sink deployment in wireless surveillance networks

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Sink Deployment in Wireless Surveillance Networks

Michael Chien-Chun Hung, Kate Ching-Ju LinMarch 31, 2011

Network and Mobile System Lab(NMS Lab)Research Center for Information Technology Innovation

(CITI)Academia Sinica, Taipei, Taiwan

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Wireless Surveillance System (WSS)

•Meerkat•Panoptes

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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?

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Wireless Surveillance System

Camera Deployment

Camera Placement Camera Management

Sink Deployment

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Sink Deployment: Scenario 1

54 MbpsLink reliability: 85%

2 MbpsLink reliability: 65%

1 MbpsLink reliability: 50%

C

B

A

Demand rate Effective throughput

A 428 kbps

B 329 kbps

C 560 kbps

Demand rate

Effective throughput

Satisfaction ratio

A 1000 kbps 428 kbps 0.43

B 1500 kbps 329 kbps 0.22

C 750 kbps 560 kbps 0.75

Total 3250 kbps 1277 kbps 1.4

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Sink Deployment: Scenario 2

5.5 MbpsLink reliability: 50%

5.5 MbpsLink reliability: 60%

5.5 MbpsLink reliability: 60%

CA

B Demand rate

Effective throughput

Satisfaction ratio

A 1000 kbps 1100 kbps 1.1

B 1500 kbps 1100 kbps 0.73

C 750 kbps 916 kbps 1.22

Total 3250 kbps 3196 kbps 3.05

•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

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Sink Deployment

• Goal: maximize overall satisfaction of all cameras

– di: demand streaming rate of camera i

– ui: 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

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Spring-Cam ApproachC

B

A

A C

B

SUMVector

Diagram

dB = 1500 kbps

dA = 1000 kbps dC = 750 kbps

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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

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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)

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Performance Evaluation

• Parameters:– 350*350 square meter field– Demand rate between [500, 1000] kbps

• Performance metric:– Total Satisfaction :•

– Hit Ratio : • , Hk =

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Total Satisfaction

23%

Number of cameras ↑, performance metric ↓Spring-Cam outperforms average location by 23%

Advanced search ↑, performance metric ↑

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Hit Ratio

Advanced search ↑, hit ratio ↑Number of cameras ↑, hit ratio ↓

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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

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Thank You for Your Attendance!

Michael Chien-Chun Hungshinglee@citi.sinica.edu.tw

http://nms.citi.sinia.edu.tw/shinglee

Network and Mobile System Group(NMSGroup)Research Center for Innovation Technology Information (CITI)

Academia Sinica, Taipei, Taiwan

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Appendix

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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 anomaly1

– Huge throughput decrement• Rate selection in WSSs is mutually dependent

1 M. Heusse, F. Rousseau, G. Berger-Sabbatel and A. Duda, “Performance anomaly of 802.11 b” in INFOCOM’03

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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 throughput2

– : approximated achievable uploading throughput– pi: link reliability between camera i and the sink

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

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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

≒

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Spring-Cam in a nutshell

• The sink must have similar distance to all cameras

– zi: the distance between camera i and the sink

– : the average distance of all zi

• Similar to mass-spring system in Physics• A virtual spring connecting a camera and the sink– If zi > : the sink should be placed closer to camera i

– If zi < : the sink should be placed further to camera i

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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