Presented at LTER ASM 03, 9/20/2003
Intelligent Sensor Network
Signal and Information
Processing For LTER
Applications
Yu Hen Hu
University of Wisconsin – Madison
Dept. Electrical and Computer Engineering
Madison, WI 53706
hu@engr.wisc.edu
© 2003 by Yu Hen Hu
Outline
• Long term environmental monitoring requirements
• Intelligent signal and information processing (ISIP)
– Intelligent agent and ISIP
– Needs for intelligence
• Enhance performance
• Reduce cost
• Work in progress
– Detection of changes
– Clustering of events
– Sampling frequency determination
• Future works
– Intelligent monitoring
– Intelligent maintenance
– Intelligent data analysis
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© 2003 by Yu Hen Hu
Challenges in Long Term
Environmental Monitor
• Objectives and hypothesis
– Specific biological,
environmental phenomena to
be observed and analyzed
– Formulate hypothesis based
on existing data
• Signal and information
processing
– On-line, real time control
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Turn on/off sensor
Adjust sampling frequency
Data routing/collecting
Streaming of video/image
on-demand
• Calibration and selfmonitoring
• Experiment design
– Location, duration of
observation,
– likelihood of onset of events
of interests
– Instrumentation
• Types of sensors,
• Deployment plan
• Maintenance plan
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Off-line
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data archival,
retrieval
Analysis, visualization
inference
– Data archiving plan
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© 2003 by Yu Hen Hu
Intelligent Agent
– Intelligent agent are persistent software/hardware
systems that perceive, reason, act, and
communicate on behalf of human users
Sensor
Environment
knowledge
action
Agent
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© 2003 by Yu Hen Hu
Characteristics of an IA
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Autonomous execution
Goal seaking
Persistent within or as a part of a system
Able to reason during action selection
Acting for another with authority granted by
another
• Interact with other agents or human via
dialog or some agent communication
language
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© 2003 by Yu Hen Hu
ISIP: Intelligent Signal and
Information Processing
• Signal processing
– The sampling, conditioning, compression, transmission, and
analysis of numerical measurements of the environment based
on sensor readings
• Information processing
– The handling of non-numerical data to coordinate collaboration,
and control operations
• ISIP
– Perform signal and information tasks using intelligent agent
– Tasks:
• statistical and heuristic reasoning, including hypothesis testing,
classification, estimation, data fusion, etc.
– Tools:
• neural network, expert system, fuzzy logic, genetic algorithm,
pattern classifiers, time series analysis, statistical learning, support
vector machine, Bayesian network, planning, etc.
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© 2003 by Yu Hen Hu
Wisconsin Long-Term Ecological
Research (LTER) project
Three Buoy Projects
1) Sparkling raft Serial
communication Fixed buoy
location Simple data format
that must conform to
historical format Wireless
takes the place of serial
cable, but download timing
automated
2) 3 small roving buoys Serial
communication Flexible
buoy locations Complex
data format with intensive
post-processing Wireless
simply takes the place of a
serial cable
3) Large profiling buoy
Bidirectional ethernet
communication Fixed buoy
location Simple but flexible
data format Real-time data
with web publishing and
buoy control
http://www.limnology.wisc.edu/
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© 2003 by Yu Hen Hu
Wisconsin Limnology Lab
Buoy Wireless Sensor Network
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© 2003 by Yu Hen Hu
How Buoy Data Are Processed?
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© 2003 by Yu Hen Hu
What a Buoy Will Do?
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© 2003 by Yu Hen Hu
Water Temperature Data
temperature profile
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water temperature
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© 2003 by Yu Hen Hu
A Change Detection Problem
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© 2003 by Yu Hen Hu
Sensor Node Signal Processing
original time series and trend
• Requirements:
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– Simple algorithm
– Robust performance
– Low power operation
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• Trend removal
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FIR filter
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– Simple statistical
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• Outlier detection
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© 2003 by Yu Hen Hu
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© 2003 by Yu Hen Hu
Enabling Technologies
• Enabling information technologies
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Internet networking and ad hoc mobile network
Wireless communication
Micro-electronic mechanical (MEM) devices
System-on-chip technology integrating
• Analog + digital
• Sensing + wireless communication + processing +
actuation
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© 2003 by Yu Hen Hu
Technical Challenges
• Self-configuration,
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Self-organization
Self maintenance
Services discovery
Directory services
• Collaborative sensor signal
processing
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Sampling
Encoding, compression
aggregation
Event detection
Target identification, situation
awareness
– Tracking
• Secure operation
– Privacy protection
• Ensure sensor
information is accessed
only by authorized
personnel
– Fault tolerance, high
availability
– Safety
• Non-intrusive,
• Safe actuation
– Sabotage resistance,
security
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© 2003 by Yu Hen Hu
Self-Configuration
• Purpose
– Reduce deployment and
configuration cost
• Vision
– Sensors are deployed
randomly (ad hoc network)
to reach a desired local
density
– After deployment, sensors
periodically communicate to
each other to establish and
maintain a connected
network.
– Directory (configuration
information) will be
aggregated, and published
to authorized agent.
– Sensors monitor network
status and periodically
report to an external
monitoring agent
– Sensor network re-organize
itself in case
• Its mission is changed as
directed from an
authorized external agent
• Traffic/load changed over
time
• Sensors’ physical position
changed if they are mobile
• Part of the network
malfunction due to sensor
failure, or communication
failure
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© 2003 by Yu Hen Hu
Self Configuration
• How to join a physical network; that is, how is it
authorized and given a network address and a
network identity?
• Once an entity is on the network and wishes to
provide a service to other entities on the network,
how does it indicate that willingness?
• If an entity is looking for a service on the network,
how does it go about finding that service?
• How does geographic location affect the services
an entity can discover or select for use?
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© 2003 by Yu Hen Hu
Collaboration
• Sensors collaborate to achieve network-wide
processing objective:
– Higher performance
– Lower resource (energy, band-width) consumption
• Challenge: How to achieve globally optimal results
– Using local, distributed criteria
– With local communication
– Using minimum amount of energy
• Approach
– Exploring redundancy and correlation
• Densely deployed sensor field
• Sensor readings are correlated
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© 2003 by Yu Hen Hu
Collaborative Sensor Signal Processing
• Sampling
– How to use lowest
sampling rate/density to
achieve desired spatialtemporal accuracy?
• Compression
– How to reduce overall
sampled data that needs
to be transmitted over
wireless channel?
• Aggregation
– How to summarize
information from multiple
sensor without overload
wireless channel
• Event detection
– How to deploy sensors so
that a desired event can
be detected with a
specified accuracy?
• Target identification,
situation awareness
– How to classify targets
when there are multiple
targets present
• Tracking
– How to coordinate sensor
activities to track multiple
target effectively.
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© 2003 by Yu Hen Hu
Sampling
• Temporal resolution
– How many samples per
unit time?
– Reduce rate to conserve
energy, band-width
– Based on underlying
physics
– Nyquist theorem for bandlimited signals
– Adaptive sampling rate for
non-stationary signals
• Spatial resolution
– For visual signals
– How small the frame size
can still allow
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Target detection
Subject identification
Tracking
Other monitoring
functions
– Adaptive spatial resolution
• Higher resolution in
region of interests
• Spatial-temporal sampling
– Different camera/sensors
coordinate to sample at
lower rate while achieving
higher resolution than a
single camera
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© 2003 by Yu Hen Hu
Compression
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Sensors closer to each other
physically, may sample similar
(correlated) data
It waste energy and bandwidth to transmit data uncompressed.
Exploiting the correlation of
sensor data among adjacent
sensors, amount of data
transmitted can be further
reduced.
Example: compression of
multiple video streams taken
from neighboring cameras
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If sensor A reading = x, then
sensor B reading = x  2, and
vice versa
Suppose both readings have
range [0, 127]
If reading A = 25, then sensor
A knows sensor B’s reading in
[23, 27].
If sensor B sends its reading
in 7 bits, sensor A knows the
receiving end must know
reading A in [21, 29]
Needs only 3-bits to encode!
D. Slepian, and J. K. Wolf, “Noiseless coding of correlated information sources,”
IEEE Trans. Information Theory, vol. 19, 1973, pp. 471-480
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© 2003 by Yu Hen Hu
Aggregation
• Problem Statement
– Find an estimate of a
statistic of sensor
measurements of a
group of sensors with
minimum amount of
wireless transmission
• Assume
– Sensors can overhear
neighboring sensor’s
transmission
– Sensor readings are
correlated
• Example:
– Find maximum reading
among N sensors
• One possible protocol:
– Sensor with higher reading
report first.
– Sensors with readings larger
than reported readings will
report with collision control
– Sensors whose readings
smaller than reported
readings remain silent.
– Wait for a pre-specified time
period. The last reported
reading is the maximum with
high probability
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© 2003 by Yu Hen Hu
Event/Target Detection
• Statistical hypothesis
testing tasks
• Collaborative detection
– Correctly detect an event
or a target while
minimizing cost (energy
and band-width)
– Individual sensors may
not detect correctly due to
• Limited range,
• Limited scope
• noise
• Methods of collaborative
detection
– Decision fusion
– Multi-modality detection
• Low cost sensor detect
first with higher false
alarm rate
• High cost sensor (visual
sensors) to verify
detection results
• Challenges
– Not all sensors report
detection
– Not all reported detection
will be forwarded to fusion
center
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© 2003 by Yu Hen Hu
Target classification/ Event awareness
• Pattern classification
problems
– Low power feature
extraction
– Decision fusion
• Feature extraction
– Invariant to variations
– Cheap to compute
– Local to each sensor
• Decision fusion
– Only discrete set of
decisions needs to be
transmitted over wireless
channel
• Event awareness
– Detecting a particular
event such as traffic
accident
– Require understanding of
a sequence of states
using hidden Markov
model
– Requires detection of
onset and offset of an
event
– Requires tracking of
objects of interests
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© 2003 by Yu Hen Hu
Conclusion
• Sensor network is a new application area
for computer vision, graphics and image
processing
• It requires multi-modality, multimedia
processing under the constraint of
minimizing communication and energy
consumption.
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