1 / 24

Data Forwarding in Extremely Low Duty-Cycle Sensor Networks with Unreliable Communication Links

Data Forwarding in Extremely Low Duty-Cycle Sensor Networks with Unreliable Communication Links. Yu Gu and Tian He Minnesota Embedded Sensor System (MESS) Department of Computer Science & Engineering http://mess.cs.umn.edu. This work is supported by National Science Foundation.

prestone
Télécharger la présentation

Data Forwarding in Extremely Low Duty-Cycle Sensor Networks with Unreliable Communication Links

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Forwarding in Extremely Low Duty-Cycle Sensor Networks with Unreliable Communication Links Yu Gu and Tian He Minnesota Embedded Sensor System (MESS) Department of Computer Science & Engineering http://mess.cs.umn.edu This work is supported by National Science Foundation

  2. Yu Gu@SenSys’07 Sleep Latency in Low Duty-Cycle Sensor Networks Sleep now. Wake up in 57seconds Sleep now. Wake up in 35 seconds D B 57s latency 35s latency A 13s latency 4s latency E C Sleep now. Wake up in 4 seconds Sleep now. Wake up in 13 seconds

  3. Yu Gu@SenSys’07 Unreliable Radio Links D B 70% 90% A 50% 95% C E

  4. Yu Gu@SenSys’07 State-of-the-art Solutions: ETX (MobiCom’03) ETX only considers link quality ETX = 1/0.5 + 1/0.5 = 4 B 50%, 100s 50%, 100s Expected E2E delay is 400s Sole link quality based solutions cannot help reduce E2E delay in extremely low-duty cycle sensor networks! A D Expected E2E delay is 50s 40%, 10s 40%, 10s C ETX = 1/0.4 + 1/0.4 = 5

  5. Yu Gu@SenSys’07 State-of-the-art Solutions: DESS (INFOCOM’05) DESS = 10 + 10 = 20s DESS only considers sleep latency B 10%, 10s 10%, 10s Expected E2E delay is 200s Sole sleep latency based solutions cannot help reduce E2E delay in extremely low-duty cycle sensor networks! D A Expected E2E delay is 40s 100%, 20s 100%, 20s C DESS = 20 + 20 = 40s

  6. Yu Gu@SenSys’07 State-of-the-art Solutions (2) 80 fold performance degradation! 20 fold performance degradation! Only Consider impact of Duty Cycling Only Consider impact of link qualities Intelligent MAC protocols (B-MAC, S-MAC, SCP-MAC …) provide significant performance improvement at the MAC layer. We focus on further performance improvement at the network layer.

  7. Yu Gu@SenSys’07 Outline • Motivation • Network Model • DSF Design • Evaluation • Conclusion

  8. Yu Gu@SenSys’07 Sensor States Representation • Scheduling Bits • (10110101)* • Switching Rate • 0.5HZ 16s round time 1 0 1 1 0 1 0 1 Off On

  9. Yu Gu@SenSys’07 Data Delivery Process ( 1 0 0 0 0 0 0 0 0 0 )* ( 0 1 0 0 0 0 0 0 0 0 )* ( 0 0 0 1 0 0 0 0 0 0 )* ( 0 0 0 0 0 0 1 0 0 0 )* 1 2 3 4 Sleep latency is 1 Sleep latency is 2 Sleep latency is 3 E2E Delay is 6

  10. Yu Gu@SenSys’07 Main Idea Sleep latency is 1 1st attempt: Sleep latency is 1 We should try a sequence of forwarding nodes instead of a fixed forwarding node! ( 1 0 0 0 0 0 0 0 0 0 )* ( 0 1 0 0 0 0 0 0 0 0 )* ( 0 0 0 1 0 0 0 0 0 0 )* ( 0 0 0 0 0 0 1 0 0 0 )* 1 2 3 4 ( 0 0 1 0 0 0 0 0 0 0 )* 5 Dynamic Switching-based Forwarding (DSF) is important in extremely low duty-cycle sensor networks. ith attempt: Sleep latency is 1 + 10 * (i-1) 2nd attempt: Sleep latency is 1 + 10 =11 2nd attempt: Sleep latency is 1 + 1 =2

  11. Yu Gu@SenSys’07 Disaster Response Traffic Control Assisted Living Environmental Monitoring Habit Monitoring Space Monitor Target Tracking Precision Agriculture Border Control Optimization Objectives • EDR: Expected Delivery Ratio • EED: Expected End-to-End Delay • EEC: Expected Energy Consumption

  12. Yu Gu@SenSys’07 Optimization Objectives(1) : EDR Forwarding Sequence EDR: Expected Delivery Ratio. 2 (010)* EDR = 70% (100)* 60% 1 3 EDR for node 1 is (EDR1): (001)* EDR = 80% 50% 0.6*0.7 + (1-0.6)*0.5*0.8 40% + (1-0.6)*(1-0.5)*0.4*0.9 4 (100)* EDR = 90%

  13. Yu Gu@SenSys’07 Optimization Objectives(2) • EDR: Expected Delivery Ratio • EED: Expected End-to-End Delay • EEC: Expected Energy Consumption

  14. Yu Gu@SenSys’07 Optimizing EDR Shall we try all available neighbors? If both node 2 and node 3 are selected as forwarding nodes: EDR1 = 1 * 0.7 = 0.7 2 (010)* EDR = 70% (100)* 100% We should only choose a subset of neighboring nodes as forwarding nodes! 1 100% If only node 3 is selected as forwarding node: EDR1 = 1 * 0.8 = 0.8 3 (001)* EDR = 80%

  15. Yu Gu@SenSys’07 Optimizing EDR with dynamic programming Try or skip 2 Select only a subset of neighbors as forwarders (010)* EDR = 70% (100)* 60% Try or skip Node 4 has to be selected 1 3 (001)* EDR = 80% 50% Then we attempt to add more nodes into the forwarding sequence backwardly. 40% Try or drop 4 (100)* EDR = 90%

  16. Yu Gu@SenSys’07 Distributed Implementation • EDR = 99%, EED = 15, EEC = 2 • EDR = 98%, EED = 2, EEC = 1 1 3 • EDR = 100%, EED = 0, EEC = 0 sink 2 4 • EDR = 97%, EED = 20, EEC = 5 • EDR = 90%, EED = 90, EEC = 12

  17. Yu Gu@SenSys’07 Interesting Findings • Temporary routing loops may be helpful on reducing E2E Delay (111111)* (010000)* 1 2 (100%,1) (90%,1) (111111)* 5 (100%,1) 3 (90%,1) 4 (100%,1) (111111)* (000010)*

  18. Yu Gu@SenSys’07 Outline • Motivation • Network Model • DSF Design • Evaluation • Conclusion

  19. Yu Gu@SenSys’07 Evaluations • Both testbed implementation and large-scale simulations • Baseline solutions: • ETX by Douglas S.J. De Couto et al. in Mobicom’03 • PRR*D by Karim Seada et al. in SenSys’04 • DESS by Gang Lu et al. in INFOCOM’05

  20. Yu Gu@SenSys’07 Testbed Results 20 MicaZ nodes, 27,398 bytes code memory and 1,137 bytes data memory

  21. Yu Gu@SenSys’07 DSF Simulation Results (1)

  22. Yu Gu@SenSys’07 DSF Simulation Results (2) DSF converges to DESS at perfect link

  23. Yu Gu@SenSys’07 DSF and ETX Simulation Results (3)

  24. Yu Gu@SenSys’07 Conclusion • A Dynamic Switch-based Forwarding (DSF) scheme for extremely low duty-cycle sensor networks • Addressed both sleep latency and lossy radio links • Dynamic switching is essential • Distributed model for data delivery ratio (EDR), E2E delay (EED) and energy consumption (EEC). • Optimal forwarding on these three metrics • A generic metrics that converge to ETX (in always-awake networks) and DESS (in perfect-link networks)

More Related