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Adaptive Radio Modes in Sensor Networks: How Deep to Sleep? . Raja Jurdak Antonio Ruzzelli Gregory O’Hare. University College Dublin , Ireland. SECON 2008 San Francisco, CA June 17, 2008. Outline. Motivation Protocols Energy Model Performance Evaluation Conclusion. Motivation.
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Adaptive Radio Modes in Sensor Networks: How Deep to Sleep? Raja Jurdak Antonio Ruzzelli Gregory O’Hare University College Dublin, Ireland SECON 2008 San Francisco, CA June 17, 2008
Outline • Motivation • Protocols • Energy Model • Performance Evaluation • Conclusion
Motivation • Need for energy-efficiency in WSNs • Radio is a major energy sink • Two major approaches for saving radio energy • Duty cycling • Wake-up radio • Put radio into sleep mode (either periodically or on-demand) • Current IEEE 802.15.4 radios (e.g CC2420) provide multiple low power modes Which radio mode is most energy-efficient?
Radio Sleep Mode Tradeoffs Deep sleep mode Light sleep mode µW mW Adapt radio sleep mode to current traffic conditions
Outline • Motivation • Protocols • Energy Model • Performance Evaluation • Conclusion
Protocols 1/2 T P • BMAC • IEEE 802.15.4 Periodically check for channel activity every T seconds Receiving Listener Sender uses preamble that has length P, with a duration of at least T seconds Data Sender Idle Transmission
Protocols 2/2 RFIDImpulse • Wake-up radio based on RFID • Attach tag to external interrupt pin of MCU • Remotely trigger tag to wake up radio • Enables power down of MCU
Outline • Motivation • Protocols • Energy Model • Performance Evaluation • Conclusion
Energy Model 1/3 Listening Energy LPL Listening Energy RFIDImpulse Radio current consumption in sleep mode α
Energy Model 2/3 Switching Energy (for one state transition) Cumulative Switching Energy (LPL) Cumulative Switching Energy (RFIDImpulse)
Energy Model 3/3 Microcontroller Energy Transmission Energy Reception Energy Sleeping Energy
Outline • Motivation • Protocols • Energy Model • Performance Evaluation • Conclusion
Performance Evaluation • Apply energy model to following scenarios • Explore inter-dependencies among MAC protocols, node platforms, and traffic load in WSNs • Energy tradeoffs • Radio sleep mode optimization • Measured current values from node platforms • 6-level binary tree static topology
Total Energy Low Traffic MicaZ TelosB
Energy Tradeoffs Low Traffic MicaZ TelosB
Total Energy High Traffic MicaZ TelosB
Energy Tradeoffs High Traffic MicaZ TelosB
Power Consumption versus Data Rate MicaZ TelosB
To Conclude Contributions • Proposed adaptive sleep modes according to current traffic activity • Presented comprehensive and generalizable energy model for evaluating energy consumption • Evaluated performance with 3 protocols and 2 node platforms with measured current values • Identified suitable radio sleep mode/protocol for given traffic load on each node platform Future work Implement mechanism to enable nodes to adapt their sleep mode on the fly according to current traffic load