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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.

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

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  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)

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