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


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


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


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


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


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


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


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


Experimentation

The USB bottleneck

Compression performance on Panoptes

Buffering and adaptation performance

Power measurements


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



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



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



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


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


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


Buffering and Adaptation

0

5

10

15

20

25

0 50 100 150 200 250

Time (seconds)

Fra

me r

ate

(fp

s)


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


A Demonstration System

The Little Sister Sensor Networking Application

Network

Cam

era

Manager(

s)

Query Manager

StreamManager

Network


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


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


More information?

http://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

panoptes: low-power, scalable video sensor networking technologies

Documents

traditional sensor

video data

sensor buffer

edusyslthe panoptes

time deliveryall data

edusyslthe rest

adaptationsensor streaming

traditional scalar