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Adaptive Sleep Scheduling for Energy-efficient Movement-predicted Wireless Communication. David K. Y. Yau Purdue University Department of Computer Science. Objective. Reducing energy consumption of battery powered devices, e.g., Laptops and Handhelds, in wireless networks
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Adaptive Sleep Scheduling for Energy-efficient Movement-predicted Wireless Communication David K. Y. Yau Purdue University Department of Computer Science
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
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
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
Network Structure • Mobile nodes are moving within a rectangular plane • We divide the network into virtual grids • Each grid has a unique grid ID
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
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)
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
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?
System/Communication Energy Tradeoff Communication energy System energy Sleep period Sleep period Total energy Sleep period Optimal Sleep period
Adaptive Wakeup Algorithms • Speed based adaptation (SBA) • Faster movement more frequent updates • Delay budget based adaptation (DBA) • Less time until deadline more frequent updates
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
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
Implementation Architecture Positioning System Application Power Manager Transmission Scheduler Sleep Scheduler Operating System Services
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
Measurement Setup Constant Power Supply Multimeter + - - + Sensor R + -
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
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
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