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Panoptes: Low-Power, Scalable Video Sensor Networking Technologies

This paper discusses the motivation, challenges, and architecture of low-power, scalable video sensor networking technologies. It also presents the Panoptes platform and its applications in environmental monitoring, health care, emergency response, surveillance, and robotics. The paper explores buffering and adaptation mechanisms, experimentation results, and future work in power management and integration with traditional sensors.

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Panoptes: Low-Power, Scalable Video Sensor Networking Technologies

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  1. Panoptes: Low-Power, Scalable Video Sensor Networking Technologies Wu-chi Feng, Ed Kaiser, Brian Code, Mike Shea, Wu-chang Feng, Louis Bavoil Department of Computer Science and Engineering OGI School of Science and Engineering at OHSU

  2. Motivation • Sensor networking technologies are great • Real-time in situ measurement of environments • Habitat monitoring (UCLA) • Columbia River forecasting (OGI) • REINAS Monterey Bay system (UC Santa Cruz) • Artic web cam (NOAA) • Video sensor networking technologies • Can add eyes to sensor data • Require significant computing and bandwidth resources beyond traditional sensor technologies

  3. Motivation • The applications • Environmental monitoring • Example: Video sensor every ¼ mile along the entire Oregon coast • Health care delivery • Example: Privacy ensuring elderly health care • Emergency response • Habitat monitoring • Surveillance and security • Robotics

  4. Motivation • Video sensor networking challenges • Low-power, power-aware video sensors • PoE applications • Environmental / autonomous deployment • Providing mechanisms that allow the sensor network to be tailored to specific applications • “Programmability” • Managing information implosion (N  1) • Buffering and adaptation • Making it easy to access both traditional scalar and video data within the sensor network

  5. The Panoptes Project at OGI • The goal: • Flexible, extensible middleware that supports massively scalable video-based sensor networks • Short term • Low-power, programmable, adaptable, video sensor for experimental testbed • Buffering and adaptation algorithms for sensor • Bringing together a large number of flows • Longer term • Integration of traditional low-power sensors with video sensors • Application-specific extensions

  6. The Rest of This Talk • The Panoptes platform • Hardware and software systems • Software architecture • Experimentation • A demonstration system • The Little Sister Sensor Networking Application • Conclusions and future work

  7. 206 MHz Intel StrongArm USB-based video 802.11 wireless Embedded Linux The Panoptes Platform • Picking a platform • Berkeley Motes • COTS web cameras • General embedded CPU platforms 320x240 video 22 fps software compressed ~5.5 Watts maximum

  8. The Panoptes Platform Video Sensor Architecture Compression IPP-based Currently: JPEG, Diff JPEG, Cond. Replenishment Buffering and Adaptation Supports disconnected or intermittent operation Priority mapping of streaming data elements Application-Specific Filtering Event-detection Time-elapsed images Computer vision Video 4 Linux Time Power Management

  9. Sensor streaming • Inverse multicast • Any data might be good • Some data unknown a priori • Buffering can be used Buffering and Adaptation • Sensor streaming is different than video streaming today • Live streaming • Late data useless • Data unknown a priori • Limited use of buffering in adaptation • Video-on-demand streaming • Just in time delivery • All data known a priori • Streaming can take advantage of known data • Buffering useful How long to keep data in the sensor buffer? How do you prioritized data between new/old?

  10. Experimentation • The USB bottleneck • Compression performance on Panoptes • Buffering and adaptation performance • Power measurements

  11. USB Capture Performance

  12. USB Capture Performance 6.9 Mbps

  13. USB Capture Performance 111 Mbps

  14. USB Capture Performance 27.6 Mbps

  15. Software Compression Performance

  16. Capture / Compression Performance

  17. Buffering and Adaptation

  18. Camera on/ net. connected All services running Camera on (capturing) CPU loop System Idle Standby Network connected Camera standby Power Consumption

  19. A Demonstration System • The Little Sister Sensor Networking Application Network Network Query Manager Stream Manager Camera Manager(s)

  20. Future Work • Python-based experimentation • Power management • Developing a smaller (more stable) platform • Finding suitable radio technology to match applications • Making the access to video sensor data more useful • Integration with traditional sensor technologies • TinyDB for video sensors

  21. Conclusions • Low-power video sensor networking technologies • Video sensor software design • Dynamically adaptable software architecture • Disconnected or intermittent operation • More information • www.cse.ogi.edu/sysl

  22. http://www.cse.ogi.edu/sysl More information?

  23. The Rest of This Talk • The Panoptes platform • Hardware and software systems • Software architecture • A demonstration system • The Little Sister Sensor Networking Application • Experimentation • System measurements • Buffering and adaptation • Power consumption • Conclusions and future work

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