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Opportunistic Flooding in Low-Duty-Cycle Wireless Sensor Networks with Unreliable Links

Opportunistic Flooding in Low-Duty-Cycle Wireless Sensor Networks with Unreliable Links. Shuo Guo, Yu Gu, Bo Jiang and Tian He University of Minnesota MobiCom09. Outline. Background Motivation Network Model and Assumption Main idea System Detail Evaluation Summary. Background.

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Opportunistic Flooding in Low-Duty-Cycle Wireless Sensor Networks with Unreliable Links

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  1. Opportunistic Flooding in Low-Duty-Cycle Wireless Sensor Networks with Unreliable Links Shuo Guo, Yu Gu, Bo Jiang and Tian He University of Minnesota MobiCom09 Presenter: Jing He

  2. Outline • Background • Motivation • Network Model and Assumption • Main idea • System Detail • Evaluation • Summary

  3. Background Traffic Control Habit Monitoring Target Tracking Space Monitor Border Control Infrastructure health Monitoring • Why a low-duty-cycle WSN is needed? • Growing need for sustainable sensor networks • Slow progress on battery capacity sustainable sensor networks

  4. Background • Sleep latency in low-duty-cycle wireless sensor networks … Sender t Receiver … t Active State Dormant State Low Duty Cycle => Long Network Lifetime

  5. Motivation C B D C B D C D B A t A Active State Dormant State • Why is Flooding in low-duty-cycle WSNs different? • No longer consists of a number of broadcasts. • Instead, it consists a number of unicasts.

  6. Network Model and Assumptions • Local synchronization of sensor nodes • Pre-determined working schedules shared with all neighbors. • Unreliable wireless links • The probability of a successful transmission depends on the link quality q • Flooding packets are only forwarded to a node with larger hop-count to avoid flooding loops

  7. Design Goal B C A Two challenging issues • Redundant transmissions • Collisions Fast data dissemination: shorter flooding delay Less transmission redundancy: less energy cost

  8. Tree-based Simple Solution • Energy-Optimal Tree • No redundant transmissions • Long flooding delay

  9. Main Idea • Early Packets • Help reduce delay • SEND Decision Making • Late Packets • Redundant • DO NOT SEND for each neighbor • Early packets are forwarded to reduce delay • Late packets are not forwarded to reduce energy cost • Adding opportunistically early links into the energy-optimal routing tree

  10. How to Determine Early Packets? Q1:When will B receive A’s packet? Q2:Is this time early enough? A B By the time Dp, the probability that B has received the packet is p B’s delay distribution p-quantile EPD < Dp, SEND EPD > Dp, DO NOT SEND t Dp Delay distribution that B receives packets from its parent! Early Packets’ EPD Late Packets’ EPD Flooding delay distribution (pmf) at node B Delay threshold Dp based on a threshold probability p Expected Packet Delay (EPD) : the packet delay when B receives A’s packet

  11. How to Determine Early Packets? A B B’s delay distribution p-quantile t Dp Early Packets’ EPD Late Packets’ EPD Flooding delay distribution (pmf) at node B Delay threshold Dp based on a threshold probability p Expected Packet Delay (EPD) : the packet delay when B receives A’s packet 11

  12. Delay Distribution Computation 0.9 0.8

  13. How to Determine Early Packets? A √ B B’s delay distribution p-quantile t Dp Early Packets’ EPD Late Packets’ EPD Flooding delay distribution (pmf) at node B Delay threshold Dp based on a threshold probability p Expected Packet Delay (EPD) : the packet delay when B receives A’s packet 13

  14. Expected Packet Delay Computation B’s working schedule EPD = 24 8 16 24 A’s second try to B A receives packet Active State A’s first try to B Dormant State A is expected to transmit twice! … t

  15. How to Determine Early Packets? A √ B √ B’s delay distribution p-quantile t Dp Early Packets’ EPD Late Packets’ EPD Flooding delay distribution (pmf) at node B Delay threshold Dp based on a threshold probability p Expected Packet Delay (EPD) : the packet delay when B receives A’s packet 15

  16. Final Decision Making Dp= 16 EPD= 24 For p = 0.8 Dp = 16< EPD = 24. A will not start the transmission to B!

  17. How “early” an early packet should be? Delay Distribution p-quantile Dp t Late Packets’ EPD Early Packets’ EPD • Small p value: smaller Dp, fewer early packets, longer flooding delay, less energy cost => Energy-Sensitive • Large p value: larger Dp, more early packets, shorter flooding delay, more energy cost => Time-Sensitive

  18. Decision Conflict Resolution B C A • The selection of flooding senders • Only a subset of neighbors are considered as a node’s flooding packet senders. • Flooding senders have a good enough link quality between each other. • Avoid hidden terminal problem

  19. Decision Conflict Resolution • Link-quality based back-off scheme • Back off for a period of time when a node start to transmit • Better link quality, shorter backoff duration • Further avoids collision when two nodes can hear each other and make the same decision • Further saves energy since the node with the best link quality has the highest chance to send

  20. Evaluation • Simulation Setup • Randomly generated network, 200~1000 nodes • Randomly generated working schedules • Duty cycle from 1%~20% • Test-bed Implementation • 30 MicaZ nodes form a 4-hop network • Randomly generated working schedules • Duty cycle from 1% to 5%

  21. Evaluation • Baseline 1: optimal performance bounds • Delay optimal: collision-free pure flooding • Energy optimal: tree-based solution • Baseline 2: improved pure flooding • Two techniques are added to avoid collisions: • Link-quality based back-off scheme

  22. Simulation Results Improved Pure Flooding Flooding delay vs. Duty Cycle

  23. Simulation Results Improved Pure Flooding Improved Pure Flooding Opportunistic Flooding Flooding delay vs. Duty Cycle

  24. Simulation Results Improved Pure Flooding Improved Pure Flooding Improved Pure Flooding Opportunistic Flooding Opportunistic Flooding Optimal Delay Bound Flooding delay vs. Duty Cycle

  25. Simulation Results Improved Pure Flooding Energy Cost vs. Duty Cycle

  26. Simulation Results Improved Pure Flooding 60% Opportunistic Flooding Energy Cost vs. Duty Cycle

  27. Simulation Results Improved Pure Flooding 60% Opportunistic Flooding Optimal Energy Bound Energy Cost vs. Duty Cycle

  28. Test-bed Performance Improved Pure Flooding Opportunistic Flooding 30% Flooding delay vs. Duty Cycle Energy Cost vs. Duty Cycle

  29. Test-bed Performance Ratio of Opportunistically Early Packets Hop Count 2 Hop Count 4 Hop Count 1 Hop Count 3

  30. Summary The flooding process in low-duty-cycle networks consists of a number of unicasts. This feature calls for a new solution Opportunistically early packets are forwarded outside the energy-optimal tree to reduce the flooding delay Late packets are not forwarded to reduce energy cost Evaluation reveals the proposed approaches both close to energy- and delay-optimal bounds

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