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Fei Yang, Isabelle Augé-Blum

Delivery ratio-maximized wakeup scheduling for ultra-low duty-cycled WSNs under real-time constraints. Fei Yang, Isabelle Augé-Blum National Institute of Applied Sciences of Lyon in the Telecommunications department ( 法國里昂國立應用科學學院 ). Computer Networks 2011. 898410120 陳正昌 2011/03/28.

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Fei Yang, Isabelle Augé-Blum

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  1. Delivery ratio-maximized wakeup scheduling for ultra-low duty-cycled WSNs under real-time constraints Fei Yang, Isabelle Augé-Blum National Institute of Applied Sciences of Lyon in the Telecommunications department (法國里昂國立應用科學學院) Computer Networks 2011 898410120 陳正昌 2011/03/28

  2. Outline Introduction and Goals Wakeup Scheduling Algorithm Performance Evaluation Conclusions

  3. Outline Introduction and Goals Wakeup Scheduling Algorithm Performance Evaluation Conclusions

  4. Introduction • WSNs have been widely used in many applications. • The data flows of WSN applications can be mainly classified into four types • Event-driven • Query-driven • Continuous • Hybrid

  5. Introduction • The characteristics of event-driven WSN applications are • Not have data most of the time • Have to report to the sink with real-time constraints • Nodes spend most of the time on idle listening.

  6. Introduction • Typical power consumptions for an IEEE 802.15.4 radio (CC2420). • Transmit：52.2mW • Receive：56.4mW • Listen：56.4mW • Sleep：3 W • Sensor nodes are battery-powered • Energy saving is an important issue in WSNs.

  7. Introduction • Duty-cycled approachcan prolongs the sensor lifetime. Scheduling period … Time

  8. Introduction • Duty-cycle will negatively affect other performances • End-to-end delay • Connectivity • Although some existing scheduling algorithms can reduce the end-to-end delay • Didn’t takes routing into account • Didn’t have a bounded delay • Didn’t takes unreliable links into account

  9. Goals • Proposes a novel forwarding scheme for ultra-low duty-cycle WSNs. • Improve the energy efficiency • Decrease end-to-end delay • Increase delivery ratio • Guarantee bounded delay on the messages • Distributed scheduling

  10. Outline Introduction and Goals Wakeup Scheduling Algorithm Performance Evaluation Conclusions

  11. Network Assumptions • All nodes are locally synchronized with their neighbors. • Only one node sends the alarm when the event happens. • One duty-cycle period is divided into many slots and have same duration. • Each node wakes up for only one slot during one period. • The node can wake up for more than one slot when it has packets to send. B A B A B A … Time

  12. 31 slots Expected Delivery Ratio … … Basic Idea Time Slots 16~20 Slots 21~25 Slots 26~30 Slot 1 0.9 Slots 1~5 Slots 6~10 Slots 11~15 Slot 31 Sink Slot 2 0.8 0.7 Slot 3 Slot 4 0.6 0.5 Slot 5

  13. Wakeup Scheduling Algorithm Hop Count (HC) Expected Delivery Ratio (EDR)

  14. Wakeup Scheduling Algorithm Hop Count (HC) ∞ ∞ 3 1 α=0.5 0.3 C A 0.9 0 Sink 0.6 0.8 0.8 ∞ 1 2 ∞ D B 0.8

  15. Wakeup Scheduling Algorithm 95 π(FD)={B, C, A} Expected Delivery Ratio (EDR) A EDR(π(FD))= 0.6*0.4 +(1-0.6)*0.7*0.6 +(1-0.6)*(1-0.7)*0.8*0.7 0.8 0.7 94 100 0.6 0.7 D C Sink =0.4752 0.4 0.6 93 B

  16. Wakeup Scheduling Algorithm 93 π(FD)={A, C, B} Expected Delivery Ratio (EDR) A EDR(π(FD))= 0.8*0.7 +(1-0.8)*0.7*0.6 +(1-0.8)*(1-0.7)*0.6*0.4 =0.6584 0.8 0.7 94 100 0.6 0.7 D C Sink > 0.4752 0.4 0.6 95 B

  17. … … Wakeup Scheduling Algorithm Time Hop Count (HC) Expected Delivery Ratio (EDR) Wakeup Slot (WS) Selection Selectable Range (SR) T

  18. Wakeup Scheduling Algorithm Hop Count (HC) Expected Delivery Ratio (EDR) 0~1 Wakeup Slot (WS) Selection

  19. Wakeup Scheduling Algorithm HCupbound=6 54 slots … 089171826273536444553 Wakeup Slot (WS) Selection Time Sink HC=6 HC=5 HC=4 HC=3 HC=2 HC=1

  20. Wakeup Scheduling Algorithm HCupbound=6 54 slots … 089171826273536444553 Wakeup Slot (WS) Selection Time EDRA=0.8 A Slot T Sink EDRB=0.4 B (HC=0, EDR=1) EDRC=0.6 C HC=6 HC=5

  21. Wakeup Scheduling Algorithm Hop Count (HC) Expected Delivery Ratio (EDR) Wakeup Slot (WS) Selection

  22. 54 slots … … Wakeup Scheduling Algorithm Time (HCi, EDRi, WSi) (1, 0.95, ∞) (0, 1, 54) Slots 45~53 Sink (1, 0.9, ∞)

  23. 54 slots … … Wakeup Scheduling Algorithm Time (HCi, EDRi, WSi) (2, 0.92, 38) (2, 0.92, ∞) (2, 0.9, ∞) (2, 0.9, 39) (1, 0.95, 47) (0, 1, 54) Slots 36~44 Slots 45~53 Sink (2, 0.88, ∞) (2, 0.88, 40) (1, 0.9, 50) (2, 0.86, ∞) (2, 0.86, 41)

  24. 54 slots … … Wakeup Scheduling Algorithm Time (3, 0.89, 28) (3, 0.89, ∞) (HCi, EDRi, WSi) (2, 0.92, 38) (3, 0.88, 29) (3, 0.88, ∞) (2, 0.9, 39) (1, 0.95, 47) (0, 1, 54) Slots 27~35 Slots 36~44 Slots 45~53 Sink (3, 0.85, ∞) (3, 0.85, 31) (2, 0.88, 40) (1, 0.9, 50) (3, 0.83, 32) (3, 0.83, ∞) (2, 0.86, 41) (3, 0.8, ∞) (3, 0.8, 33)

  25. 54 slots … … Wakeup Scheduling Algorithm Time (6, 0.75, 2) (4, 0.85, 19) (5, 0.80, 11) (3, 0.89, 28) (HCi, EDRi, WSi) (2, 0.92, 38) (6, 0.73, 3) (4, 0.83, 20) (3, 0.88, 29) (5, 0.78, 12) (2, 0.9, 39) (1, 0.95, 47) (0, 1, 54) Slots 27~35 Slots 36~44 Slots 45~53 Slots 0~8 Slots 9~17 Slots 18~26 Sink (4, 0.82, 21) (6, 0.70, 5) (3, 0.85, 31) (5, 0.76, 13) (2, 0.88, 40) (1, 0.9, 50) (6, 0.68, 6) (5, 0.75, 14) (4, 0.78, 23) (3, 0.83, 32) (2, 0.86, 41) (4, 0.76, 24) (3, 0.8, 33) (6, 0.6, 7) (5, 0.73, 16)

  26. 54 slots … … Wakeup Scheduling Algorithm Time (6, 0.75, 2) (4, 0.85, 19) (5, 0.80, 11) (3, 0.89, 28) (HCi, EDRi, WSi) (2, 0.92, 38) (6, 0.73, 3) (4, 0.83, 20) (3, 0.88, 29) (5, 0.78, 12) (2, 0.9, 39) 12 20 29 39 47 (1, 0.95, 47) 54 (0, 1, 54) Slots 27~35 Slots 36~44 Slots 45~53 Slots 0~8 Slots 9~17 Slots 18~26 Sink (4, 0.82, 21) (6, 0.70, 5) (3, 0.85, 31) (5, 0.76, 13) (2, 0.88, 40) (1, 0.9, 50) (6, 0.68, 6) (5, 0.75, 14) (4, 0.78, 23) (3, 0.83, 32) (2, 0.86, 41) (4, 0.76, 24) (3, 0.8, 33) (6, 0.6, 7) (5, 0.73, 16)

  27. Outline Introduction and Goals Wakeup Scheduling Algorithm Performance Evaluation Conclusions

  28. Performance Evaluation

  29. Performance Evaluation

  30. Performance Evaluation Sink HC=6 HC=5 HC=4 HC=3 HC=2 HC=1

  31. Performance Evaluation

  32. Performance Evaluation

  33. Performance Evaluation

  34. Performance Evaluation

  35. Performance Evaluation

  36. Performance Evaluation

  37. Performance Evaluation

  38. Performance Evaluation

  39. Performance Evaluation

  40. Performance Evaluation

  41. Outline Introduction and Goals Wakeup Scheduling Algorithm Performance Evaluation Conclusions

  42. Conclusion • Proposes a novel forwarding scheme for ultra-low duty-cycle WSNs. • Improve the energy efficiency • Decrease end-to-end delay • Maximizes the delivery ratio • Distributed scheduling • Highly suitable for ultra-low duty-cycle real-time event-driven WSN

  43. T h a n k s ~ ~ ~ T T h h a a n n k k s s ~ ~ ~ ~ ~ ~

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