Your SlideShare is downloading. ×
Locating Emergency Responders using Mobile Wireless Sensor Networks
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Introducing the official SlideShare app

Stunning, full-screen experience for iPhone and Android

Text the download link to your phone

Standard text messaging rates apply

Locating Emergency Responders using Mobile Wireless Sensor Networks

70
views

Published on

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

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

Published in: Technology, Business

0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
70
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
0
Comments
0
Likes
2
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 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. 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. 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. 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. 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. 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. 13/05/2013 7Imane BENKHELIFA – ISCRAM 2013 satellite Control center hospital military volounteers police Fire truck doctor Rescue team Disaster area
  • 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. 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. 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. 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. 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. 13/05/2013 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. 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. 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. 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. 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. 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. • 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. 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. • 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. 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. • 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. 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. • 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. – 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. - 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. – 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. 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. • 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. • 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. 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. 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. • 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. • 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. • 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. 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. • 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. • 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. 13/05/2013 40Imane BENKHELIFA – ISCRAM 2013