1. Residual Energy Scan for Monitoring Sensor Network Yonggang Jerry Zhao,Ramesh Govindan Computer Science Department/ISI University of Southern CaliforniaLos Angeles Deborah Estrin Computer Science Department University of California, Los Angeles WCNC 2002

2. Outline  Introduction  Residual Energy Scan  Simulation  Conclusion

3. Introduction  Sensor network Consist a large collection of sensor node  Random deployment  Have only finite energy reserves from battery  Motivation It is critical that users be continuously updated of the sensor networks health indication  Explicit knowledge of the overall state of the sensor network

4. Introduction  Goal Design a residual energy scan  Depicts the remaining energy distribution within sensor network  Aid in incremental deployment of sensors

5. Introduction An Example of Residual Energy Scan

6. Residual Energy Scan  System model and assumption N sensor nodes random deployed on a m by m square plane Sensor node  Immobile  Symmetric communications  Location information  Power by batteries with normalized capacity 100%

7. Residual Energy Scan  The process of constructing a eScan Determining local eScans Disseminating eScans Aggregating eScans

8. Residual Energy Scan  Determining local eScans Each node constructs its local scan with  Residual energy level  Location {Value, Coverage}

9. Residual Energy Scan 1 2 3 4 5 7 8 6 16 12 11 10 15 9 14 13 {38%,C 16 } {32%,C 15 } {35%,C 12 } {36%,C 13 } {28%,C 14 } {28%,C 10 } {30%,C 11 } {23%,C 8 } {23%,C 7 } {30%,C 9 } {27%,C 4 } {25%,C 3 } {20%,C 2 } {24%,C 5 } {32%,C 1 } {28%,C 6 } sink

10. Residual Energy Scan 1 2 3 4 5 7 8 6 16 12 11 10 15 9 14 {38%,C 16 } {32%,C 15 } {35%,C 12 } {28%,C 14 } {28%,C 10 } {30%,C 11 } {23%,C 8 } {23%,C 7 } {30%,C 9 } {27%,C 4 } {25%,C 3 } {20%,C 2 } {24%,C 5 } {32%,C 1 } {28%,C 6 } sink interest 13 {36%,C 13 }

11. Residual Energy Scan 1 2 3 4 5 7 8 6 16 12 11 10 15 9 14 {38%,C 16 } {32%,C 15 } {35%,C 12 } {28%,C 14 } {28%,C 10 } {30%,C 11 } {23%,C 8 } {23%,C 7 } {30%,C 9 } {27%,C 4 } {25%,C 3 } {20%,C 2 } {24%,C 5 } {32%,C 1 } {28%,C 6 } sink interest Aggregation Tree 13 {36%,C 13 }

12. Residual Energy Scan  eScan A and eScan B can be aggregated if A.VALUE AND B.VALUE are similar A.COVERAGE AND B.COVERAGE are adjacent  When both condition are met

13. Residual Energy Scan 1 2 3 4 5 7 8 6 16 12 11 10 15 9 14 {38%,C 16 } {32%,C 15 } {35%,C 12 } {28%,C 14 } {28%,C 10 } {30%,C 11 } {23%,C 8 } {23%,C 7 } {30%,C 9 } {27%,C 4 } {25%,C 3 } {20%,C 2 } {24%,C 5 } {32%,C 1 } {28%,C 6 } sink interest Assume: T(tolerance):25% R=d 13 {36%,C 13 }

14. Residual Energy Scan 1 2 3 4 5 7 8 6 16 12 11 10 15 9 14 {38%,C 16 } {32%,C 15 } {35%,C 12 } {28%,C 14 } {28%,C 10 } {30%,C 11 } {23%,C 8 } {23%,C 7 } {30%,C 9 } {27%,C 4 } {25%,C 3 } {20%,C 2 } {24%,C 5 } {32%,C 1 } {28%,C 6 } sink interest Assume: T(tolerance):25% R=d 13 {36%,C 13 }

15. Residual Energy Scan 1 2 3 4 5 7 8 6 16 12 11 10 15 9 14 {38%,C 16 } {32%,C 15 } {35%,C 12 } {28%,C 14 } {28%,C 10 } {30%,C 11 } {23%,C 8 } {23%,C 7 } {30%,C 9 } {27%,C 4 } {25%,C 3 } {20%,C 2 } {24%,C 5 } {32%,C 1 } {28%,C 6 } sink interest Assume: T(tolerance):25% R=d 13 {36%,C 13 } Scan A ={(35%,38%),(12,13,16)} Scan A ={(30%,32%),(12,13)}

16. Residual Energy Scan 16 12 11 10 15 {38%,C 16 } {32%,C 15 } {35%,C 12 } {28%,C 10 } {30%,C 11 } 13 {36%,C 13 } Scan A ={(35%,38%),(12,13,16)} Scan B ={(25%,32%),(11,14,15)} Condition1: Condition2: Distance(A.COVERAGE,B.COVERAGE)<R

17. Residual Energy Scan 1 2 3 4 5 7 8 6 16 12 11 10 15 9 14 {38%,C 16 } {32%,C 15 } {35%,C 12 } {28%,C 14 } {28%,C 10 } {30%,C 11 } {23%,C 8 } {23%,C 7 } {30%,C 9 } {27%,C 4 } {25%,C 3 } {20%,C 2 } {24%,C 5 } {32%,C 1 } {28%,C 6 } sink interest Assume: T(tolerance): t R=d 13 {36%,C 13 }

18. Residual Energy Scan 1 2 3 4 5 7 8 6 16 12 11 10 15 9 14 {38%,C 16 } {32%,C 15 } {35%,C 12 } {28%,C 14 } {28%,C 10 } {30%,C 11 } {23%,C 8 } {23%,C 7 } {30%,C 9 } {27%,C 4 } {25%,C 3 } {20%,C 2 } {24%,C 5 } {32%,C 1 } {28%,C 6 } sink interest Assume: T(tolerance): t R=d 13 {36%,C 13 }

19. Simulation  Cost ratio: R=E 0 /E c  Relative distortion :  Energy dissipation model Uniform dissipation model HOTSPOT dissipation model  Each node has a probability of p=f(d) to initiate a local sensing activity  Exponential distribution  Pareto density

20. Simulation

25. Conclusion  Design of residual energy scans provides an overall abstracted view of residual energy in an energy-efficient manner

26. Residual Energy Scan Condition1: Condition2: Distance(A.COVERAGE,B.COVERAGE)<R