speed and direction prediction - based localization for mobile wireless sensor networks

Speed and Direction Prediction-based localization for Mobile Wireless Sensor

Networks

Imane BENKHELIFA and Samira MOUSSAOUI

Computer Science DepartmentHouari Bourmediene University of Science and Technology-USTHB

Algiers, Algeria

210/13/2012 Imane BENKHELIFA –MIC-CCA 2012

Motivation & Introduction

Localization in Mobile Wireless Sensor Networks

SDPL: Speed and Direction Prediction- based Localization

Evaluation

Conclusion & Future Directions

Outline

3

Motivation & IntroductionLocalization in Mobile Wireless Sensor NetworksSDPL: Speed and Direction Prediction-based LocalizationEvaluationConclusion & Future Directions

MotivationIntroduction

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Battlefield Surveillance

Maritime SurveillanceForest Fire Detection

Precision Agriculture Monitoring Patients

Monitoring Animals

Imane BENKHELIFA –MIC-CCA 2012

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MotivationIntroduction


• WSN Attractive Caracteristics (Fast Deployment, Fault Tolerance, reduced cost,… etc)

• Why Sensor Localization is important?

A detected event is only useful if an information relative to its geographical position is provided

Simple solution: equip each sensor with a GPS (Global Positioning System) Costly






Evaluation


Outline

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IntroductionMonte Carlo Boxed MCB

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• Mobility adds a challenge to localization in WSN• Simple Solution: refresh frequently the calculation of positions


Imane BENKHELIFA –MIC-CCA 2012 710/13/2012

Estimated positionReal position

Sample Box

Previous position

Estimated positionVmax

• Monte Carlo Boxed Method

• Key idea: represent the posterior distribution of possible positions with a set of samples based on previous positions and the maximal speed.





Advantage:

• Uses probabilistic approaches to predict new estimations





Drawbacks: • Works with maximal values such as

communication range and maximal speed of nodes.• No consideration of directions and real speed.• Considers a good number of anchors.







Evaluation


Outline


MotivationPrinciple

Prediction


• Most of the proposed methods consider a network equipped with many anchors very expensive and energy consumer

• Use of a robot (vehicule, drone, …) equipped with GPS as a single mobile anchor

• The robot can do other tasks:– Configure and calibrate sensors,– Synchronise them, – Collect sensed data, – Deploy new sensors ans disable others.


MotivationPrinciple

Prediction

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Most of proposed methods for MWSNs consider the maximum speed of all the nodes and none considers the direction of the nodes

As nodes may have different velocities and directions

Solution Predict the speed and the direction of unknown nodes

SDPL: Speed &Direction Prediction based Localization


MotivationPrinciple

Prediction

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Real position

Estimated position

r1

r2

r3

• Principle of SDPL


MotivationPrinciple

Prediction

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• Principle of SDPL EkEi


Δ TkΔ Ti


MotivationPrinciple

Prediction

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• Principle of SDPL– According to Ek

• If reception of one message – The node draws N samples from circle(pos A, DRSSI)– Estimated position = mean of samples

• If reception of two messages– Estimated position= gravity center of the intersection zone of anchor

circles

• If reception of more than three messages– Node calculates the intersection points of circles three by three– The smallest distances determine the most probable positions – Calculation and save of the predicted velocity and direction for futur

ustilization


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• Case of reception of one message

Anchor Position

Real Position of the sensor

Estimated Position of the sensor

MotivationPrinciple

Prediction






• If reception of two messages– Estimated position= gravity center of the intersection zone of anchor

circles

• If reception of more than three messages– Node calculates the intersection points of circles three by three– The smallest distances determine the most probable positions – Calculation and save of the predicted velocity and direction for futur

ustilization


MotivationPrinciple

Prediction

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• Case of reception of 2 messages

Anchor Position

Real Position of the sensor




MotivationPrinciple

Prediction




• If reception of two messages– Estimated position= gravity center of the intersection zone of

anchor circles

• If reception of more than three messages– Node calculates the intersection points of circles two by two– The smallest distances determine the most probable positions – Calculation and save of the predicted velocity and direction for

futur ustilization


MotivationPrinciple

Prediction

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• Case of reception of more than 3 messages

Anchor Positions

Real Positions of the sensor




MotivationPrinciple

Prediction


Sensor positions

Anchor positions

Sensor is static during Δt sensor is mobile during Δt


MotivationPrinciple

Prediction

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• If it exists a sub-set Ei (>=3)before the last sub-set Ek (i<k):– Node draws a line T through points of Ei with a linear

regression


EkEi

T


MotivationPrinciple

Prediction

http://fr.wikipedia.org/wiki/Fichier:Linreg.svg


– If T goes across all the elements of Ek, the node concludes that it doesn’f change its direction:• If (|Ek|<= 2) : the estimated position will be predicted from

T through a linear regression using the known Least square technique. • If (|Ek|>3) : use the resulted positions to refine the line of

the previous regression.

Ek

Ei

T

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MotivationPrinciple

Prediction

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- If there is no connection between T and Ek, the node concludes that it has changed its direction.* the node then calculates its new estimation according only to Ek.


EkEi

T1T2


MotivationPrinciple

Prediction

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– If no reception in Δt

• If the node has already estimated its speed and its direction:

• Else, the node keeps the last estimated position.

x= xprev + cos θ * speed * Time-diff y= yprev + sin θ * speed * Time-diff



MotivationPrinciple

Prediction


Speed and Direction Prediction:• Nodes follow a rectilnear movement where nodes have aconstant velocity and direction during certain time periods (Δt)


MotivationPrinciple

Prediction

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• Case of prediction


θCurrent Real Position of the sensor

Old estimated postions of the sensor

New estimated positon of the sensor


MotivationPrinciple

Prediction

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• Advantages:– Using measured distances instead of the communication

range small boxes more accurate positions.– Predecting the real speed of each sensor instead using

the maximum speed of all the sensors.– Predecting the direction of sensors.– One single mobile anchor.– Distributed. – Simple calculations: linear regression…– Can be applied in mix networks (static and mobile).10/13/2012 Imane BENKHELIFA –MIC-CCA 2012


MotivationPrinciple

Prediction





Evaluation


Outline


Simulation EnvironmentEvaluation of SDPL

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• Simulation Environment:– Simulator NS2 under Ubuntu 9.2– Area =200m x 200m– Nomber of nodes =100– Communication range =30m– Anchor velocity =20m/s

• Metrics:– Mean Error (Distance between estimated position and

real position)



• SDPL vs MCBVariation of the maximum speed

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1 3 5 10 150

0.5

1

1.5

2

2.5

3

SDPLMCB

Maximum speed of nodes (m/s)

Mea

n Er

ror (

r)




• SDPL vs MCBVariation of the broadcasting interval

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0.25 0.5 1 2 4 8 120

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

SDPLMCB

Broadcasting interal (s)

Mea

n Er

ror (

r)




• SDPL Occurrence ratio of each case of estimation

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0.5 1 2 40

5

10

15

20

25

30

35

40

45

50

12345

Broadcasting interval (s)

Occ

urre

nce

ratio

(%

)

Where1- Estimation from the whole area

2- Estimation from one anchor circle

3- Estimation from the intersection of 2 circles

4- Estimation from the intersection of 3 circles

5- Estimation from the prediction of the speed and the direction







Evaluation


Outline


• The prediction of the speed and the direction of unknown nodes is a promising idea.

• Thanks to the prediction , SDPL method allows decreasing the mean error by up to 50% comparing to MCB.

ConclusionPerspectives

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• Using SDPL technique in a geographic routing protocol.

• Combining the prediction with the multi-hop fashion.

ConclusionPerspectives

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QUESTIONS ?

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speed and direction prediction - based localization for mobile wireless sensor networks

Documents

sensor localization

mobile anchorafter

localization methods

localization basics

fault tolerance

reduced cost

environmental monitoring

geographical position