Adaptive Sleep Scheduling for Energy-efficient Movement-predicted Wireless Communication

1. Adaptive Sleep Scheduling for Energy-efficient Movement-predicted Wireless Communication David K. Y. Yau Purdue University Department of Computer Science

2. Objective • Reducing energy consumption of battery powered devices, e.g., Laptops and Handhelds, in wireless networks • Wireless communication is power intensive • Node movement can be exploited to reduce energy use in communication

3. Movement Prediction • Observation: Reduced distance between communicating peers ⇒ Reduced transmission power requirement ⇒ Energy saving • Assuming network interface has transmission power control capability • Single hop communication – obvious • Multi hop communication – expected

4. Power Saving Strategy • If likely to move closer to the target, postpone communication for a future time • Assuming application can tolerate some delay k. • Needs movement prediction • Based on movement history • Consumes energy itself

5. Network Structure • Mobile nodes are moving within a rectangular plane • We divide the network into virtual grids • Each grid has a unique grid ID

6. Assumptions • Each node knows its own position – GPS • Each mobile host maintains a sequence of n previous grid IDs • Simplifying assumption • Target is fixed • Every mobile node knows the target’s location • Fixed target assumption can be relaxed • Both communicating peers are mobile

7. Notations • History of node h: Sh = {x1, x2, …, xn} • A window of size l (for i ≤ n-l+1): W(i,i+l-1) = {xi, xi+1, …, xi+l-1} • Distance between two grids i and j: d(i,j)

8. Example Average Distance (AD) Algorithm • [TMC 05] Calculate the average distance between a mobile node and the target over all windows of size k in the mobile node's movement history as: • If the current distance between the mobile node and the target is greater than avg, then the mobile node decides to postpone the communication, or else it communicates immediately. • Less Computational overhead • Takes into consideration the actual distance

9. AD Algorithm Example

10. Energy Use in Movement Tracking • Continuous movement tracking requires device to be turned on • Idle device can consume significant energy • Movement sampling • Device put to sleep between sampling instants • More frequent sampling => higher accuracy • How often and when to sample?

11. System/Communication Energy Tradeoff Communication energy System energy Sleep period Sleep period Total energy Sleep period Optimal Sleep period

12. Adaptive Wakeup Algorithms • Speed based adaptation (SBA) • Faster movement  more frequent updates • Delay budget based adaptation (DBA) • Less time until deadline  more frequent updates

13. Adaptive Wakeup Algorithms (Cont’d) • Position based adaptation (PoBA) • Position estimated at next wakeup instant, based on nodal speed and direction • Wakeup chosen at ``good’’ position estimate • Performance based adaptation (PeBA) • Distance savings compared between current and last updates • Decreased saving  reduced sleep

14. Sensor Implementation • Berkeley Mica mote • TR1000 916.5 MHz network interface • 256 level power control (radio output voltage proportional to square of input data pin current) • GPS would add 12—24 mW in operation • TinyOS • Sleep by snooze component • Power control by pot component

15. Implementation Architecture Positioning System Application Power Manager Transmission Scheduler Sleep Scheduler Operating System Services

16. Power Manager • Transmission scheduler • Implements postponement algorithm • Variable length of movement history • Variable delay budget (application specific) • Buffers packets until decision to transmit • Adaptive wakeup scheduler • Implements adaptive wakeup algorithm for position sampling • Wakes up at sampling instant • Adjusts next sleep period

17. Measurement Setup Constant Power Supply Multimeter + - - + Sensor R + -

18. Energy Estimation by Component • Multimeter setup measures total system energy use • Energy breakdown by selectively turning off system components / activities • Radio network interface turned on / off • Packet actually sent / not sent (but deleted from transmission queue) • Energy difference between configurations gives estimates of component energy use

19. System Parameters • Input parameters • Length of history maintained • Application delay budget • Mobility scenario • Fixed vs adaptive wakeup • Output parameters • Energy use and percentage saving (total and component) • Percentage distance saving • Actual postponement delay • Number of wakeups for position sampling

20. Average Speed of Mobility Scenarios

21. Percentage Distance Saving vs Sleep Period and Delay Budget

22. Percentage Energy Saving vs Sleep Period and Delay Budget

23. Percentage Distance Saving (Bicycle)

24. Percentage Energy Saving (Bicycle)

25. Number of Wakeups (Bicycle)

26. Actual Postponement Delay (Bicycle)

27. Percentage Distance Saving vs Mobility Scenario

28. Percentage Energy Saving vs Mobility Scenario

29. Number of System Wakeups vs Mobility Scenario

30. Performance Comparison between Sampling Strategies

31. Conclusions • Node movement prediction can reduce the energy cost of wireless network communication • However, need to balance against energy cost of movement prediction • Adaptive sampling schedule works well based on operating conditions • Simulations + measurements on sensor prototype • Saves substantial energy by putting device to sleep • Outperforms fixed sleep period in general, since optimal sleep period is hard to determine a priori