Milestones, Feedback, Action Items Power Aware Distributed Systems Kickoff August 23, 2000.
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Transcript of Milestones, Feedback, Action Items Power Aware Distributed Systems Kickoff August 23, 2000.
Milestones, Feedback, Action Items
Power Aware Distributed Systems Kickoff
August 23, 2000
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
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
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
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
FY01/Q1
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
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
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
Financial Profile
FY00 FY01 FY02 FY03 Total
$620,791 $1,481,410 $432,154 $450,000 $2,984,355
Rockwell $150,000 $ 407,000 $100,000 $136,000 $ 793,000UCLA $150,000 $ 450,000 $129,000 $ 729,000ISI $320,791 $ 624,410 $203,154 $314,000 $1,462,255
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