sam_thesis_defense.ppt 4051KB Jun 23 2011 12:32:42 PM

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Sam Tran P.

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APPLYING IMAGE PROCESSING TECHNIQUES

TO SIMULATE A SELF-ORGANIZED SENSOR

NETWORK FOR TRACKING OBJECTS

Thesis Committees:

Dr. T. A. Yang, Chair

Dr. L. Shih

Dr. G. C. Collins

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CONTENTS

I.

INTRODUCTION

II.

RELATED WORK

III.

THESIS METHOD

IV.

MODULE SIMULATION

V.

MATRICS

VI.

SIMULATION RESULTS

VII.

CONCLUSION

VIII.

LIMITATIONS AND FUTURE WORK

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I. INTRODUCTION

• A wireless sensor network (WSN) is a network of wireless sensor

nodes. Each node is a computer with attached sensors that can

[image:3.720.192.638.213.435.2]

process, exchange sensing data, as well as communicate wirelessly

among themselves to perform various tasks.

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I. INTRODUCTION

[image:4.720.21.707.184.369.2]

• Wireless sensor networks have many efficiency applications in military

and civil, which may be classified into three classes: data collection,

surveillance, and object tracking

Figure 2: An example of wireless sensor network for data collection in agriculture

Figure 3: Indoor Navigator project

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I. INTRODUCTION

• Current challenges:

– Small size of sensor node



Limit battery capacity and lower hardware

performance.

– The network is formed by randomly throwing thousands or even millions of

sensor nodes in an area



Overlapping sensing areas (redundancy).

– The network is usually installed in a large area with many physical effects,

such as earthquake, explosive, or etc.



The topology of the network has

to be changed frequently.

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• So, what we expect …

A sensor network that

• Employs multi-hop communication with.

• Is reconfigurable.

• Self-organizes (including re-routing, redundancy reduction, and

sensor deployment).

• Thesis target

Develop a method, called OCO (

Optimized Communication and

Organization

), which achieves the goals:

• Redundancy reduction.

• Efficient energy dissipation.

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II. RELATED WORK

Direct Communication

• The sensor module of all nodes is ON.

• Nodes send data directly to the base.

Advantages

• Give the best accuracy.

Disadvantages

• Unrealistic because the base has limited

number of channels.

• Node energy is limited.

• Cannot applied to a large area.

• Suffer redundancy.

According to a survey of Chuang (National Tsing Hua Uni.) in 5/2005, there

are three main approaches for target tracking in sensor network:

tree-based

,

cluster-based

, and

prediction-based

. Of course, the

Direct

[image:7.720.402.701.180.458.2]

Communication

is also a kind of approach.

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II. RELATED WORK

Tree-based method

• Build a hierarchy tree by using mathematic

model such as graph theory or Voronoi

diagram.

• Nodes send data to the base through its

ancestors.

Advantages

• Can applied for a large area because of

using multi-hop communication.

• Data could be aggregated at intermediate

nodes

Disadvantages

• Demand heavy calculation on the nodes for

building the tree and routing.

• Suffer redundancy.

_{Figure 6: Some methods to build} [image:8.720.463.706.93.425.2]

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II. RELATED WORK

Cluster-based method

• 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 communicate directly to

the base



Cannot applied to a large area and

suffer the channel limitation of the base.

[image:9.720.487.689.70.483.2]

• Suffer redundancy.

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II. RELATED WORK

Prediction-based method

• Based on tree-based or cluster-based method with added prediction

models:

– The moving objects will stay at the current speed and direction for the

next few seconds.

– The object’s speed and direction for next few seconds can be deduced

from the average of the object’s movement history.

– Different weights can be assigned to the different stages based on the

history.

Advantages

• Give more efficient results.

Disadvantages

• Keep disadvantages of the base method (tree or cluster).

• Prediction modes



unstable results.

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III. THESIS METHOD

OCO (Optimized Communication and Organization)

includes 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 intrusion objects.

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III. THESIS METHOD

Position collection phase:

– 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 father ID of

the neighbor nodes.

– The base’s neighbor nodes, after

sending its ID and position to its father

(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 father node,

and so on.

– When a node gets the position and ID

information from its neighbor, it

[image:12.720.488.679.82.467.2]

forwards the information to its father.

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III. THESIS METHOD

Processing phase:

Consists of three steps:

Clean up redundant nodes

;

Define the border nodes

;

Find the shortest path from

each node to the base

.

A.

Clean up redundant nodes:

1.

Firstly, we build a geographic image of the network by

assign color value = 1 for all points that is covered by

at least one sensor node. The rest points are assigned

color value = 0.

2.

Initialize a list of nodes that are supposed to cover the

whole network area, called Area_List. Assign Area_List

= null.

3.

Add the base node to the Area_List.

4.

For all 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 the 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.

[image:13.720.532.687.44.456.2]

6.

Nodes that are not in the Area_list after the “for” loops

in steps 3, 4, and 5 are redundant nodes and will be

turned off.

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III. THESIS METHOD

Processing phase….

B. Define border nodes

:

Nodes that are positioned along the border of the network area

are called border nodes. To define these nodes, firstly, apply the

border detection algorithm to find out a list of points that traverse

the border of the geographic image, called border points. Finally,

find a minimum set of nodes in the that contain all the border

points. The algorithm bellow is used to find the border of a

monochrome image:

•

For each pixel in the image, check if the color value =1.

•

If true (meaning this pixel belongs to the object), scan all its

neighbors to see if any of them having the color value = 0. If

true, this pixel belongs to the border.

•

Note: In this thesis, to optimize the border nodes, the image

border is moved toward inside of the network area by a

distance of sensing_radius/2 (half of the sensing radius). By

doing so, the number of border nodes will decrease

significantly without sacrificing any major characteristics of

the network. This change may cause the accuracy of object

detection to decrease a little bit, because the objects will be

recognized a little bit late. The delay is acceptable though,

in light of the gained benefit of reduced number of border

nodes.

_{Figure 10: An example of} [image:14.720.544.693.48.477.2]

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III. THESIS METHOD

C. Find the shortest path from each node to the base:

The following algorithm is used to find the shortest

path (the least hops) from every node in the list of

nodes (after the cleaning up) to the base:

1.

Work only with cleaned nodes.

2.

Assign father_ID = 0 for all nodes.

3.

Assign father_ID = the base’s ID for all

neighbors of the base and add these node to a

list, call processing list.

4.

For each node in the processing list. Consider all

its neighbors. If the neighbor having father_ID =

0, assign the neighbor’s father_ID = the node’s

ID. Add the neighbor to the processing list.

[image:15.720.493.702.71.474.2]

5.

Repeat step 4 until all nodes are assigned

father_ID.

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III. THESIS METHOD

After the processing phase, all nodes are assigned missions. The

base broadcasts messages with node IDs to assign task for

them. The summary is as follow:

•

The redundant nodes are turned off totally. 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 are ON (called ACTIVE state).

•

The rest of the nodes in the sensor network are called

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III. THESIS METHOD

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 father. We

have two different types of sensor nodes, so , there are two tracking solution

appropriated for each of them:

1.

Nodes are capable of sensing distinct multiple objects

•

Nodes can accurately track each of the multiple objects; thus it only

needs to activate its neighbors when a particular object is leaving its

coverage area.

•

The activated nodes will automatically return to the original status after

an interval of sensing nothing.

2.

Nodes can not distinguish multiple objects (regular node)

•

Without the capability to identify each of the multiple objects



Need to

periodically activate its neighbors, assuming one or more of the multiple

objects may leave its coverage area at any time.

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III. THESIS METHOD

Maintenance phase:

The purpose is to reconfigure the network in the case of topology change. The

following are example cases showing when such a topology change may be

necessary:

1.

Exhausted nodes



When energy level of a node is below a threshold, it turns all its sons to

SLEEP and sends a report to the base. 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.

2.

Damaged nodes



After a predefined interval of time, nodes require their child nodes to send

their IDs to them (via a small size message). Children nodes that do not report to their parents

are assumed to be damaged and will be reported to the base. Also, if a child node did not

receive any asking from its father after the predefined interval of time (meaning the father is

damaged), it will turn to SLEEP mode and wait for the command from the base.

3.

Re-positioned nodes (due to physical events, such as earthquake, explosion, etc)



When a

node’s position changes, it will be considered as damaged by its parent (case b.). After being

changed position, the node will do the following jobs:



Automatically turns to SLEEP mode.



Broadcast a message indicating that its position needs to be updated.



Any node that has received the broadcast will forward the information to the base,

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IV. MODULE SIMULATION

•

The tool that is used for simulation is OMNET++. It is selected

because it enables to put simulated modules at any place. That is

why we can simulate the random location feature of a sensor

network as well as build moving objects.

•

The are 3 basic components needed for the simulation:

sensor

node

,

intruder object, and sensor network

. In addition, we need a

module called

manager

to help the simulation such as:

–

Put nodes to locations based on the result files of the

processing

phase

.

–

Making connection among nodes.

–

Making connection between the objects and the nodes.

–

Simulate the sensing behavior.

–

Detect the first dead node in the network.

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Sensor node simulation:

Application

MAC

Layer 0

Coordinator Module

Sensor

Energy Radio



The Layer 0

module represents the physical layer of a sensor node.



MAC

module represents pre-processing packet layers.



The

Application

module represents the application layer.



The

Coordinator

module is an interface to connect all modules together. It categorizes an incoming

message in order to deliver it to the right module.



The

Sensor

module represents the sensor board in a sensor node.



The

Radio

module represents the radio board in a sensor node.

[image:20.720.73.569.62.302.2]



The

Energy module

represents battery in a sensor node.

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Intrusion object simulation:

Similar to the sensor node, an object has only two layers: the application layer on

top of the physical layer.

[image:21.720.112.641.191.469.2]

Simulating a sensor network with intruder objects:

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Selected method for simulation:

In evaluating the performance of OCO, two methods

are selected as comparisons: the Naive method (DC,

Direct Communication) and the cluster-based method

(LEACH).

–

In DC, the sensor modules of all nodes are ON and the radio

receiver modules are OFF. When having detected an

intruder object, the node sends the information about

intruders directly to the base node.

–

In LEACH, the sensor modules of all nodes are ON. The

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V. METRICS

There are four types of metrics that are considered when comparing

the performance of the three selected methods:

total energy

consumption

,

accuracy

,

cost per detected point

, and

time before the

first dead node

.

1.

The

total energy consumption

is defined as the total energy that the

network spends for tracking in a scenario.

2.

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 underlying assumption is that the DC method, due to its

direct communication to the base, should exhibit the highest

accuracy in detecting objects.

3.

The

cost per detected point

is an average number of energy units

that are spent for a detected position.

4.

The

time before first dead node

is the time when the first node of the

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V. METRICS

Energy consumption calculation

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

[image:24.720.147.637.142.338.2]

Radio board (idle/receive mode)

100 µJ/s

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V. METRICS

Accuracy calculation



Standard number of detected point

A sensor network with all nodes in the tracking mode (i.e. the sensor

board is in full operation mode) is a useful base for comparison, because it

provides the best possible quality of tracking. So, we consider the total

number of detected points in this case as 100%, and call it the

standard

number of detected point

. It also means the number of detected points in

DC is 100%.



Accuracy calculation

The accuracy of each method is a percent ratio between the number of

detected points of the method and the

standard number of detected point

.

Cost per detected point calculation

Cost per detected point

is a ratio between the total energy dissipation and

the total number of detected points of the method.

Time before first dead node calculation

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VI. SIMULATION RESULTS

Simulation summary:

The simulation environment is built as an area of 640x540. The number of nodes in

the network is 200, 250, 300, 350, 400, 450, 500,550, 600, 650,700, 750, 800, 850,

900, 950, and 1000 with 2J (Joule) of energy for each node. The sensing radius of

each node is 30m and the communication radius is 60m.

- Randomly generate nodes.

- Do collect ion position phase for OCO. - Generate text file results for OCO, DC, and LEACH. (OMNeT++)

Processing program: - Generate text files for OCO after do the processing phase

(C#)

DC algorithm simulation program  Text

files (OMNET++) OCO algorithm simulation program  Text files

(OMNET++)

LEACH algorithm simulation program  Text files

(OMNET++)

Evaluation programs

[image:26.720.146.596.198.460.2]

(MATLAB)

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Simulation summary…

Intruder objects are supposed moving follow specific paths and come from

outside of the network area. No data aggregation is allowed. The moving paths of

objects are created by draw images. A MATLAB program reads the images and

[image:27.720.157.587.187.470.2]

generates appropriate text files of positions of the path images. 4 paths 1, 2, 3, 4 are

shown respectively as in Figure 15

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[image:28.720.0.703.144.369.2]

Simulation results:

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Simulation results...

The detail results

are shown in the

thesis document.

This presentation just

shows the main

[image:29.720.237.666.64.529.2]

results of the

simulation.

Table 2: Summary of node deployment of the 3 methods

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)

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[image:30.720.31.700.93.358.2]

Simulation results in the case of no intruder object

Figure 17: Energy consumption (left) and time before first dead node (right) of the 3 methods in 9000 seconds (Note that if the time before first dead node is 1000, it means there are no dead node in the simulation)

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Simulation results in the case of 4 intruder objects moving in the 1

st

, 2

nd

, 3

rd

, and 4

th [image:31.720.109.620.119.441.2]

paths, respectively.

Figure 18: Energy consumption (upper left), time before first dead node (upper right), accuracy (lower left), and cost per detected points (lower right) of the 3 methods in the case of 4 intruder objects

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VII. CONCLUSION

•

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.

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IIX. LIMITATIONS AND FUTURE WORK

Limitations:

–

OCO needs to be implemented in a real sensor network to

further verify its performance.

–

The OCO did not yet support node-to-node communication

which could be needed for other applications.

–

The sensor network usually works in hostile environments,

therefore, security features of OCO need to be seriously

considered.

Future Work:

–

Implement OCO in a real sensor network.

–

The node to node multi-hops communication is very important. We

are considering adding this feature to OCO.

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IX. REFERENCES

[1] Guo, Weihua, Zhaoyu Liu, and Guangbin Wu (2003). “An Energy-Balanced Transmission Scheme for Sensor Networks”. Dept. of Software and Information Systems - Univ. of North Carolina at Charlotte. Retrieved 9/8/2005 at

http://www.cens.ucla.edu/sensys03/proceedings/p300-guo.pdf

[2] S.C.Chuang (5/26/2005) . “Survey on Target Tracking in Wireless Sensor Networks ”. Dept. of Computer Science – National Tsing Hua University. Retrieved 11/8/2005 at

http://mnet.cs.nthu.edu.tw/paper/934355tbl/050526--Survey%20on%20Target%20Tracking%20in%20wireless%20se nsor%20newworks.pdf

.

[3] H. T. Kung and D. Vlah. (2003) “Efficient Location Tracking Using Sensor Networks.” WCNC, March 2003. Retrieved 11/7/2005 from http://www.eecs.harvard.edu/~htk/publication/2003-wcnc-kung-vlah.pdf

[4] Wensheng Zhang and Guohong Cao (9/2004), “DCTC: Dynamic Convoy Tree-Based Collaboration for Target Tracking in Sensor Networks” IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 3, NO. 5, SEPTEMBER 2004. Retrieved 11/7/2005 from http://mcn.cse.psu.edu/paper/zhang/Twireless04.pdf

[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, JANUARY 4-7, 2000, MAUI, HAWAII. Retrieved 6/20/05 from

http://academic.csuohio.edu/yuc/mobile03/0403-heinzelman.pdf

[6] Wei-Peng Chen, Jennifer C. Hou, and Lui Sha, (2004) “Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks” IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 3, NO. 3, JULY SEPTEMBER 2004. Retrieved 11/7/2005 from http://www-rtsl.cs.uiuc.edu/papers/icnp03_final.pdf

[7] Yingqi Xu Winter, J. Wang-Chien Lee (2004). “Prediction-based strategies for energy saving in object tracking sensor networks” Mobile Data Management, 2004. Proceedings. 2004 IEEE International Conference. Retrieved 11/7/2005 from: doi.ieeecomputersociety.org/10.1109/MDM.2004.1263084

[8] Xu, Y., Winter, J., Lee, W.-C. “Dual predictionbased reporting for object tracking sensor networks” MOBIQUITOUS 2004. Retrieved 11/7/2005 from: doi.ieeecomputersociety.org/10.1109/MOBIQ.2004.1331722

[9] Andreas Savvides Mani Srivastava. “A SELF-CONFIGURING LOCATION DISCOVERY SYSTEM FOR SMART ENVIRONMENTS”. Retrieved 11/7/2005 from: http://www.eng.yale.edu/enalab/publications/cpcn_chapter.pdf

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IX. REFERENCES

[11] Sundeep Pattem, Sameera Poduri, and Bhaskar Krishnamachari (2003). “Energy-Quality Tradeoffs for Target

Tracking in Wireless Sensor Networks”. DEPARTMENT OF ELECTRICAL ENGINEERING AND DEPARTMENT OF COMPUTER SCIENCE,UNIVERSITY OF SOUTHERN CALIFORNIA. Retrieved 7/10/05 from http://www-scf.usc.edu/~pattem/PattemKrishnamachari_Tracking.pdf

[12] Shin Yoshizawa. “Voronoi Diagram”. Retrieved 11/7/2005 from:

http://www.mpi-sb.mpg.de/~shin/Research/CCurve/node22.html

[13] Rev B (2005). “MPR/ MIB User’s Manual”. DOCUMENT 7430-0021-06. Retrieved 7/10/05 from http://www.xbow.com/Support/Support_pdf_files/MPR-MIB_Series_Users_Manual.pdf [14] Matlab Image Processing Toolbox User's Guide. Retrieved 11/7/2005 from:

http://www.mathworks.com/access/helpdesk/help/toolbox/images/intro5.htm [15] Die.Net Dictionary Online. Retrieved 11/7/2005 from: http://dict.die.net/joule/

[16] Unit of Measure conversion table. Retrieved 11/7/2005 from: http://www.themeter.net/conv5_e.htm

[17] Jason Lester Hill (2003). “System Architecture for Wireless Sensor Networks”. Ph.D. thesis - UNIVERISY OF CALIFORNIA, BERKELEY. Retrieved 11/7/2005 from: http://www.motelab.org/papers/jhill-thesis.pdf [18] Cricket Project Group (2005). “The Cricket Indoor Location System” website, MIT. Retrieved 11/7/2005 from:

http://nms.lcs.mit.edu/projects/cricket/

[19] VigilNet Project group (2004). “VigilNet: An Integrated Sensor Network System for Energy-Efficient Surveillance” website, Computer Science Dept. – University of Virginia. Retrieved 11/7/2005 from:

http://www.cs.virginia.edu/~control/SOWN/index.html

[20] Sensor Network Research Group at Louisiana State University (2/1/2005), “Simulating Wireless Sensor Networks with OMNeT++”. Retrieved 11/7/2005 from http://bit.csc.lsu.edu/sensor_web/final_papers/SensorSimulator-IEEE-Computers.pdf

[21] Son Tran (2000). “Parallel Skeletonization for 3D binary image”, Son Tran Master Thesis. Retrieved 11/11/2005 from UHCL Library.

[22] Chih-Yu Lin, Wen-Chih Peng, and Yu-Chee Tseng (2004). “Efficient In-Network Moving Object Tracking in Wireless Sensor Networks”. Department of Computer Science and Information Engineering - National Chiao Tung

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