OCO - a method for target tracking in wireless sensor networks

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OCO - a method for target tracking in wireless
sensor networks

• T. Andrew Yang (yang_at_uhcl.edu)
• with Sam Tran
• Computer Science Program
• University of Houston Clear Lake
• Houston, Texas

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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 networks
  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
  re-construct 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. OCO Optimized Communication Organization
2. DC Direct Communication
3. 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 bases 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
• 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 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. Initialize a list of nodes that are supposed to cover the whole network area, called Area_List. Add the base node to the Area_List. 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. 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. 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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Number of nodes in the sensing area OCO number of nodes after redundancy removal (number of border nodes) of reduction
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
950 315 (61 border nodes) 67
1000 295 (60 border nodes) 71
OCOs reduction of sensor nodes
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OCO Processing phase- Determine the border nodes

1. Clean up redundant nodes
2. Define the border nodes
3. Find the shortest path from each node to the base

Table 2 Algorithm for Finding the Border For each pixel in the image, check if the color value 1. 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 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
950 61 6
1000 60 6
Number of nodes vs percentage of border nodes
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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 Work only with nodes in the Area_List of the cleaning up redundant nodes step (Table 1). Assign parent_ID 0 for all nodes. Assign parent_ID the bases ID for all neighbors of the base and add these nodes to a list, called Processing List. For each node in the Processing List, consider all its neighbors. If the neighbor has parent_ID 0, assign the neighbors parent_ID the nodes ID. Add the neighbor to the Processing List. 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.
• 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 wake up to create messages only Sleep wake up to create messages only Sleep
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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OCO Maintenance phaseSample caseThe dead
node is a forwarding node.
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OCO Maintenance phaseSample caseThe dead
node is a border node.
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Simulation

• The tool used for simulation is OMNET.
• There are 3 basic components needed for the
  simulation
• 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
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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.

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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 networks well-being or longevity.

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Simulation Settings
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 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 yang_at_uhcl.edu University
of Houston Clear Lake Houston, TX