1. Minimum Power Configuration in Wireless Sensor Networks Guoliang Xing*, Chenyang Lu*, Ying Zhang**, Qingfeng Huang**, and Robert Pless* *Washington University in St. Louis **Palo Alto Research Center (PARC) Inc. MobiHoc '05 Chien-Ku Lai

2. Outline Introduction An Illustrating Example Problem Definition Approximation Algorithms Experimentation Discussion Conclusion

3. Introduction - Wireless Sensor Network (WSN) WSNs must aggressively conserve energy  to operate for extensive periods Wireless communication often dominates the energy dissipation in a WSN  Topology control  Power-aware routing Sleep management  Turning off redundant nodes  Only keeping a small number of active nodes

4. Introduction - Minimum Power Configuration (MPC) When network workload is low  The power consumption of a WSN is dominated by the idle state  Scheduling nodes to sleep saves the most power  It is more power-efficient for active nodes to use long communication ranges When network workload is high  Short radio ranges may be preferable

5. Introduction - Minimum Power Configuration (MPC) MPC provides a unified approach integrating  Topology control  Power-aware routing  Sleep management

6. An Illustrating Example Two network configurations 1. a communicates with c directly using transmission range |ac| while b remains sleeping 2. a communicates with b using transmission range |ab| and b relays the data from a to c using transmission range |bc| a b c

7. An Illustrating Example (cont.) a b c R ： a needs to send data to c at the rate B ： The bandwidth of all nodes

8. An Illustrating Example (cont.) a b c

10. An Illustrating Example - Mica2 Energy Model 433MHz Mica2 radio ：  The bandwidth ： 38.4Kbps  30 transmission power level  Suppose ： P tx (|ac|) = maximum transmission power (80.1mW) P tx (|ab|) = P tx (|bc|) = 24.6mW P id = 24mW P rx = 24mW P s = 6uW  When R > 16.8Kbps  Relaying through node b is more power efficient

11. Problem Definition A node can either be active or sleeping This work only considers the total active power consumption in a network The MPC problem ：  Given a network and a set of traffic demands, find a subnet that satisfies the traffic demands with minimum power consumption

12. Problem Definition (cont.) For the node u on the path f(s i,t j ) The data rate for source s i to sink t j

13. Problem Definition (cont.) Definition 1 (MPC problem)

14. Approximation Algorithms 1. Shortest-path Tree Heuristic (STH) 2. Incremental Shortest-path Tree Heuristic (ISTH)

15. Shortest-path Tree Heuristic (STH)

16. 2 22 2 2 2 2 2 1 3 4 2 4 2 1 s1s1 s2s2 t r1 = 0.1 r2 = 0.2

17. Shortest-path Tree Heuristic (STH) 2 22 2 2 2 2.2 2.1 2.3 2.4 2.2 2.4 2.2 2.1 s1s1 s2s2 t r1 = 0.1

18. Shortest-path Tree Heuristic (STH) 2 22 2 2 2 2.2 2.1 2.3 2.4 2.2 2.4 2.2 2.1 s1s1 s2s2 t r1 = 0.1

19. Shortest-path Tree Heuristic (STH) 2 22 2 2 2 2.4 2.2 2.6 2.8 2.4 2.82.4 2.2 s1s1 s2s2 t r2 = 0.2

20. Shortest-path Tree Heuristic (STH) 2 22 2 2 2 2.4 2.2 2.6 2.8 2.4 2.82.4 2.2 s1s1 s2s2 t r2 = 0.2

21. Incremental Shortest-path Tree Heuristic (ISTH)

22. 2 22 2 2 2 2 2 1 3 4 2 4 2 1 s1s1 s2s2 t r1 = 0.1 r2 = 0.2

23. Incremental Shortest-path Tree Heuristic (ISTH) 2 22 2 2 2 2.2 2.1 2.3 2.4 2.2 2.4 2.2 2.1 s1s1 s2s2 t r1 = 0.1

24. Incremental Shortest-path Tree Heuristic (ISTH) 2 22 2 2 2 2.2 2.1 2.3 2.4 2.2 2.4 2.2 2.1 s1s1 s2s2 t r1 = 0.1

25. Incremental Shortest-path Tree Heuristic (ISTH) 2 22 2 2 2 2.4 0.4 2.2 2.6 2.8 2.4 2.8 0.4 2.2 s1s1 s2s2 t r2 = 0.2

26. Incremental Shortest-path Tree Heuristic (ISTH) 2 22 2 2 2 2.4 0.4 2.2 2.6 2.8 2.4 2.8 0.4 2.2 s1s1 s2s2 t r2 = 0.2

27. Experimentation 1. Simulation Environment 2. Simulation Settings 3. Results

28. Simulation Environment Simulator ： Prowler  The MAC layer in Prowler is similar to the B- MAC protocol in TinyOS

29. Simulation Settings MPCP is compared with two baseline protocols  Minimum transmission （ MT ） routing  Minimum transmission power （ MTP ） routing The number of nodes ： 100 The region of network ： 150m × 150m  Divided into 10 × 10 grids Simulation time ： 300s  60s for initialization phase

30. Results - Total network energy cost

31. Results - Data delivery ratio at the sink

33. Discussion 1. Limitations of this paper 2. Potential future work

34. Discussion Every node in the network operates in a constant state  Further energy savings can be achieved by reducing the idle time of active nodes

35. Discussion This approach mainly focuses on minimizing the total energy consumption  Power-balanced should be achieved

36. Conclusion This paper  proposes the minimum power configuration approach to minimize the total power consumption of WSNs  formulates the energy conservation problem as a joint optimization problem where the power configuration of a network consists of  a set of active nodes  the transmission ranges of the nodes

37. Question? Thank you.