Panoptes: Low-Power, Scalable Video Sensor Networking Technologies
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Transcript of Panoptes: Low-Power, Scalable Video Sensor Networking Technologies
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 EngineeringOGI School of Science and Engineering at OHSU
www.cse.ogi.edu/sysl
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
www.cse.ogi.edu/sysl
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
www.cse.ogi.edu/sysl
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
www.cse.ogi.edu/sysl
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
www.cse.ogi.edu/sysl
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
www.cse.ogi.edu/sysl
The Panoptes Platform
Picking a platform Berkeley Motes COTS web cameras General embedded CPU platforms
USB-basedvideo
206 MHz IntelStrongArm
EmbeddedLinux
802.11wireless
320x240 video22 fps software compressed~5.5 Watts maximum
www.cse.ogi.edu/sysl
The Panoptes Platform
Video SensorArchitecture Buffering and
Adaptation
Supports disconnected or intermittent operation
Priority mapping of streaming data elements
Video 4Linux
Compression
IPP-based
Currently: JPEG, Diff JPEG, Cond. Replenishment
Application-Specific Filtering
Event-detectionTime-elapsed imagesComputer vision
TimePower Management
www.cse.ogi.edu/sysl
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?
Sensor streaming
• Any data might be good
• Buffering can be used
• Some data unknown a priori
• Inverse multicast
www.cse.ogi.edu/sysl
Experimentation
The USB bottleneck
Compression performance on Panoptes
Buffering and adaptation performance
Power measurements
www.cse.ogi.edu/sysl
USB Capture Performance
Image
size
USB
Comp.
Frame Rate % Sys
CPU
160x120 0 29.64 4
1 29.77 22
3 29.88 16
320x240 0 4.88 3
1 28.72 67
3 29.68 45
640x480 0 - -
1 14.14 84
3 14.73 78
www.cse.ogi.edu/sysl
USB Capture Performance
6.9 Mbps
Image
size
USB
Comp.
Frame Rate % Sys
CPU
160x120 0 29.64 4
1 29.77 22
3 29.88 16
320x240 0 4.88 3
1 28.72 67
3 29.68 45
640x480 0 - -
1 14.14 84
3 14.73 78
www.cse.ogi.edu/sysl
USB Capture Performance
111 Mbps
Image
size
USB
Comp.
Frame Rate % Sys
CPU
160x120 0 29.64 4
1 29.77 22
3 29.88 16
320x240 0 4.88 3
1 28.72 67
3 29.68 45
640x480 0 - -
1 14.14 84
3 14.73 78
www.cse.ogi.edu/sysl
USB Capture Performance
27.6 Mbps
Image
size
USB
Comp.
Frame Rate % Sys
CPU
160x120 0 29.64 4
1 29.77 22
3 29.88 16
320x240 0 4.88 3
1 28.72 67
3 29.68 45
640x480 0 - -
1 14.14 84
3 14.73 78
www.cse.ogi.edu/sysl
Software Compression Performance
Image
size
IPP
(ms)
ChenDCT
(ms)
320x240 26.65 73.69
640x480 105.84 291.28
Image
size
IPP
(ms)
ChenDCT
(ms)
320x240 19.41 52.96
640x480 77.42 211.42
www.cse.ogi.edu/sysl
Capture / Compression Performance
Image
size
IPP
(ms)
ChenDCT
(ms)
320x240 29.20 80.63
640x480 115.42 319.71
Image
size
IPP
(ms)
ChenDCT
(ms)
320x240 20.95 57.31
640x480 83.95 228.42
www.cse.ogi.edu/sysl
Buffering and Adaptation
0
5
10
15
20
25
0 50 100 150 200 250
Time (seconds)
Fra
me r
ate
(fp
s)
www.cse.ogi.edu/sysl
Power Consumption
0
1
2
3
4
5
6
7
0 10 20 30 40 50
Time (seconds)
Pow
er
(watt
s)
Camera on(capturing)
Camerastandby
Networkconnected
Camera on/net. connected
All services running
CPUloop
SystemIdle
Standby
www.cse.ogi.edu/sysl
A Demonstration System
The Little Sister Sensor Networking Application
Network
Cam
era
Manager(
s)
Query Manager
StreamManager
Network
www.cse.ogi.edu/sysl
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
www.cse.ogi.edu/sysl
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
www.cse.ogi.edu/sysl
www.cse.ogi.edu/sysl
www.cse.ogi.edu/sysl
More information?
http://www.cse.ogi.edu/sysl
www.cse.ogi.edu/sysl
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