Tracking Mobile Sensor Nodes
in Wildlife
Francine Lalooses
Hengky Susanto
EE194-Professor Chang
Outline
 Type of sensors
 Tracking algorithm
 Applications of habitat monitoring
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ZebraNet
Great Duck Island
 Our approach
 Expectations
 References
Type of Sensors
 Border sensors
 Never sleep
 Senses all the time
 Non-border sensors
 Sleep unless sense
an event
 Can predict the next
location
 Synchronization with
time stamp
Search for sensors using normal beam
A Protocol for Tracking Mobile Targets using Sensor Networks, RPI
Tracking Algorithm: Characteristics
 No central point
 Use cluster-based architecture
 Sensor strength: Normal and high beam
 Assumptions:
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Randomly distributed sensors
Default to normal beam
Hibernation mode
Tracking Algorithm
CH
CHi+1
CHi+2
CH = cluster head
 Algorithm:
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CHi+1 receives TD from CH
CHi+1 stores TD in its own
local database
Based on TD, CHi+1 selects
3 sensors
CHi+1 formulates TD based
on input from its
subordinates
CHi+1 passes it to the CHi+2
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TD = target description
Energy conservation:
 Only border nodes are
awake
 Prefer to use normal beam
 TD only travels between
CH to base station
Failure: Lost ACKs between
cluster heads
1. Use high beam
2. Wakeup sensors in radius r
3. Extend search to (2N-3)r
Tracking Algorithm Predictions
 Use at least 3 sensors per target
 Poisson rate algorithm
 p = Probability of at least 3 sensors per target
 l = Node density; r = normal beam radius
 Probability that arbitrary point inside the sensor
network can be sensed simultaneously by at least 3
sensors with their normal beams should be close to 1
ZebraNet
 Track animals long term
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and over long distances
All nodes mobile
Light-weight energy
efficient
GPS enabled
Wireless sensor net
Peer-to-peer routing
and data storage
Plan for one year
autonomous operation
GPS chip and CPU
8 grams
Short-range radio
20 grams
Packet Modem
140 grams
Long-range radio
156 grams
Lithium-Ion batteries
226 grams
Solar cell array
540 grams
Total
1090 grams (2.4 lbs)
Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with ZebraNet, Princeton University
ZebraNet: Protocols for Data Aggregation
 Two peer-to-peer protocols were evaluated:
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Flooding: Every 3 minutes, zebras send to
everyone discovered in their range.
History-Based: Every 3 minutes, choose at
most one peer to send within range; the one
with best past history success rate of
delivering data is chosen.
 Compared to “direct”: no peer-to-peer, just to
base.
ZebraNet: Mobility & Protocol Summary
 Radio range key to data homing success:
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~3-4km for 50 collars in 20kmx20km area
 Success rate:
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Ideal: flooding best
Constrained bandwidth: history best
 Energy trends make selective protocols best
ZebraNet: Discovered Lifestyles
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Mostly: herbivores graze
Sometimes: graze-walk while looking for greener pastures.
Rare: To run to or away from something
Water
 Thirsty once a day
 Model at random time
 Walk to nearest water
 After drink, resume ambient motion
Great Duck Island (GDI)
On Maine's Great Duck Island, biologists put
sensor devices in the underground nests (1)
and on 4-inch stilts placed just outside their
burrows (2). These devices record data about
the birds and relay it to a gateway node (3),
which transmits the information to a laptop in
the research station (4), then to a satellite dish
(5) and, ultimately, to an Intel Research lab at
Berkeley California.
Wireless Sensor Networks for Habitat Monitoring, Berkeley
GDI: Mica Hardware
 32 UC Berkeley motes
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deployed (9 underground)
Size:
 2 x 1.5 x 0.5 inches
Energy:
 Pair of AA batteries
 Monitoring runs for 9
months
Interchangeability:
 Less than 3% variation
when interchanged with
others of same model
Accuracy:
 Within 3% of the actual
value
Mica sensor node
Mica Weather Board
- temperature
- photo resistor
- barometric pressure
- humidity
- passive infrared sensors
GDI: System Architecture
 Tiered Architecture
 Lowest level: Small battery
Patch
Network
Sensor Node
Sensor Patch
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Gateway
Client Data Browsing
And Processing
TRANSIT
NETWOR
K
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Base Station
Base-Remote Link
INTERNE
T
Data Service
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powered sensor nodes
collect data
Gateway transmits sensor
data from patch
 Solar powered
 Always on
Base station provides WAN
and data storage
Base station connects to
database replicas across the
Internet
Finally, data is displayed
through user interface
GDI: Protocols & Discoveries
 Efficient routing for low duty cycle sensor network is
sending data to gateway on scheduled periods, one
direction communication
 Data collected from weather motes and burrow motes
 Temperature, relative humidity, solar radiation,
voltage utilization, live sensor readings
http://www.greatduckisland.net
Our Approach
 Improve the border layer energy conservation
 Better prediction algorithm
 Optimization of current failure/recovery
algorithm
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Propose layered sensor net architecture
Sensor node layers
Approach Overview
 Border layer energy conservation
 Issue: Only border nodes are awake
 Our approach to solve the problem
 Prediction Algorithm
 What is Prediction Algorithm?
 Challenges with prediction algorithm
 Failure and Recovery Algorithm
 Space decomposition
 Sweeping across region
 Challenges with both approaches
Expectations
 To conduct a study on one of the proposed
topics
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Improve the border layer energy conservation
Better prediction algorithm
Optimization of current failure/recovery algorithm
 To avoid working on NP-complete and
unsolvable problem in two months
References
 A Protocol for Tracking Mobile Targets using Sensor Networks.
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H. Yang and B. Sikdar. RPI, 2003.
Energy-Efficient Computing for Wildlife Tracking: Design
Tradeoffs and Early Experiences with ZebraNet. P. Juang, H.
Oki, Y. Wang, M. Martonosi, L. Peh, D. Rubenstein. Princeton
University, 2002.
Processing in a Tired Sensor Network for Habitat Monitoring. H.
Wang, D. Estrin, L. Girod, UCLA, 2002
Wireless Sensor Networks for Habitat Monitoring. A. Mainwaring,
J. Polastre, R. Szewczyk, D. Culler, J. Anderson. Berkeley, 2002.
Computational Geometry. M. De Berg, M. van Kreveld, M.
Overmars, O. Schwarzkopf. Utrecht University, 1999.
Wireless Sensor Networks. F. Zhao, L. Guibass. Microsoft and
Stanford University, 2003.
Networking Issues in Wireless Sensor Networks. D. Ganesan, A.
Cerpa, W. Ye, Y. Yu, J. Zhao, D. Estrin. UCLA, 2003.
Questions
Backup Slides
Border Layer Energy Conservation
 Issue: Only border nodes are awake
 Propose study:
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Divide outer layer into several layers
Each layer takes turn to watch
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Each layer shares the border watch task
The impact of multiple layers border watch
How to determine which sensors belong to
which layer in the cluster implementation
Prediction Algorithm
 Is it necessary to implement a prediction
algorithm? Yes
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To conserve energy and allow only the
relevant sensors to wake up
Tracking the object effectively
Better network performance and data mining
strategy
Issues in Prediction Algorithm
 How many sensors should be involved?
 At least 3 sensors vs. the entire cluster
 The impact of landscape (river, cliffs, etc)
 Effectiveness tracking strategy vs. energy
conservation
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What is the trade off?
 History improves the correctness of prediction
algorithm. Issue:
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Requires more energy for computation
Requires storage space to store history
Aspects of Prediction Algorithm
 Speed and velocity affects the direction of the objects
Very Fast: Likely to go in a straight line
 Fast: More likely to turn
 Medium and slow: High probability to turn and potential error
in predicting the next location
 Stop
 When the target is not moving or stopped
 How many sensors are needed?
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Single object
Multiple objects in the area
How to conserve energy?
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Taking turns to monitor target
Tell everybody on the routing path to sleep vs. keeping
everyone in the ready position
Aspects of Prediction Algorithm
 When the target is not moving or stopped
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When to cancel reservation if QoS is provided
If no QoS, who is responsible for storing data
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The cluster head vs. each sensor
The impact of storing data at cluster head and individual sensor
 Tracking multiple targets in the same location (e.g tracking a
dog and zebra in the same location)
 How to predict their next location (assuming they are going
to a different location)
 How multiple targets impact prediction algorithm
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Higher error tracking the target
One object might cause the other object to choose different
direction
Number of sensors needed if they are idle
Failure and Recovery Algorithm:
Approaches to Find Lost Target
 Space decomposition
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It is quicker to find the lost
target
It takes O(log n) running
time for a successful search
It guarantees to find the lost
target if the target is still in
the region
Awake every nodes in the
region
Not energy efficient
Costly
Creates traffic in network
 Sweeping across the region
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Sweeping outward from the
last seen position to the
border node
Only notifies their neighbor
at the outer layer
Perform a short overlap
layer search for fault
tolerance
When successful:
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Broadcast to stop the
search
The founder node takes
over the tracking
When target is not found,
border sensors report to
base station
Failure and Recovery Algorithm Issue
 Problem in sweeping across the region
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Running time is O(n) for a successful search
Target might be able to trick the algorithm
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While search is performed, the target might leave
monitored area
 Waste of searching effort
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Target might move faster than the sweep
High chance of flooding the network
High probability of waking the entire sensors
Failure and Recovery Algorithm
 Propose study:
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Combining Sweep and Space Decomposition
search
Algorithm under investigation
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Plane sweep algorithm
Topology sweep algorithm
Convex hull 3-dimension
Searching for more suitable algorithm