1. Milestones, Feedback, Action Items Power Aware Distributed Systems Kickoff August 23, 2000

2. Power Aware Distributed Systems Impact  Power-aware algorithms, sensor node RTOS, and middleware will reduce sensor network aggregate energy requirements >1000X.  This capability will extend sensor network power dynamic range to span from prolonged (months) quiescent operation to “get me the information now at any cost”.  Power instrumentation of existing low-power sensor node provides baseline by which PAC/C tools and technology will be measured. Goals  Algorithms. Develop power-aware algorithms for cooperative signal processing that exploit sensor data locality, multi-resolution processing, sensor fusion, and accumulated intelligence.  Protocols. Design a distributed sensor network control middleware for power-aware (P-A) task distribution and hardware/software resource utilization migration.  Compilers/OS. Create sensor node RTOS to manage key resources – processor, radio, sensors.  Systems. Identify hardware power control knobs and readable parameters and make them available to the sensor node power-aware RTOS. Milestones [FY/Q]  P-A RTOS scheduling on research platform [01/Q1].  Instrumentation board for research platform [01/Q1].  Compressed image transmission (Laplacian Pyramid) [01/Q1].  SensorSim simulation tool with P-A extensions [01/Q4].  Tool for power-aware RTOS kernel synthesis [02/Q4].  Deployable platform with P-A control “knobs” [02/Q4].  P-A network resource allocation DP field demo [03/Q2].  RP w/ sensor-triggered activation & low power sleep [03/Q3].  High-res multi-look image classification demo [03/Q4]. Extending dynamic power range for distributed sensor networks. Sensor Node Hardware Control Knobs and Power Aware RTOS Cooperative Signal Processing Sensor Network Middleware

3. Power Aware Distributed Systems Q: How can you extend the dynamic power range of sensor networks from quiescent months of monitoring to frenetic minutes of activity? Architectural Approaches  Power Aware Research Platform Testbed  Deployable Power Aware Sensor Platform Middleware, Tools, and Techniques  Power Aware Resource Scheduling in RTOS  Techniques for Network-Wide Power Management Power Aware Algorithms  Multi-Resolution Distributed Algorithms

4. Architectural Approaches Instrument a state-of-the-art sensor node to understand power consumption in current systems.  Where can we expect significant power tradeoff?  Which knobs have the greatest dynamic range?  What baseline will we use for comparison? Develop power reconfigurable communications module.  Adapts parameters such as error control, equalization, data rate, and noise figure in real time according to channel state.  Leverage existing FPGA-based Rockwell radio. FY02/Q1 FY01/Q1

5. Power Aware Sensor Node RTOS Identify hardware knobs for radio/processor modules that can be altered dynamically by a power aware RTOS.  Find knobs in existing commercial deployable sensor nodes.  Introduce new knobs into research platform for eventual tech transfer into deployed commercial systems. Identify readable parameters (power, BER, signal strength, battery, etc.) that can be provided to power-aware RTOS.  Identify available parameters in deployed sensor nodes and instrument parameters in future PAC/C research platform modules. Provide operating system extensions for power management, task scheduling, and task control on individual sensor nodes.  Extend open API to expose and take advantage of available power aware capabilities on multiple research and deployed platforms. FY02/Q4 FY02/Q1 FY01/Q1

6. Power Aware Resource Scheduling Traditional RTOS schedulers manage processor from perspective of time. Key issue for power aware sensor node RTOS is to manage all resources – processor, radio, sensors. Use both static scheduling and dynamic scheduling.  Fixed priority preemptive scheduling (e.g. rate monotonic).  Dynamic priority preemptive scheduling (e.g. earliest deadline-first). The basic approach will exploit slack time in the schedule to shutdown a resource or to operate it at a lower-power lower- speed setting. A crucial observation that we offer is that missed deadlines in many computation tasks on a sensor node are no different than noise in the radio or sensor: it would result in a radio or sensor packet being dropped. FY02/Q4 FY03/Q2

7. Techniques for Network-Wide Power Management Design a distributed sensor network control middleware for power-aware task distribution and hardware/software resource utilization migration.  Extends existing low-power routing protocols developed in SensIT. Incorporate power trade-off analysis tools into the SensIT platform emulator for power aware application development and scenario simulation for sensor networks.  SensorSim simulator allows analysis with a larger system than is practical with real nodes.  Use SensIT topographical map GUI to visualize network power consumption behavior and analyze power aware techniques against replayed scenarios from SensIT field experiment data sets. FY02/Q3 FY01/Q4

8. Multi-Resolution Distributed Algorithms Develop power-aware algorithms for cooperative signal processing that exploit sensor data locality, multi-resolution processing, sensor fusion, and accumulated intelligence.  Adapt Laplacian Pyramid techniques for the efficient transmission and processing of images at multiple resolutions.  Multi-resolution image target classifier based on neural networks.  Multi-resolution, hierarchical sensor cueing including acoustic and/or low-res imaging sensor cueing simulator demo.  Directed high-res multi-look image classification/validation demo. FY01/Q1 FY02/Q1 FY02/Q4 FY03/Q4

9. Financial Profile FY00 FY01FY02 FY03Total $620,791 $1,481,410 $432,154 $450,000$2,984,355 Rockwell $150,000 $ 407,000 $100,000 $136,000$ 793,000 UCLA $150,000 $ 450,000 $129,000$ 729,000 ISI $320,791 $ 624,410 $203,154 $314,000$1,462,255

10. Feedback

11. Action Items