Locating Emergency Responders using Mobile Wireless Sensor Networks

Locating Emergency Responders
using Mobile Wireless Sensor
Networks
Imane BENKHELIFA*,** Samira MOUSSAOUI**
Nadia NOUALI*
*CERIST Research Centre ** USTHB University
Algiers, Algeria Algiers, Algeria

13/05/2013 Imane BENKHELIFA – ISCRAM 2013 2
Motivation & Introduction
Localization in Mobile Wireless Sensor Networks
SDPL: Speed and Direction Prediction- based Localization
Evaluation
Conclusion & Future Directions
Outline

SDPL: Speed and Direction Prediction-based Localization
Evaluation
Motivation
Introduction
13/05/2013 3Imane BENKHELIFA – ISCRAM 2013

Evaluation
Motivation
Introduction

Evaluation
Motivation
Introduction
• Algerian Project: SAGESSE
– Disaster Management Information System using ICTs
– Post-Disaster Scenarios
– Ad hoc networks (WMNs, MANETs, WSNs) frameworks
– Wireless Sensor Networks (Data collection,
communication between teams…)
– Aerial/ Mobile Supervision

Evaluation
Motivation
Introduction

satellite
Control
center
hospital
military
volounteers
police
Fire truck
doctor
Rescue
team
Disaster
area

Evaluation
Motivation
Introduction
• Source: AWARE Project

Evaluation
Outline

Introduction
Monte Carlo Boxed MCB
Evaluation
• Simple solution: equip each sensor with a GPS
(Global Positioning System)
 Excessive energy consumption
 Cost
– Eg.: an integrated GPS chip: 50€ - 90€
– 60 sensors: 3000€ - 5400€ only for GPS receivers
 If No connection with Satellite (NLoS problem)
 Time Synchronization

Estimated position
Real position
Sample Box
Previous position
Estimated positionVmax
• Monte Carlo Boxed Method
Assumptions:
• some nodes know their locations anchors
Key idea:
• represent the posterior distribution of
possible positions with a set of samples
based on previous positions and the
maximal speed.
Evaluation
Introduction

Advantage:
• Uses probabilistic approaches to predict new
estimations
Evaluation
Introduction

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
Introduction

Evaluation
Outline

Motivation
Principle
Prediction
Evaluation
• Most of the proposed methods consider a network
equipped with many anchors  very expensive and energy
consumer
• Use of vehicule (car, drone, …) equipped with GPS as a
single mobile anchor
• The anchor can do other tasks:
– Take useful photos/ videos of the areas
– Configure and calibrate sensors,
– Synchronise them,
– Collect sensed data,
– Deploy new sensors ans disable others.

Motivation
Principle
Prediction
Evaluation
 Most of proposed methods for MWSNs
consider the maximum speed of all the
nodes and none considers the direction of
the nodes
 Nodes may have different velocities and
directions
 Solution  Predict the speed and the
direction of unknown nodes
 SDPL: Speed &Direction Prediction based
Localization

Motivation
Principle
Prediction
13/05/2013 17
• Principle of SDPL Ek
Ei
Imane BENKHELIFA – ISCRAM 2013
Evaluation
Δ Tk
Δ Ti

Motivation
Principle
Prediction
• 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
Evaluation

• Case of reception of one message
19
Anchor Position
Real Position of the sensor
Estimated Position of the sensor
Motivation
Principle
Prediction
13/05/2013 Imane BENKHELIFA – ISCRAM 2013
Evaluation

– According to Ek
anchor circles
– Node calculates the intersection points of circles three by three
futur ustilization
Evaluation
Motivation
Principle
Prediction

• Case of reception of 2 messages
21
Anchor Position
Real Position of the sensor
Evaluation
Motivation
Principle
Prediction

– According to Ek
anchor circles
– Node calculates the intersection points of circles two by two
futur ustilization
Evaluation
Motivation
Principle
Prediction

• Case of reception of more than 3 messages
23
Anchor Positions
Real Positions of the sensor
Evaluation
Motivation
Principle
Prediction

Sensor positions
Anchor positions
Sensor is static during Δt sensor is mobile during Δt
Evaluation
Motivation
Principle
Prediction

• 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
Ek
Ei
T
Evaluation
Motivation
Principle
Prediction

– 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.
26
Ek
Ei
T
Evaluation
Motivation
Principle
Prediction

- 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.
Ek
Ei
T1
T2
Evaluation
Motivation
Principle
Prediction

– If no reception in Δt
• If the node has already estimated its speed and its direction:
• Else, the node keeps the last estimated position.
28
x= xprev + cos θ * speed * Time-diff
y= yprev + sin θ * speed * Time-diff
Evaluation
Motivation
Principle
Prediction

2913/05/2013
 Speed and Direction Prediction:
• Nodes follow a rectilnear movement where nodes have a
constant velocity and direction during certain time periods (Δt)
Imane BENKHELIFA – ISCRAM 2013
Evaluation
Motivation
Principle
Prediction

• Case of prediction
θCurrent Real Position of the sensor
Old estimated postions of the sensor
New estimated positon of the sensor
Evaluation
Motivation
Principle
Prediction

• Advantages:
– Using measured distances instead of the communication
range small cercles  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).
Evaluation
Motivation
Principle
Prediction

Evaluation
Outline

Simulation Environment
Evaluation of SDPL
• Simulation Environment:
– Simulator NS2 under Ubuntu 9.2
– Area =200m x 200m
– Nomber of nodes =100
– Communication range =30m
– Anchor velocity =20m/s
– Mobility Model: Random Way Point
• Metrics:
– Mean Error (Distance between estimated position and real
position)
Evaluation

• SDPL vs MCB
Variation of the maximum speed
MeanError(r)
Maximum speed of nodes (m/s)
SDPL
MCB
Evaluation
Evaluation of SDPL

• SDPL vs MCB
Variation of the broadcasting interval
MeanError(r)
Broadcasting interal (s)
SDPL
MCB
Evaluation
Evaluation of SDPL

• SDPL
 Occurrence ratio of each case of estimation
Occurrenceratio(%)
Broadcasting interval (s)
1
2
3
4
5
Where
1- 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
Evaluation of SDPL

Evaluation
Outline

• The prediction of the speed and the direction
of Emergency Responders is a promising idea.
• Thanks to the prediction , SDPL method allows
decreasing the mean error by up to 50%
comparing to MCB.
Conclusion
Perspectives
Evaluation

• Using SDPL technique in disseminating emergency
messages (a real-time geographic routing protocol).
• Appling SDPL in 3D environment  drones,
helicopters..
• Studying the effect of noisy environment
Conclusion
Perspectives
Evaluation

Locating Emergency Responders using Mobile Wireless Sensor Networks

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