Experiment: System
• Software– TinyOS– Deluge
• Network reprogramming
– Drip and Drain (Routing Layer)
• Drip: disseminate commands• Drain: collect data
– DetectionEvent • Multi-moded event generator
– Multi-sensor fusion and multiple-target tracking algorithms
Nov 1-2 2005: Review Meeting ACCLIMATE
Current and Future Work
• Sensor networks– Robust distributed tracking algorithm – Robust tracking against malicious attacks– Performance analysis and metrics for sensor
networks
• Camera networks • Distributed multiple-target tracking and identity
management
Nov 1-2 2005: Review Meeting ACCLIMATE
Distributed multiple-target tracking and identity management*: an application of MCMCDA
*[Oh, Hwang, Roy, Sastry, 2005]
Nov 1-2 2005: Review Meeting ACCLIMATE
Summary
• Sensor networks– Individual sensor nodes are incapable and inaccurate – But the aggregation of spatially spread sensors can provide
accurate estimates using spatio-temporal correlation
• System-level approach– Multi-sensor fusion may provide incorrect and inconsistent
position reports – The inconsistency in position reports are fixed by the
MCMCDA tracking algorithm using temporal correlation – Adaptive control system