1. SensEye: A Multi-Tier Camera Sensor Network by Purushottam Kulkarni, Deepak Ganesan, Prashant Shenoy, and Qifeng Lu Presenters: Yen-Chia Chen and Ivan Pechenezhskiy EE225B (March 17, 2011)

2. Cameras and Sensor Platforms Sensor platforms Cameras Kulkarni et al, In Proc. of ACM NOSSDAV, pages 141–146, 2005.

3. Previous Work Power Management –Wakeup-on-wireless & Turducken (always-on) Multimedia Sensor Network –Panoptes (a video-based single-tier sensor network) Sensor Placement –Solvable optimization problem Video Surveillance –Techniques for target detection, classification, and tracking –Systems with central control unit

4. Motivation Applications –Environmental monitoring –Ad-hoc surveillance Constraints –No human interference –Battery-powered deployment

5. Multi-Tier Sensor Network Single-Tier Network vs. Multi-Tier Network –reduces power consumption –achieves similar performance Benefits: –Low cost –High coverage –High reliability –High functionality

6. SensEye: Multi-Tier Camera Network Achieve low latencies without sacrificing energy- efficiency Tasks: object detection, recognition and tracking Exploits redundancies in camera coverage (e.g. object localization)

7. General Design Principles Map each task to the least powerful tier with sufficient resources Exploit wakeup-on-demand Exploit redundancy in coverage

8. System Design—Object Detection Performed at the most energy-efficient tier (Tier 1) Detection via frame differencing Randomized duty-cycling algorithm

9. System Design—Object Localization Calculation of the vector along which the centroid of an object lies

10. System Design—Object Localization Involves two rotations and one translation Transformation to the global coordinate frame Triangulation

11. System Design—Inter-Tier Wakeup Localization by tier 1 is used to decide which tier 2 nodes to wake up Wakeup packet to node 2, similar to wake-on-wireless Reduce the duration of wakeup: Tier 2 runs at bare minimum when suspended

12. System Design— Recognition and Tracking Recognition algorithm executed at tier 2 It is assumed any object recognition algorithm can be employed in SensEye Tracking involves detection, localization, and inter-tier wakeup

13. Hardware Architecture Camera Sensors Sensor Platforms

14. Hardware Architecture Tier 1: –lower-power camera sensors (Cyclop or CMUcam) –low-power sensor platform (Mote) Tier 2: –webcams (Logitech) –sensor platform (Intel Stargate), low-power wakeup circuit (Mote) Tier 3: –high-performance PZT camera and mini-ITX embedded PC (Sony)

15. Hardware Architecture

16. Software Architecture (Proposed)

17. Software Architecture (Implemented) CMUcam Frame Differentiator Mote-Level Detector Wakeup Mote High Resolution Object Detection and Recognition PTZ Controller

18. CMUcam Frame Differentiator CMUcam image capture is triggered by Mote-Level Detector Detection is achieved by differencing with reference background frame (non-zero areas correspond to object) Two differencing modes: initial image (88x143 or 176x255) is converted to a 8x8 or 16x16 grid

19. Mote-Level Detector Sends initialization commands Sends sampling signal to CMUcam Gets the frame difference from CMUcam Decides whether an event occur Broadcasts a trigger to the higher tier if an even occur Sleeps, on no event detection Duty-cycles CMUcam

20. Wakeup Mote Receives Triggers from the lower tier Motes Computes the coordinates of the detected object Decides whether to wakeup Stargate

21. High Resolution Object Detection and Recognition by Stargate Frame differencing Image smoothing Obtaining an average value of the red, green and blue components of the object Matching against a library of objects

22. Experimental Evaluation Component Benchmarks –Latency and Energy Consumption –Localization Accuracy SensEye vs. Single-Tier Network –Coverage –Energy Usage –Sensing Reliability –Sensitivity to System Parameters

23. Latency and Energy Consumption Tier 1: –Cyclope –CMUcam Tier 2: –webcam

24. Latency and Energy Consumption Tier 1: –Cyclope –CMUcam Tier 2: –webcam 4 sec4.7 J

25. Localization Accuracy

26. Experimental Evaluation: Sensor Placement and Coverage wall 3m x 1.65m Object appearance time: 7 sec Interval between appearance: 30 sec Only one object at any time 50 object appearances Tier 1 Motes sampling period: 5 sec

27. Network Energy Usage ~470 J ~2900 J (SensEye) (Single Tier)

28. Sensing Reliability Single-tier system detected 45 out of the 50 objects SensEye detected 42 (46 with the use of PZT)

29. Sensitivity to System Parameters

30. Conclusion A well-design multi-tier camera sensor network might have significant benefits over a single-tier camera network General principles for multi-tier sensor network design have been proposed It has been experimentally demonstrated that a multi-tier network can achieve about an order of magnitude reduction in energy usage without sacrificing reliability

31. Thank you!

32. Power Management wake-on-wireless –Separation of the control channel and the data channel –Incoming radio signal to wake up power-off devices Turducken –Multi-tier structure that uses a lower tier to wake up a higher tier

33. Multimedia Sensor Network Panoptes –Video-based sensor network –Single-tier, similar to tier 2 in SensEye –Incorporates compression, buffering and filtering (can be used by tier 2)