Download slides - University of Houston

OCO
- a method for target tracking in wireless sensor
networks
T. Andrew Yang ([email protected])
with Sam Tran
Computer Science Program
University of Houston – Clear Lake
Houston, Texas
6-7-2006
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Outline
•
•
•
•
•
Introduction
The Methods
Simulation-based Evaluation
Evaluation Results
Conclusion and Future Work
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Acknowledgement
• This work is supported in part by
National Science Foundation (Grant DUE-0311592)
Institute of Space Systems Operations (ISSO)
UHCL Faculty Research and Support Fund (No. 859)
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Introduction
• wireless sensor networks (WSN)
- a network of wireless sensor nodes.
- Each node is a computer with attached sensors (aka
mote) that can process, exchange sensing data, as
well as communicate wirelessly among themselves to
perform various tasks.
• WSN have many applications in both military and
civilian systems.
• Sample applications:
data collection, surveillance, object tracking, etc.
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WSN applications
An example
of wireless
sensor
network for
data
collection
in
agriculture
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WSN applications
A
scenario
of enemy
tracking
using
sensor
networks
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The Challenges
Small size of sensor node
 Limited battery capacity and lower hardware performance
Overlapping sensing areas (redundancy)
 The network may be formed by randomly throwing thousands
or even millions of sensor nodes in an area.
Frequent change of network topology
 The network is usually installed in a large area with many
physical effects, such as earthquake, explosion, etc.
Limited bandwidth of wireless links + interferences in the
environment
 Problems such as dropped packets and disconnected links
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Requirements
•
Accuracy of target detection
– The primary goal is to ensure consistent accuracy without
sacrificing the network’s longevity.
•
Efficient energy dissipation
– The goal is to increase the overall longevity of the WSN.
•
Effective computation
– Due to the limited processor and battery power of a sensor,
computation performed on the sensor must be effective, in order to
incur minimum energy dissipation.
•
Re-configurability
– When one or more of the sensors cease to function, the network
should be able to self-organize or re-configure itself, in order to reconstruct a functional WSN allowing the mission to continue to be
fulfilled.
•
Secure communications
– In the context of WSN security, prevention-based security features
such as authentication, data integrity, confidentiality, and availability
are needed.
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The Methods
1.
2.
3.
OCO: Optimized Communication & Organization
DC: Direct Communication
LEACH: Low Energy Adaptive Clustering Hierarchy [Heinzelman,
Chandrakasan, etc., 2000]
Direct Communication
– The sensor modules of all nodes are ON.
– Nodes send data directly to the base.
Advantages
• Gives the best accuracy.
Disadvantages
• Unrealistic because the base has limited
number of channels, and node energy is
limited.
• Cannot be applied to a large area.
• Suffer redundancy
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The Methods
•
Cluster-based method (e.g., LEACH)
– Build a hierarchy tree by using LEACH
algorithm:
• Nodes randomly self-elect to become cluster
heads.
• The cluster head invites its neighbors to join to the
group.
• Re-elect cluster heads after a period of time for
energy balancing.
– Nodes send data to the base through the cluster
heads.
– Cluster heads communicate to the base directly.
– Advantages
• Simple
– Disadvantages
• All nodes are supposed to communicate directly
with the base
 Cannot be applied to a large area
 suffer the channel limitation of the base
• Suffer redundancy.
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OCO
• 4 phases
- position collection, processing, tracking, and maintenance
– In the position collection phase, the base-station collects positions of
all reachable nodes in the network.
– In the processing phase, it applies image processing techniques to
clean up the redundant nodes, detect border nodes, and find the
shortest path from each node to the base.
– In the tracking phase, the sensors in the network all work together to
detect and track intruding objects.
– The maintenance phase involves re-organizing the network when, for
example, a change in the topology of the network occurs, or some of
the sensor nodes die (i.e., running out of power).
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OCO
- Position collection phase
•
•
Assumption: The sensor nodes are randomly
scattered in the geographical area.
The base station sends a message to its
neighbors to gather their IDs and positions, and
at the same time advertise its own ID as the
parent ID of the neighbor nodes.
•
The base’s neighbor nodes, after sending its ID
and position to its parent (the base), marks itself
as recognized, and then performs the same
actions as the base does by collecting IDs and
positions from their neighbors, and advertising
itself as the parent node, and so on.
•
When a node gets the position and ID
information from its neighbor, it forwards the
information to its parent.
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OCO
- Processing phase
•
3 steps:
1.
2.
3.
Clean up redundant nodes
Define the border nodes
Find the shortest path from each node to the base
Table 1: Algorithm for Determining Redundant Nodes
1) Build a geographic image of the network by assigning color value = 1 for all
points that is covered by at least one sensor node. The rest of the points are
assigned color value = 0.
2) Initialize a list of nodes that are supposed to cover the whole network area, called
Area_List.
3) Add the base node to the Area_List.
4) For all the nodes in the area, if a node is not overlapping with any node in the
Area_List, add it to the Area_List. The purpose of this step is to optimize node
distribution.
5) For each point in the network area, if the point is not covered by any node in the
Area_List, add the node that contains the point to the Area_List.
6) Nodes that are not in the Area_list after the “for” loops in steps 4, and 5 are
redundant nodes.
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OCO: Processing phase
Clean up redundant nodes
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OCO’s
reduction of
sensor nodes
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Number of nodes
in the sensing
area
OCO: number of nodes
after redundancy
removal + (number of
border nodes)
200
178 (126 border nodes)
11%
250
212 (136 border nodes)
15%
300
230 (126 border nodes)
23%
350
251 (131 border nodes)
28%
400
269 (110 border nodes)
33%
450
280 (101 border nodes)
38%
500
285 (101 border nodes)
43%
550
291 (86 border nodes)
47%
600
287 (88 border nodes)
52%
650
278 (83 border nodes)
57%
700
299 (72 border nodes)
57%
750
297 (74 border nodes)
60%
800
294 (73 border nodes)
63%
850
293 (73 border nodes)
66%
900
279 (66 border nodes)
69%
950
3152006)
(61 border nodes)
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67%
1000
295 (60 border nodes)
71%
% of reduction
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OCO: Processing phase
- Determine the border nodes
1.
2.
3.
Clean up redundant nodes
Define the border nodes
Find the shortest path from each node to the base
Table 2: Algorithm for Finding the Border
1)
For each pixel in the image, check if the color value =1.
2)
If true (meaning this pixel belongs to an object), scan all its
neighbors to see if any of them having the color value = 0. If true,
this pixel belongs to the border.

Finally, find a minimum set of nodes in the Area_List that
contain all the border points, which are the border nodes.
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OCO: Processing phase
Finding the Border Nodes
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Number of nodes
vs percentage of
border nodes
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Number of nodes in
the sensing area
OCO: number of
border nodes
% of border
nodes / number
of nodes
200
126
63%
250
136
54%
300
126
42%
350
131
37%
400
110
28%
450
101
22%
500
101
20%
550
86
16%
600
88
15%
650
83
13%
700
72
10%
750
74
10%
800
73
9%
850
73
9%
900
66
7%
61
6%
60
6%
950
1000
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OCO: Processing phase
- find the shortest path
Algorithm for finding the shortest path to the base for each node in the
Area_List
1) Work only with nodes in the Area_List of the ‘cleaning up redundant nodes’ step
(Table 1).
2) Assign parent_ID = 0 for all nodes.
3) Assign parent_ID = the base’s ID for all neighbors of the base and add these
nodes to a list, called Processing List.
4) For each node in the Processing List, consider all its neighbors. If the neighbor
has parent_ID = 0, assign the neighbor’s parent_ID = the node’s ID. Add the
neighbor to the Processing List.
5) Repeat step 4 until all nodes are assigned parent_ID.
After the loop, each node in the Area_list has a parent_ID. When a node wants to
send a message to the base, it just delivers the message to its parent. The
message is then continually forwarded until it reaches the base.
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Find the shortest path to the base for each node in the
Area_List.
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Assigning roles to the nodes
•
At the end of the processing phase, all nodes are
•
assigned missions.
The base broadcasts messages with node IDs to
assign tasks for them.
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•
The redundant nodes are turned off to conserve energy. They
just wake up after a long period (predefined) to receive
commands from the base. If there is no command or the
commands do not relate to them, they again switch to off totally.
•
The border nodes have the sensor modules and the radio
receiver modules ON (called ACTIVE state).
•
The rest of the nodes in the sensor network are called
forwarding nodes, which have their sensor modules OFF but
the radio receiver modules is ON (called FORWARD state).
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Roles of the nodes & initial states
Border
nodes
Forwarding
nodes
Redundant
Nodes +
Sensor
Active
Sleep
Sleep
Radio
Receive
Receive
Sleep
MCU
board
Sleep;
Sleep;
Sleep
wake up to create
messages only
wake up to create
messages only
+ Periodically wake up
to receive
commands from the
base
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OCO: Tracking phase
• Objects are assumed to have come from the outside.
• Normally, only the border nodes are ACTIVE.
• When a border node detects an object, it periodically
sends its position information to the base by first
forwarding the information to its parent.
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OCO: Maintenance phase
Maintenance phase
•
To reconfigure the network in the case of topology change
(probably due to dying or dead nodes)
•
Examples: exhausted nodes, damaged nodes, re-positioned
nodes
•
Exhausted nodes
 turns all its child nodes to SLEEP (orphan nodes)
 sends a report to the base.
•
Global re-configuration: When the base gets the report, it
enters the processing phase to reconfigure the whole
network, with dead nodes being removed and the network
restructured.
Local re-configuration: Find new parent(s) to adopt the
orphans.
•
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Base
OCO:
Maintenance
phase
O
new
M
N
X
Sample case:
The dead node is a
forwarding node.
new
M
x
A
C
C
2
B
A
1
A
2
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new
new
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B
1
C
1
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Base
OCO:
Maintenance
phase
O
X
A
Sample case:
The dead node is a
border node.
C
B
new
A
1
C
2
new
A
2
A
3
GAP
B
2
B
1
A3 Sensor RangeB2 Sensor Range
C
1
B1 Sensor Range
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Simulation
•
The tool used for simulation is OMNET++.
•
There are 3 basic components needed for the
simulation:
–
–
–
•
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sensor node
intruder object
sensor network
In addition, we need a module called manager to help
configure and run the simulation.
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Simulation of Sensor Nodes
Application
Sensor
Coordinator
Module
MAC
Radio
Energy
Layer 0
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Simulation
- Intrusion object simulation
An object has only two layers: the application layer + the physical layer.
Simulating
a sensor
network
with
intruder
objects
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Energy consumption calculation
• We use the assumptions in [5] as the basis when calculating the energy
dissipation for our simulations.
[5] Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan
(2000). “Energy-Efficient Communication Protocol for Wireless Microsensor Networks”. THE
HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, 2000.
Create/Receive a data message
Create/Receive a signal message
100 µJ
3 µJ
Send a data message (d<= 60m)
Send a signal message (d<=60m)
820 µJ
26 µJ
Send a message (d > 60m)
100 µJ + 0.1*d^2
Sensor board (full operation)
66 µJ/s
Radio board (idle/receive mode)
100 µJ/s
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Metrics
•
Four types of metrics are considered when comparing the
performance of the three selected methods:
•
The total energy consumption is defined as the total energy that
the network spends in a scenario.
•
The accuracy is a percentage of the number of detected object
positions of the method over the number of detected positions of
the DC.
•
The cost per detected point is an average number of energy units
that are spent for a detected position.
•
The time before first dead node is the time when the first node of
the network runs out of energy. This matrix is a significant
indication of the sensor network’s ‘well-being’ or longevity.
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Simulation Settings
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Num of nodes
DC
LEACH-based
OCO
200
200
200
178 (126 border nodes)
250
250
250
212 (136 border nodes)
300
300
300
230 (126 border nodes)
350
350
350
251 (131 border nodes)
400
400
400
269 (110 border nodes)
450
450
450
280 (101 border nodes)
500
500
500
285 (101 border nodes)
550
550
550
291 (86 border nodes)
600
600
600
287 (88 border nodes)
650
650
650
278 (83 border nodes)
700
700
700
299 (72 border nodes)
750
750
750
297 (74 border nodes)
800
800
800
294 (73 border nodes)
850
850
850
293 (73 border nodes)
900
900
900
279 (66 border nodes)
950
950
950
315 (61 border nodes)
1000
1000
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1000
295 (60 border nodes)
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Simulation Results
•
in the case of no intruder object
Energy
consumption
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Simulation Results
•
in the case of no intruder object
time before
first dead
node
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Paths of intruding objects
•
•
•
Intruder objects move following specific paths and come from outside of the network
area.
The moving paths of objects are created by draw images.
A MATLAB program reads the images and generates appropriate text files of
positions of the path images.
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Simulation Results (one intruder)
Energy consumption
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Simulation Results (one intruder)
time before first dead node
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Simulation Results (one intruder)
accuracy
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Simulation Results (one intruder)
cost per detected points
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Summary
•
We have devised a method, OCO, for efficient target tracking
in wireless sensor networks, and have evaluated its
performance in various simulation scenarios against two
other methods (DC and LEACH).
•
Based on the evaluations, OCO appears to consume less
energy than the other methods while achieving superior
accuracy.
•
The main strengths of OCO include its efficiency and easy
maintenance, meaning that, when too many nodes have
exhausted their energy, new nodes can be refilled to the
tracking area and the OCO method will be able to
dynamically build up a new network.
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Limitations & On-going Work
•
The sensor network usually works in hostile
environments; therefore, security features need to
be added to OCO.
 node-to-base and node-to-node authentications
•
Re-configuration of the networks
 algorithms in the maintenance phase
•
OCO needs to be implemented in a real sensor
network to further verify its performance.
 WSN test beds
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A WSN Test Bed
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Questions?
Contact:
T. Andrew Yang
[email protected]
University of Houston – Clear Lake
Houston, TX
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