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Locating Emergency Responders
using Mobile Wireless Sensor
Networks
Imane BENKHELIFA*,** Samira MOUSSAOUI**
Nadia NOUALI*
...
13/05/2013 Imane BENKHELIFA – ISCRAM 2013 2
Motivation & Introduction
Localization in Mobile Wireless Sensor Networks
SDPL...
Motivation & Introduction
Localization in Mobile Wireless Sensor Networks
SDPL: Speed and Direction Prediction-based Local...
Motivation & Introduction
Localization in Mobile Wireless Sensor Networks
SDPL: Speed and Direction Prediction-based Local...
Motivation & Introduction
Localization in Mobile Wireless Sensor Networks
SDPL: Speed and Direction Prediction-based Local...
Motivation & Introduction
Localization in Mobile Wireless Sensor Networks
SDPL: Speed and Direction Prediction-based Local...
13/05/2013 7Imane BENKHELIFA – ISCRAM 2013
satellite
Control
center
hospital
military
volounteers
police
Fire truck
doctor...
Motivation & Introduction
Localization in Mobile Wireless Sensor Networks
SDPL: Speed and Direction Prediction-based Local...
13/05/2013 Imane BENKHELIFA – ISCRAM 2013 9
Motivation & Introduction
Localization in Mobile Wireless Sensor Networks
SDPL...
Introduction
Monte Carlo Boxed MCB
13/05/2013 10Imane BENKHELIFA – ISCRAM 2013
Motivation & Introduction
Localization in M...
13/05/2013 11Imane BENKHELIFA – ISCRAM 2013
Estimated position
Real position
Sample Box
Previous position
Estimated positi...
13/05/2013 12Imane BENKHELIFA – ISCRAM 2013
• Monte Carlo Boxed Method
Advantage:
• Uses probabilistic approaches to predi...
13/05/2013 13Imane BENKHELIFA – ISCRAM 2013
• Monte Carlo Boxed Method
Drawbacks:
• Works with maximal values such as
comm...
13/05/2013 Imane BENKHELIFA – ISCRAM 2013 14
Motivation & Introduction
Localization in Mobile Wireless Sensor Networks
SDP...
13/05/2013 15Imane BENKHELIFA – ISCRAM 2013
Motivation
Principle
Prediction
Motivation & Introduction
Localization in Mobi...
Motivation
Principle
Prediction
13/05/2013 16Imane BENKHELIFA – ISCRAM 2013
Motivation & Introduction
Localization in Mobi...
Motivation
Principle
Prediction
13/05/2013 17
• Principle of SDPL Ek
Ei
Imane BENKHELIFA – ISCRAM 2013
Motivation & Introd...
Motivation
Principle
Prediction
13/05/2013 18Imane BENKHELIFA – ISCRAM 2013
• Principle of SDPL
– According to Ek
• If rec...
• Case of reception of one message
19
Anchor Position
Real Position of the sensor
Estimated Position of the sensor
Motivat...
13/05/2013 20Imane BENKHELIFA – ISCRAM 2013
• Principle of SDPL
– According to Ek
• If reception of one message
– The node...
• Case of reception of 2 messages
21
Anchor Position
Real Position of the sensor
Estimated Position of the sensor
13/05/20...
13/05/2013 22Imane BENKHELIFA – ISCRAM 2013
• Principle of SDPL
– According to Ek
• If reception of one message
– The node...
• Case of reception of more than 3 messages
23
Anchor Positions
Real Positions of the sensor
Estimated Position of the sen...
2413/05/2013 Imane BENKHELIFA – ISCRAM 2013
Sensor positions
Anchor positions
Sensor is static during Δt sensor is mobile ...
• 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 linea...
– If T goes across all the elements of Ek, the node
concludes that it doesn’f change its direction:
• If (|Ek|<= 2) : the ...
- If there is no connection between T and Ek, the node concludes
that it has changed its direction.
* the node then calcul...
– If no reception in Δt
• If the node has already estimated its speed and its direction:
• Else, the node keeps the last e...
2913/05/2013
 Speed and Direction Prediction:
• Nodes follow a rectilnear movement where nodes have a
constant velocity a...
• Case of prediction
3013/05/2013 Imane BENKHELIFA – ISCRAM 2013
θCurrent Real Position of the sensor
Old estimated postio...
• Advantages:
– Using measured distances instead of the communication
range small cercles  more accurate positions.
– Pr...
13/05/2013 Imane BENKHELIFA – ISCRAM 2013 32
Motivation & Introduction
Localization in Mobile Wireless Sensor Networks
SDP...
Simulation Environment
Evaluation of SDPL
13/05/2013 33Imane BENKHELIFA – ISCRAM 2013
• Simulation Environment:
– Simulato...
• SDPL vs MCB
Variation of the maximum speed
13/05/2013 34Imane BENKHELIFA – ISCRAM 2013
MeanError(r)
Maximum speed of no...
• SDPL vs MCB
Variation of the broadcasting interval
13/05/2013 35Imane BENKHELIFA – ISCRAM 2013
MeanError(r)
Broadcastin...
• SDPL
 Occurrence ratio of each case of estimation
13/05/2013 36Imane BENKHELIFA – ISCRAM 2013
Occurrenceratio(%)
Broadc...
13/05/2013 Imane BENKHELIFA – ISCRAM 2013 37
Motivation & Introduction
Localization in Mobile Wireless Sensor Networks
SDP...
• The prediction of the speed and the direction
of Emergency Responders is a promising idea.
• Thanks to the prediction , ...
• Using SDPL technique in disseminating emergency
messages (a real-time geographic routing protocol).
• Appling SDPL in 3D...
13/05/2013 40Imane BENKHELIFA – ISCRAM 2013
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Locating Emergency Responders using Mobile Wireless Sensor Networks

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Presentation of Imane Benkhelifa, Samira Moussaoui and Nadia Nouali on the topic "Locating Emergency Responders using Mobile Wireless Sensor Networks" at ISCRAM2013

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Locating Emergency Responders using Mobile Wireless Sensor Networks

  1. 1. Locating Emergency Responders using Mobile Wireless Sensor Networks Imane BENKHELIFA*,** Samira MOUSSAOUI** Nadia NOUALI* *CERIST Research Centre ** USTHB University Algiers, Algeria Algiers, Algeria
  2. 2. 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
  3. 3. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Introduction 13/05/2013 3Imane BENKHELIFA – ISCRAM 2013
  4. 4. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Introduction 13/05/2013 4Imane BENKHELIFA – ISCRAM 2013
  5. 5. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Introduction 13/05/2013 5Imane BENKHELIFA – ISCRAM 2013 • 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
  6. 6. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Introduction 13/05/2013 6Imane BENKHELIFA – ISCRAM 2013
  7. 7. 13/05/2013 7Imane BENKHELIFA – ISCRAM 2013 satellite Control center hospital military volounteers police Fire truck doctor Rescue team Disaster area
  8. 8. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Introduction 13/05/2013 8Imane BENKHELIFA – ISCRAM 2013 • Source: AWARE Project
  9. 9. 13/05/2013 Imane BENKHELIFA – ISCRAM 2013 9 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction- based Localization Evaluation Conclusion & Future Directions Outline
  10. 10. Introduction Monte Carlo Boxed MCB 13/05/2013 10Imane BENKHELIFA – ISCRAM 2013 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions • 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
  11. 11. 13/05/2013 11Imane BENKHELIFA – ISCRAM 2013 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. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Introduction Monte Carlo Boxed MCB
  12. 12. 13/05/2013 12Imane BENKHELIFA – ISCRAM 2013 • Monte Carlo Boxed Method Advantage: • Uses probabilistic approaches to predict new estimations Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Introduction Monte Carlo Boxed MCB
  13. 13. 13/05/2013 13Imane BENKHELIFA – ISCRAM 2013 • Monte Carlo Boxed Method 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. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Introduction Monte Carlo Boxed MCB
  14. 14. 13/05/2013 Imane BENKHELIFA – ISCRAM 2013 14 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction- based Localization Evaluation Conclusion & Future Directions Outline
  15. 15. 13/05/2013 15Imane BENKHELIFA – ISCRAM 2013 Motivation Principle Prediction Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions • 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.
  16. 16. Motivation Principle Prediction 13/05/2013 16Imane BENKHELIFA – ISCRAM 2013 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions  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
  17. 17. Motivation Principle Prediction 13/05/2013 17 • Principle of SDPL Ek Ei Imane BENKHELIFA – ISCRAM 2013 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Δ Tk Δ Ti
  18. 18. Motivation Principle Prediction 13/05/2013 18Imane BENKHELIFA – ISCRAM 2013 • 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 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions
  19. 19. • 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 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions
  20. 20. 13/05/2013 20Imane BENKHELIFA – ISCRAM 2013 • 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 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction
  21. 21. • Case of reception of 2 messages 21 Anchor Position Real Position of the sensor Estimated Position of the sensor 13/05/2013 Imane BENKHELIFA – ISCRAM 2013 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction
  22. 22. 13/05/2013 22Imane BENKHELIFA – ISCRAM 2013 • 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 two by two – The smallest distances determine the most probable positions – Calculation and save of the predicted velocity and direction for futur ustilization Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction
  23. 23. • Case of reception of more than 3 messages 23 Anchor Positions Real Positions of the sensor Estimated Position of the sensor 13/05/2013 Imane BENKHELIFA – ISCRAM 2013 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction
  24. 24. 2413/05/2013 Imane BENKHELIFA – ISCRAM 2013 Sensor positions Anchor positions Sensor is static during Δt sensor is mobile during Δt Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction
  25. 25. • 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 2513/05/2013 Imane BENKHELIFA – ISCRAM 2013 Ek Ei T Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction
  26. 26. – 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 13/05/2013 Imane BENKHELIFA – ISCRAM 2013 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction
  27. 27. - 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. 2713/05/2013 Imane BENKHELIFA – ISCRAM 2013 Ek Ei T1 T2 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction
  28. 28. – 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 13/05/2013 Imane BENKHELIFA – ISCRAM 2013 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction
  29. 29. 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 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction
  30. 30. • Case of prediction 3013/05/2013 Imane BENKHELIFA – ISCRAM 2013 θCurrent Real Position of the sensor Old estimated postions of the sensor New estimated positon of the sensor Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction
  31. 31. • 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). 3113/05/2013 Imane BENKHELIFA – ISCRAM 2013 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction
  32. 32. 13/05/2013 Imane BENKHELIFA – ISCRAM 2013 32 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction- based Localization Evaluation Conclusion & Future Directions Outline
  33. 33. Simulation Environment Evaluation of SDPL 13/05/2013 33Imane BENKHELIFA – ISCRAM 2013 • 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) Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions
  34. 34. • SDPL vs MCB Variation of the maximum speed 13/05/2013 34Imane BENKHELIFA – ISCRAM 2013 MeanError(r) Maximum speed of nodes (m/s) SDPL MCB Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Simulation Environment Evaluation of SDPL
  35. 35. • SDPL vs MCB Variation of the broadcasting interval 13/05/2013 35Imane BENKHELIFA – ISCRAM 2013 MeanError(r) Broadcasting interal (s) SDPL MCB Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Simulation Environment Evaluation of SDPL
  36. 36. • SDPL  Occurrence ratio of each case of estimation 13/05/2013 36Imane BENKHELIFA – ISCRAM 2013 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 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Simulation Environment Evaluation of SDPL
  37. 37. 13/05/2013 Imane BENKHELIFA – ISCRAM 2013 37 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction- based Localization Evaluation Conclusion & Future Directions Outline
  38. 38. • 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 13/05/2013 38Imane BENKHELIFA – ISCRAM 2013 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions
  39. 39. • 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 13/05/2013 39Imane BENKHELIFA – ISCRAM 2013 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions
  40. 40. 13/05/2013 40Imane BENKHELIFA – ISCRAM 2013

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