1. An Energy-Efficient Geographic Routing with Location Errors in Wireless Sensor Networks Julien Champ and Clement Saad I-SPAN 2008, Sydney (The international Symposium on Parallel Architectures, Algorithms, and Networks)

2. Outline ► Introduction ► Definition of Metric for Arc Cost ► The Routing Algorithm ► Simulations ► Conclusion

3. Introduction ► Wireless Sensor Network  Random Deployment  Inaccurate location information  Energy Constraint  Reply data with multi-hop routing path ► Routing algorithm  Flooding is not suitable in large Wireless Sensor Networks  GPS wastes heavy cost ► A routing algorithm has to select paths toward the destination that consume as less energy as possible.

4. Introduction ► Sensors are static ► Helicopter (X a, Y a ) Sensor a Sensor b Sensor c (X c, Y c ) (X b, Y b ) εaεa ε a : position error bound εaεa velocity

5. Introduction ► G = (V, E) ►

6. Introduction ► Motivations  Communication Probability with Location Errors  Energy Saving ► Goal  Metric for Arc Cost  Using this metric to instead of location information in each sensor node

7. Overview ► Only one destination, sink node ► Sensor with no location information ► Sensor with metric is determined by the computer on the helicopter ► Sensor find route to the destination by the proposed distributed algorithm

8. Definition of Metric for Arc Cost ► A cost to each arc of the graph  The probability of communication  Energy consumption  The realized progress cost

9. The probability of communication ► Three cases  Two sensors are located with exact positions  Only one sensor is located with estimated position and the other node is exactly located  Two sensors are located with estimated positions

10. The probability of communication ► p AB : a function to estimate the communication probability between A and B ► Case1 ► Case2

11. The probability of communication ► Case3 guarantee possible to communicate or possible not Definitely impossible

12. The probability of communication ► Case3 p AB =[0,1] p AB =[0,1]

13. The ration between energy consumption and realized progress ► R AB :normalized value between 0 and 1 as a function of energy consumption and progress realized when sensor A sends a message to B

14. The ration between energy consumption and realized progress ► [8], 1998 ICC ► As authors in [8]  a = 4, c = 2 x 10 8

15. The ration between energy consumption and realized progress ► Energy / Distance ► Optimal transmission range

16. The ration between energy consumption and realized progress ► E’(d) = 0 ► Use this optimal transmission range in order to normalize ratio R between 0 and 1

17. The ration between energy consumption and realized progress ► The optimal ratio ► The ratio corresponding to (A, B ) d A,BS d B,BS d AB A B BS

18. The ration between energy consumption and realized progress ► R AB

19. Metric for Arc Cost ► Cost to Arc (A, B)

20. The Routing Algorithm ► Energy-Efficient Geographic Routing (EEG- Routing) ► The least path cost to reach the base station for its possible neighbors, computer computes before deployment. ► Each sensor stores a Table Tab_Costs associating to each neighbor the cost to reach destination ► Exchange Hello message

21. The Routing Algorithm ► The position of the sensor which detected an event ► A message id ► Detected event information

22. The Routing Algorithm C B: C AB C: C AC C AC >C AB C: C AC A: C BC A: C CB D: C CD D B A C: C DC Base Station

23. Simulations ► 100 sensors ► 1200 x 1200 area ► Adjusting the maximum transmission range to have densities between 6 and 20.

24. Simulations

25. Simulations

26. Conclusion ► A new geographic routing for WSN based on estimated positions with position error bounds. ► EEG-Routing sends message along paths having the best trade-off between communication probability, progress and energy consumption

27. Thank You !