1. CS 546: Intelligent Embedded Systems Gaurav S. Sukhatme Robotic Embedded Systems Lab Center for Robotics and Embedded Systems Computer Science Department University of Southern California gaurav@usc.edu http://robotics.usc.edu/~gaurav/CS546

2. Last time –Aevena updates (Richard) –HW4 (Karthik) –Localization overview Today –Localization papers –Energy management –Projects

3. Today Memory Processor InterfaceSensor and actuator suite Energy supply External communication Platform OS and SW architecture Tools User interface Application Figure adapted from [Pottie and Kaiser 2005] 1/24, 4/18: Cyclops 1/31: Networking 2/21: Energy management 4/18: Cyclops 2/7,14 and 28: Time synch, localization and data management 3/21,28 and 4/11: Environmental monitoring

4. Outline Sources of energy (size and densities) Where is energy consumed in an embedded system ? How can one optimize energy usage in embedded systems ?

5. Sources of Energy and their Densities Zinc air battery1200 mWh/cm 3 Li-Ion battery300 mWh/cm 3 Solar (outdoors)0.15-15 mW/cm 2 Solar (indoors)0.006 mW/cm 2 Vibrations0.001 mW/cm 3 Passive human powered1.8 mW Thermal0.0018 mW (/10C) Nuclear reaction10 6 mWh/cm 3 Fuel cells500 mWh/cm 3

6. Energy Capacity per kg NiCd air battery40 Wh Li-Ion battery200 Wh Hydrogen33 kWh Diesel13 kWh Methanol6 kWh TNT1.4 kWh

7. Energy sources for embedded systems Batteries are the best bet –Why – even though other sources have higher energy densities ? Issues to consider are capacity and lifetime

8. Energy consumption: Sensors Passive sensors may even generate energy –May even generate it –Geophone: magnetic core within a coil. Vibrations cause it to move thereby generating current in coil –Similarly microphones, photodetectors Passive sensors also consume energy –Signal amplification –A/D conversion Energy intensive sensors: IR detection devices which need cooling, digital cameras etc.

9. Energy Consumption: ICs Circuits have fundamental limits on energy efficiency CMOS transistor pair draws power each time its flipped Save power by parallel processing (exploit Moore’s law) and low clock speeds

10. Dynamic Voltage Scaling Control voltage and clock rates to save energy Scheduler determines lower processor speed which allows compute deadline to be met Clock speed and voltage scaled accordingly

11. Processing and Radios ASICs typically clock at lower speeds and have lower precision hence less power than DSPs Radios: power amplifier for Xmission, amplifiers, mixers, oscillators, A/D conversion and digital electronics

12. Energy Consumption: Communications Fundamental limit on range for a given power –Irrespective of Moore’s law –A power amplifier on a radio consumes most of the power and can’t be made smaller beyond a certain limit –Short range radios consume essentially the same power whether xmitting or receiving Only reliable power saving is to keep radio off for large periods of time

13. Energy Optimization: Location Communication raw data Process, reduce data volume and then xmit Choose routes to balance energy reserves of nodes

14. Energy Optimization: Communications Duty Cycling Turn system components on –Based on a deterministic schedule –Dynamically in response to events For the radio this means figuring out when to transmit, receive, idle and sleep

15. Energy Optimization: Adaptive Fidelity Extend network lifetime by turning off nodes Tradeoff detection probability for lifetime

16. Heterogeneous Energy Sources Harvest energy (e.g. Solar) Energy mules Load balancing in a network

17. Next week Adaptive sampling and data management