Transcript Document 7246548

Tracking Mobile Sensor Nodes
in Wildlife
Francine Lalooses
Hengky Susanto
EE194-Professor Chang
Outline
 Recap
 Tracking Algorithms
 Failure and Recovery Algorithms
 Future Work
 References
Recap
 Purpose of tracking
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For wild life habitat watch purpose
Sensors only monitor land targets (animals)
Animals are tagged
Sensors were purposely placed at certain
location
Save time finding target in the region
Better understanding of region/animal
relationship
Cow Tracking
Is it just me........
or does anyone else find it absolutely amazing that the
U.S. government can track a cow born in Canada almost three years ago,
right to the stall where she sleeps in the state of Washington,
and determine exactly what that cow ate.
They can also track her calves right to their stalls,
and tell you what kind of feed they ate.
But they are unable to locate 11 million illegal aliens
wandering around in their country, including people
that are trying to blow up important structures in the U.S.
My solution is to give every illegal alien a cow
as soon as they enter the country.
E-mail, 2004
Tracking Algorithms
 Factors affecting tracking performance:
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Sensor range
Average speed of target
 Algorithms:
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Naïve Activation
Randomized Activation
Selective Activation based on Prediction
Low Duty Cycle Operation
Naïve Activation
 All nodes are in tracking mode all the time
 Worst energy efficiency
 Best possible quality of tracking
Energy-Quality Tradeoffs for Target Tracking in Wireless Sensor Networks, USC.
Randomized Activation
 Each node is on with probability p
Selective Activation Based on Prediction
 Small subset of nodes are in tracking mode
 Nodes predict “next” position of target
 Rest of nodes in communication mode
Low Duty Cycle Operation: Frisbee Model
 Entire sensor network turns off and on
 Low power operating mode with wakeup
 Power-saving mode
 “Wakeup wavefront”
 Sensors must use localized algorithms
 Fully distributed, decentralized design
 Each node autonomously decides whether it
lies in Frisbee
 Decentralized decision = which exact node
should wake up
Frisbee Model
Failure and Recovery Recap
 Hierarchy cluster based sensor networks
management
 Only Cluster Head (CH) communicates with
other CH
 CH will wake up the next CH in target’s path
 Failure occurs when lost ACKs between
cluster heads
Failure and Recovery Algorithms
 Failure and recovery factors:
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Broken link
Power consumed
CH dies
 Algorithms:
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Retry sending
Space decomposition (hierarchical clustering)
Sweeping across the region
Search by region
Space Decomposition
 Quicker to find the lost target
 Takes O(log n) running time for a successful search
 Guaranteed to find lost target if the target is still in
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the region
Awake every node in the region
Not energy efficient
Costly
Creates network traffic
Sweeping Across the Region
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Sweeping outward from last seen position to border node
Perform a short overlap layer search for fault tolerance
Only notifies their neighbor at outer layer
When successful, the founder takes over target
When target is not found, border sensors report to base
station
 Awake all nodes in region and flood network
 Running time is O(n)
Example of sweeping:
Sensor node layers
Search by Region
 Hierarchy cluster / tree environment
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CH and its subordinates
Cluster border nodes
 CH makes decision based on input from its
subordinates
 CH knows radius of its cluster
 Cluster size is proportional to other clusters
 Drawback:
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Constantly changing topology
Search by Region Approach
 Extending search to larger region
 Uses hypothesis or cluster border nodes
 Advantages:
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Minimizes the number of clusters involved
Reduces network traffic
Allows multilevel monitoring (hierarchy cluster
based)
Cluster Border Nodes Summary
 Nodes at the edges of clusters
 Alternative approach to predicting the next target’s
location
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Avoid fault prediction
Helps CH’s decision of contacting the next proper CH
 All nodes ask their neighbor’s CH ID
 All nodes whose neighbors have different CH ID
declare to be border node
 Border nodes report to its CH with new status and
neighbor CH ID
Search by Region: Cluster Border
 Dark area: where the target
is lost
 Algorithm:
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CH uses cluster border to
determent the next
location
CH of target’s last
position broadcast to all
neighbors
Only CHs attached to
dark area wake up and
continue broadcast
Otherwise ignore the
alert and sleep
 Drawback:
Difficult to predict
animal’s behavior without
prior knowledge
 Difficult to determent if
areas are covered
properly
 How to determine if
target is still in dark
area?
 Proposed solution:
 Tagging target and
retrieving clue from tag
 Use two E[X] to
determine whether or not
target is still in dark area
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Search by Region: Hypothesis
 Take advantage of hierarchy cluster structure
 Each CH counts average visit per day by any
target (e.g. animals)
 High rank CH queries clues from its
subordinates
 Create a prediction based on hypothesis
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Find popular place to roam
 Predict a trace from predicted destination to
last known location of animal
Search by Region: Foreseeable Issues
 How to bound the search area
 What is the probability of:
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Target electing to visit non-hypothesized
destination
Target taking different path to predicted
destination
 Multiple candidates of popular destinations
Future Work
 Tracking algorithm
 Compare current tracking algorithms
 Implement better algorithm
 Failure and recovery algorithm
 Optimize current algorithm
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Solve problem at “Search by Region: Foreseeable
Issues”
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Propose new algorithm
Simulation
Performance analysis of the algorithms
References
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Energy-Quality Tradeoffs for Target Tracking in Wireless Sensor Networks. S. Pattem. USC,
2003.
Habitat Monitoring: Application Driver for Wireless Communications Technology. D. Estrin, J.
Zhao, et al. UCLA, 2000.
Next Century Challenges: Scalable Coordination in Sensor Networks. D. Estrin, et al. USC,
1999.
Sensing, Tracking, and Reasoning with Relations. L. Guibas. Stanford University, 2002.
Computational Geometry. M. De Berg, M. van Kreveld, M. Overmars, O. Schwarzkopf.
Utrecht University, 1999.
Minimizing Communication Cost in Hierarchically Clustered Networks of Wireless Sensors.
S. Bandyopadhyay, E.J. Coyle. Purdue University.
Efficient Location Tracking Using Sensor Networks. H.T. Kung, D.Vlah. Harvard University.
Distributed State Representation for Tracking Problems in Sensor Networks. J. Liu, M. Chu,
J. Liu, J. Reich, F. Zhou. Microsoft Corp, 2004.
Locating Moving Entities in Indoor Environments with Teams of Mobile Robots. M.
Rosencrantz, G. Gordon, S. Thurn. Carnegie Mellon University, 2003.
Lightweight Sensing and Communication Protocols for Target Enumeration and
Aggregation. Q. Fang, F. Zhao, L. Guibas. Stanford University, 2003.
Questions
Backup Slides
Sweeping Across the Region Problem
 Running time is O(n) for a successful search
 The target might be able to fool the algorithm
 The target might leave the monitored area and
return while search is performed and waste of
searching effort
 The target might moves faster than the sweep
because the network traffic might slowdown
sweep
 High chance of flooding the network
 High probability of awake the entire sensors