1. Panoptes: A Scalable Architecture for Video Sensor Networking Applications Wu-chi Feng, Brian Code, Ed Kaiser, Mike Shea, Wu-chang Feng (OGI: The Oregon Graduate Institute) Presented by Gary Huang March 1 st, 2004

2. Outline Motivation Motivation Video Sensor Platform Video Sensor Platform Video Sensor Networking Application Video Sensor Networking Application Experimentation Experimentation Related Work Related Work Conclusion Conclusion Future Work Future Work

3. 1. Motivation Video sensor networking application requires video sensor: Video sensor networking application requires video sensor: - low power consumption - flexible enough to support a broad range of applications and environments (scalability) Panoptes sensor employed: Panoptes sensor employed: -a low-power, high-quality video capturing platform. - a prioritizing buffer management algorithm to save power. - a bit-mapping algorithm for the efficient querying and retrieval of video data.

4. Introduce Panoptes Video Sensor in terms of: Introduce Panoptes Video Sensor in terms of: -Video Sensor Platform (Design) -Video Sensor Networking Application (Implementation) -Experimentation (Performance)

5. 2. Video Sensor Platform Design Requirement Design Requirement Panoptes Sensor Hardware Panoptes Sensor Hardware Panoptes Sensor Software Architecture Panoptes Sensor Software Architecture

6. 2.1 Design Requirement Low power Low power Minimizing power can significantly increase the number of sensors that can be economically deployed. Flexible adaptive buffering Flexible adaptive buffering Video sensors should support a variety of latency and networking configurations, with a buffer on the sensor acting as the intermediate store for the date. Power Management Power Management -Turn on sensor to capture as much video as it can before the battery dies. - Turn off some components to save power when sensor is idle.

7. 2.2 Panoptes Sensor Hardware IntelStrongArm 206 MHz embedded platform IntelStrongArm 206 MHz embedded platform Logitech 3000 USB-based video camera Logitech 3000 USB-based video camera 64 Mbytes of memory 64 Mbytes of memory Linux 2.4.19 operating system kernel Linux 2.4.19 operating system kernel 802.11-based networking card 802.11-based networking card

8. Figure: Panoptes Sensor Hardware Approximately 7 inches long (with an 802 card inserted). Approximately 7 inches long (with an 802 card inserted). Approximately 4 inches wide. Approximately 4 inches wide.

9. 2.3 Panoptes Sensor Software Architecture Video Capture Video Capture Filtering Filtering Compression Compression Buffering and Adaptation Buffering and Adaptation

10. Figure: Panoptes Sensor Software Components

11. Video Capture Choose a USB-based video camera Choose a USB-based video camera Use the Phillips Web Camera interface with video for Linux Use the Phillips Web Camera interface with video for Linux

12. Filtering Filtering uninteresting data to reduce the overhead of compression or transmission. Filtering uninteresting data to reduce the overhead of compression or transmission. Allows a user to specify how and what data should be filtered. Allows a user to specify how and what data should be filtered. Use a brute-force, pixel-by-pixel algorithm that detects whether or not the video has changed over time. Unchanged frames will be filtered. Use a brute-force, pixel-by-pixel algorithm that detects whether or not the video has changed over time. Unchanged frames will be filtered.

13. Compression JPEG and differential JPEG are set up as the compression format on the Panoptes platform. JPEG and differential JPEG are set up as the compression format on the Panoptes platform. Compression on the Panoptes sensor is CPU bound. Compression on the Panoptes sensor is CPU bound. - a 320x240 4:1:1 YUV frame requires approximately 33 ms of CPU time.

14. Buffering and Adaptation Important for 3 Reasons: Important for 3 Reasons: -be able to manage transmitting video during network congestion. - save battery life for a long-lived scenario - when buffer fills up, which data should be discarded first. Priority-based Streaming Mechanism: Priority-based Streaming Mechanism: -Incoming video data are mapped to priorities defined by the applications. -If the buffer gets full, the algorithm starts discarding data based on data priorities. -It is important to note that the priority mapping can be dynamic over time.

15. Figure: A Dynamic Priority Example

16. 3. The Little Sister Sensor Networking Application The User Interface The User Interface Video Sensor Software Video Sensor Software Video Aggregation Software Video Aggregation Software

17. 3.1 The User Interface Figure: the Little Sister Sensor Networking Application

18. 3.2 Video Sensor Software A simple change detection filtering algorithm A simple change detection filtering algorithm - for event recognition. A simple bitmapping algorithm A simple bitmapping algorithm - for the efficient querying and access to the stored video data.

19. 3.3 Video Aggregation Software Responsible for the storage and retrieval of the video data between video sensors and clients Responsible for the storage and retrieval of the video data between video sensors and clients There are 3 components within a video aggregation node. There are 3 components within a video aggregation node. - Camera Manager - Query Manger - Stream Manager

20. Figure: Video Aggregation Software

21. Camera Manager Responsible for dealing with the video sensors. Responsible for dealing with the video sensors. Register video sensors. Register video sensors. Maximize scalability (using multiple Camera Managers). Maximize scalability (using multiple Camera Managers). Create an event_overview_map. Create an event_overview_map. - Purpose: create an overview of the entire event to aid in the efficient querying of the video data. - Two approaches (Union maps and Trail maps)

22. Figure: Union Map and Trail map Examples

23. Query Manager Responsible for handling requests from clients. Responsible for handling requests from clients. Users can highlight a 16x16 pixel region. Users can highlight a 16x16 pixel region. Finds all events within the system matching the region. Finds all events within the system matching the region. Returns the list of matching events to users. Returns the list of matching events to users.

24. Stream Manager Responsible for streaming events of interest to the clients. Responsible for streaming events of interest to the clients.

25. 4. Experimentation USB Performance USB Performance Compression Performance Compression Performance Component Interaction Component Interaction Power measurements Power measurements

26. 4.1 USB Performance For two different size of frames, the smaller frame has a larger transmission rate under a same compression level. For two different size of frames, the smaller frame has a larger transmission rate under a same compression level. For two same size of frames, the frame has higher compression, the transmission rate is larger. For two same size of frames, the frame has higher compression, the transmission rate is larger.

27. 4.2 Compression Performance

28. 4.3 Component Interaction

29. 4.4 Power Measurements

30. 5. Related Work Sensor Networking Research Sensor Networking Research Mobile Power Management Mobile Power Management Video Streaming Technologies Video Streaming Technologies

31. 6. Conclusion There are a number of significant contributions: There are a number of significant contributions: - Developed a low-power, high-quality video capturing platform. - Designed a prioritizing buffer management algorithm to save power - Designed a bit-mapping algorithm for the efficient querying and retrieval of video data. Buffering and adaptation algorithms manage to deal with being disconnected from the network. Buffering and adaptation algorithms manage to deal with being disconnected from the network. Experiments show that high quality video can be captured fairly while a lower power is consumed like as a standard night light running. Experiments show that high quality video can be captured fairly while a lower power is consumed like as a standard night light running.

32. 7. Future Work Assembling a sensor with a wind-powered generator for deployment along the coast of Oregon. Assembling a sensor with a wind-powered generator for deployment along the coast of Oregon. Creating an open source platform that can be used by researchers. Creating an open source platform that can be used by researchers. Working on similarity searching algorithms for the trail maps being generated. Working on similarity searching algorithms for the trail maps being generated.