1. Speed and Direction Prediction- based localization for Mobile Wireless Sensor Networks Imane BENKHELIFA and Samira MOUSSAOUI Computer Science Department Houari Bourmediene University of Science and Technology-USTHB Algiers, Algeria Imane BENKHELIFA and Samira MOUSSAOUI Computer Science Department Houari Bourmediene University of Science and Technology-USTHB Algiers, Algeria

2. 10/13/2012Imane BENKHELIFA –MIC-CCA 20122 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 Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Introduction Motivation Introduction 10/13/20123 Battlefield Surveillance Maritime Surveillance Forest Fire Detection Precision AgricultureMonitoring Patients Monitoring Animals Imane BENKHELIFA –MIC-CCA 2012

4. Motivation Introduction Motivation Introduction 10/13/20124Imane BENKHELIFA –MIC-CCA 2012 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 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions

5. 10/13/2012Imane BENKHELIFA –MIC-CCA 20125 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction- based Localization Evaluation Conclusion & Future Directions Outline

6. Introduction Monte Carlo Boxed MCB Introduction Monte Carlo Boxed MCB 10/13/20126Imane BENKHELIFA –MIC-CCA 2012 Mobility adds a challenge to localization in WSN Simple Solution: refresh frequently the calculation of positions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions

7. 10/13/20127Imane BENKHELIFA –MIC-CCA 2012 Estimated position Real position Sample Box Previous position Estimated position Vmax 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. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Introduction Monte Carlo Boxed MCB Introduction Monte Carlo Boxed MCB

8. 10/13/20128Imane BENKHELIFA –MIC-CCA 2012 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 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Introduction Monte Carlo Boxed MCB Introduction Monte Carlo Boxed MCB

9. 10/13/20129Imane BENKHELIFA –MIC-CCA 2012 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 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Introduction Monte Carlo Boxed MCB Introduction Monte Carlo Boxed MCB

10. 10/13/2012Imane BENKHELIFA –MIC-CCA 201210 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction- based Localization Evaluation Conclusion & Future Directions Outline

11. 10/13/201211Imane BENKHELIFA –MIC-CCA 2012 Motivation Principle Prediction Motivation Principle Prediction Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions 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 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.

12. Motivation Principle Prediction Motivation Principle Prediction 10/13/201212Imane BENKHELIFA –MIC-CCA 2012 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions 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 As nodes may have different velocities and directions Solution  Predict the speed and the direction of unknown nodes SDPL: Speed &Direction Prediction based Localization

13. Motivation Principle Prediction Motivation Principle Prediction 10/13/201213Imane BENKHELIFA –MIC-CCA 2012 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Real position Estimated position r1 r2 r3 Principle of SDPL

14. Motivation Principle Prediction Motivation Principle Prediction 10/13/201214 Principle of SDPL Ek Ei Imane BENKHELIFA –MIC-CCA 2012 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Δ T k Δ Ti

15. Motivation Principle Prediction Motivation Principle Prediction 10/13/201215Imane BENKHELIFA –MIC-CCA 2012 Principle of SDPL – According to Ek If reception of one message – The node draws N samples from circle(pos A, D RSSI ) – 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 & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions

16. Case of reception of one message 16 Anchor Position Real Position of the sensor Estimated Position of the sensor Motivation Principle Prediction Motivation Principle Prediction 10/13/2012Imane BENKHELIFA –MIC-CCA 2012 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions

17. 10/13/201217Imane BENKHELIFA –MIC-CCA 2012 Principle of SDPL – According to Ek If reception of one message – The node draws N samples from circle(pos A, D RSSI ) – 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 & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction Motivation Principle Prediction

18. Case of reception of 2 messages 18 Anchor Position Real Position of the sensor Estimated Position of the sensor 10/13/2012Imane BENKHELIFA –MIC-CCA 2012 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction Motivation Principle Prediction

19. 10/13/201219Imane BENKHELIFA –MIC-CCA 2012 Principle of SDPL – According to Ek If reception of one message – The node draws N samples from circle(pos A, D RSSI ) – 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 & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction Motivation Principle Prediction

20. Case of reception of more than 3 messages 20 Anchor Positions Real Positions of the sensor Estimated Position of the sensor 10/13/2012Imane BENKHELIFA –MIC-CCA 2012 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction Motivation Principle Prediction

21. 2110/13/2012Imane BENKHELIFA –MIC-CCA 2012 Sensor positions Anchor positions Sensor is static during Δtsensor is mobile during Δt Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction Motivation Principle Prediction

22. 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 2210/13/2012Imane BENKHELIFA –MIC-CCA 2012 Ek Ei T Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction Motivation Principle Prediction

23. – 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. 23 Ek Ei T 10/13/2012Imane BENKHELIFA –MIC-CCA 2012 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction Motivation Principle Prediction

24. - 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. 2410/13/2012Imane BENKHELIFA –MIC-CCA 2012 Ek Ei T1 T2 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction Motivation Principle Prediction

25. – If no reception in Δt If the node has already estimated its speed and its direction: Else, the node keeps the last estimated position. 25 x= xprev + cos θ * speed * Time-diff y= yprev + sin θ * speed * Time-diff 10/13/2012Imane BENKHELIFA –MIC-CCA 2012 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction Motivation Principle Prediction

26. 2610/13/2012  Speed and Direction Prediction: Nodes follow a rectilnear movement where nodes have a constant velocity and direction during certain time periods (Δt) Imane BENKHELIFA –MIC-CCA 2012 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction Motivation Principle Prediction

27. Case of prediction 2710/13/2012Imane BENKHELIFA –MIC-CCA 2012 θ Current 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 & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction Motivation Principle Prediction

28. 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). 2810/13/2012Imane BENKHELIFA –MIC-CCA 2012 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation Principle Prediction Motivation Principle Prediction

29. 10/13/2012Imane BENKHELIFA –MIC-CCA 201229 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction- based Localization Evaluation Conclusion & Future Directions Outline

30. Simulation Environment Evaluation of SDPL Simulation Environment Evaluation of SDPL 10/13/201230Imane BENKHELIFA –MIC-CCA 2012 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) Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions

31. SDPL vs MCB  Variation of the maximum speed 10/13/201231Imane BENKHELIFA –MIC-CCA 2012 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Simulation Environment Evaluation of SDPL Simulation Environment Evaluation of SDPL

32. SDPL vs MCB  Variation of the broadcasting interval 10/13/201232Imane BENKHELIFA –MIC-CCA 2012 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Simulation Environment Evaluation of SDPL Simulation Environment Evaluation of SDPL

33. SDPL  Occurrence ratio of each case of estimation 10/13/201233Imane BENKHELIFA –MIC-CCA 2012 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 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Simulation Environment Evaluation of SDPL Simulation Environment Evaluation of SDPL

34. 10/13/2012Imane BENKHELIFA –MIC-CCA 201234 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction- based Localization Evaluation Conclusion & Future Directions Outline

35. 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. Conclusion Perspectives Conclusion Perspectives 10/13/201235Imane BENKHELIFA –MIC-CCA 2012 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions

36. Using SDPL technique in a geographic routing protocol. Combining the prediction with the multi-hop fashion. Conclusion Perspectives Conclusion Perspectives 10/13/201236Imane BENKHELIFA –MIC-CCA 2012 Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-based Localization Evaluation Conclusion & Future Directions

37. 10/13/201237Imane BENKHELIFA –MIC-CCA 2012