1. Sink Deployment in Wireless Surveillance Networks Michael Chien-Chun Hung, Kate Ching-Ju Lin March 31, 2011 1 Network and Mobile System Lab(NMS Lab) Research Center for Information Technology Innovation (CITI) Academia Sinica, Taipei, Taiwan

2. Wireless Surveillance System (WSS) 2 Meerkat Panoptes

3. Camera Deployment Camera placement – Environment-dependent Location: where to put? Angle: how to place ? Camera management – Application-dependent Resolution: how to set? Tracking: how to group? 3

4. 4 Wireless Surveillance System Camera Deployment Camera PlacementCamera Management Sink Deployment

5. Sink Deployment: Scenario 1 5 54 Mbps Link reliability: 85% 2 Mbps Link reliability: 65% 1 Mbps Link reliability: 50% C B A Demand rateEffective throughput A428 kbps B329 kbps C560 kbps Demand rate Effective throughput Satisfaction ratio A1000 kbps428 kbps0.43 B1500 kbps329 kbps0.22 C750 kbps560 kbps0.75 Total3250 kbps1277 kbps1.4

6. Sink Deployment: Scenario 2 6 5.5 Mbps Link reliability: 50% 5.5 Mbps Link reliability: 60% 5.5 Mbps Link reliability: 60% C A B Demand rate Effective throughput Satisfaction ratio A1000 kbps1100 kbps1.1 B1500 kbps1100 kbps0.73 C750 kbps916 kbps1.22 Total3250 kbps3196 kbps3.05 Camera’s demand rate The sink should be closer to the camera with higher demand Each camera should utilize the same bit-rate The sink should have similar distance to all cameras

7. Sink Deployment Goal: maximize overall satisfaction of all cameras – d i : demand streaming rate of camera i – u i : effective throughput of camera i Similar to circumcenter in a polygon – Circumcenter may not exist in general case Exhausted search is achievable – Enormous deployment-cost 7

8. Spring-Cam Approach 8 C B A A C B SUM Vector Diagram d B = 1500 kbps d A = 1000 kbpsd C = 750 kbps

9. Spring-Cam Framework Step 1: Initialization – Origin (corner) – Central point – Average point – Average point weighted by the camera’s demand – Random Step 2: Adjustment – Move the sink according to the net force of the mass-spring system 9

10. Spring-Cam Framework (Cont.) Step 3: Termination – When the potential energy cannot be further reduced Step 4: Advanced search – (x,y): the result of step 3 – Spring-Cam+5 returns the best result within (x ± 5, y ± 5) 10

11. Performance Evaluation Parameters: – 350*350 square meter field – Demand rate between [500, 1000] kbps Performance metric: – Total Satisfaction : – Hit Ratio :, Hk = 11

12. Total Satisfaction 12 23 % Number of cameras ↑, performance metric ↓ Spring-Cam outperforms average location by 23% Advanced search ↑, performance metric ↑

13. Hit Ratio 13 Advanced search ↑, hit ratio ↑ Number of cameras ↑, hit ratio ↓

14. Conclusion Introducing sink deployment problem – Maximizing the overall satisfaction of all cameras Proposing Spring-Cam – Locating the sink that satisfies each camera’s demand – Reducing the overhead of exhausted search 14

15. Thank You for Your Attendance! 15 Michael Chien-Chun Hung shinglee@citi.sinica.edu.twhttp://nms.citi.sinia.edu.tw/shinglee Network and Mobile System Group(NMSGroup) Research Center for Innovation Technology Information (CITI) Academia Sinica, Taipei, Taiwan

16. Appendix 16

17. Sink deployment Topology-dependent – Supplementary to camera deployment Multiple bit-rates supported by IEEE 802.11 – Auto rate-selection based on transmission quality – Distance to the sink significantly affect SNR 802.11 performance anomaly 1 – Huge throughput decrement Rate selection in WSSs is mutually dependent 17  1 M. Heusse, F. Rousseau, G. Berger-Sabbatel and A. Duda, “Performance anomaly of 802.11 b” in INFOCOM’03

18. System overview Independent rate selection for each camera – The quality of the link between itself and the sink Multi-path fading 、 interference 、 channel fading – Focus on channel fading determined by the distance The impact of 802.11 performance anomaly – All cameras obtain similar throughput 2 – : approximated achievable uploading throughput – p i : link reliability between camera i and the sink 18  2 K.-J. Lin and C. fu Chou, “Exploiting multiple rates to maximize the throughput of wireless mesh networks,” IEEE Transactions on Wireless Communications, 2009

19. System overview (cont.) Maximizing equals to minimizing – By AM-GM Inequality Property: – Maximum: when all cameras use the same bit-rate Bit-rate selection mainly bases on the distance – The sink must have similar distance to all cameras 19 ≒

20. Spring-Cam in a nutshell The sink must have similar distance to all cameras – z i : the distance between camera i and the sink – : the average distance of all z i Similar to mass-spring system in Physics A virtual spring connecting a camera and the sink – If z i > : the sink should be placed closer to camera i – If z i < : the sink should be placed further to camera i 20

21. Spring-Cam Overview Utilizing mass-spring operations – Virtual spring between the sink and each camera – Demand rate as elasticity coefficient. Efficient in locating possible position – Promptly converge to a potential point Supplementary to exhausted search – Reduce search cost 21