Speed and Direction Prediction - based localization for Mobile Wireless Sensor Networks

1. Speed and Direction Prediction-basedlocalization for Mobile Wireless Sensor Networks • Imane BENKHELIFA and Samira MOUSSAOUI • Computer Science Department • HouariBourmediene University of Science and Technology-USTHB • Algiers, Algeria

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

3. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Introduction Maritime Surveillance Forest FireDetection Monitoring Animals Precision Agriculture Monitoring Patients Battlefield Surveillance Imane BENKHELIFA –MIC-CCA 2012

4. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Introduction • WSN Attractive Caracteristics (FastDeployment, • FaultTolerance, reducedcost,… etc) • WhySensorLocalizationis important? • A detectedeventisonlyuseful if an information • relative to itsgeographical position isprovided • Simple solution: equipeachsensorwith a GPS (Global Positioning System)  Costly Imane BENKHELIFA –MIC-CCA 2012

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

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

7. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Introduction Monte Carlo Boxed MCB • Monte Carlo BoxedMethod • Key idea: represent the posterior distribution of possible positions with a set of samplesbased on previous positions and the maximal speed. Sample Box Vmax Estimated position Estimated position Previous position Real position Imane BENKHELIFA –MIC-CCA 2012

8. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Introduction Monte Carlo Boxed MCB • Monte Carlo BoxedMethod • Advantage: • Uses probabilistic approaches to predict new estimations Imane BENKHELIFA –MIC-CCA 2012

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

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

11. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Principle Prediction • Most of the proposedmethodsconsider a network equippedwithmanyanchors veryexpensive and energy consumer • Use of a robot (vehicule, drone, …) equippedwith GPS as a single mobile anchor • The robot can do othertasks: • Configure and calibratesensors, • Synchronise them, • Collectsensed data, • Deploy new sensors ans disableothers. Imane BENKHELIFA –MIC-CCA 2012

12. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Principle Prediction • Most of proposedmethodsforMWSNsconsider the maximum speed of all the nodes and none considers the direction of the nodes • Asnodesmay have differentvelocities and directions • Solution  Predict the speed and the direction of unknownnodes • SDPL: Speed &Direction PredictionbasedLocalization Imane BENKHELIFA –MIC-CCA 2012

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

14. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Principle Prediction • Principle of SDPL Ek Ei ΔTk Δ Ti Imane BENKHELIFA –MIC-CCA 2012

15. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Principle Prediction • Principle of SDPL • According to Ek • If reception of one message • The nodedraws N samplesfromcircle(pos A, DRSSI) • Estimated position = mean of samples • If reception of two messages • Estimated position= gravity center of the intersection zone of anchorcircles • If reception of more thanthree messages • Nodecalculates the intersection points of circlesthree by three • The smallest distances determine the most probable positions • Calculation and save of the predictedvelocity and direction for futur ustilization Imane BENKHELIFA –MIC-CCA 2012

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

17. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Principle Prediction • Principle of SDPL • According to Ek • If reception of one message • The nodedraws N samplesfromcircle(pos A, DRSSI) • Estimated position = mean of samples • If reception of two messages • Estimated position= gravity center of the intersection zone of anchorcircles • If reception of more thanthree messages • Nodecalculates the intersection points of circlesthree by three • The smallest distances determine the most probable positions • Calculation and save of the predictedvelocity and direction for futur ustilization Imane BENKHELIFA –MIC-CCA 2012

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

19. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Principle Prediction • Principle of SDPL • According to Ek • If reception of one message • The nodedraws N samplesfromcircle(pos A, DRSSI) • Estimated position = mean of samples • If reception of two messages • Estimated position= gravity center of the intersection zone of anchorcircles • If reception of more thanthree messages • Nodecalculates the intersection points of circlestwo by two • The smallest distances determine the most probable positions • Calculation and save of the predictedvelocity and direction for futur ustilization Imane BENKHELIFA –MIC-CCA 2012

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

21. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Principle Prediction Sensor positions Anchor positions SensorisstaticduringΔt sensoris mobile duringΔt Imane BENKHELIFA –MIC-CCA 2012

22. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Principle Prediction • If itexists a sub-set Ei (>=3)before the last sub-set Ek (i<k): • Nodedraws a line Tthrough points ofEiwith a linearregression Ek Ei T Imane BENKHELIFA –MIC-CCA 2012

23. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Principle Prediction • If T goesacross all the elements of Ek, the nodeconcludesthatitdoesn’f change its direction: • If (|Ek|<= 2) : the estimated position willbepredictedfromT through a linearregressionusing the knownLeast square technique. • If (|Ek|>3) : use the resulted positions to refine the line of the previousregression. Ek Ei T Imane BENKHELIFA –MIC-CCA 2012

24. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Principle Prediction - If thereis no connectionbetweenT and Ek, the nodeconcludesthatit has changedits direction. * the nodethencalculatesits new estimation accordingonly to Ek. Ek Ei T2 T1 Imane BENKHELIFA –MIC-CCA 2012

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

26. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Principle Prediction • Speed and Direction Prediction: • Nodesfollow a rectilnearmovementwherenodes have a constant velocity and direction during certain time periods (Δt) Imane BENKHELIFA –MIC-CCA 2012

27. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Principle Prediction • Case of prediction Oldestimated postions of the sensor θ Current Real Position of the sensor New estimated positon of the sensor Imane BENKHELIFA –MIC-CCA 2012

28. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Motivation Principle Prediction • Advantages: • Usingmeasured distances instead of the communication range small boxes  more accurate positions. • Predecting the real speed of eachsensorinsteadusing the maximum speed of all the sensors. • Predecting the direction of sensors. • One single mobile anchor. • Distributed. • Simple calculations: linearregression… • Can beapplied in mix networks (static and mobile). Imane BENKHELIFA –MIC-CCA 2012

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

30. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Simulation Environment Evaluation of SDPL • Simulation Environment: • Simulator NS2 underUbuntu 9.2 • Area =200m x 200m • Nomber of nodes =100 • Communication range =30m • Anchor velocity =20m/s • Metrics: • MeanError(Distance betweenestimated position and real position) Imane BENKHELIFA –MIC-CCA 2012

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

32. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Simulation Environment Evaluation of SDPL • SDPL vs MCB • Variation of the broadcastinginterval Imane BENKHELIFA –MIC-CCA 2012

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

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

35. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Conclusion Perspectives • The prediction of the speed and the direction of unknownnodesis a promisingidea. • Thanks to the prediction , SDPL methodallowsdecreasing the meanerror by up to 50% comparing to MCB. Imane BENKHELIFA –MIC-CCA 2012

36. Motivation & Introduction Localization in Mobile Wireless Sensor Networks SDPL: Speed and Direction Prediction-basedLocalization Evaluation Conclusion & Future Directions Conclusion Perspectives • Using SDPL technique in a geographicroutingprotocol. • Combining the predictionwith the multi-hop fashion. Imane BENKHELIFA –MIC-CCA 2012

37. QUESTIONS ? Imane BENKHELIFA –MIC-CCA 2012