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

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, Twin Cities Shuo Guo @ University of Minnesota

2. 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 Shuo Guo @ University of Minnesota

3. Background • Sleep latency in low-duty-cycle wireless sensor networks … Sender t Receiver … t Active State Dormant State Low Duty Cycle => Long Network Lifetime Shuo Guo @ University of Minnesota

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

5. Motivation • Existing solutions are not suitable to be directly applied to low-duty-cycle wireless sensor networks Shuo Guo @ University of Minnesota X. Chen, M. Faloutsos, and S. Krishnamurthy. Power Adaptive Broadcasting with Local Information in Ad Hoc Networks. ICNP’03. J. W. Hui and D. Culler. The Dynamic Behavior of a Data Dissemination Protocol for Network Programming at Scale. SenSys’04. P. Kyasanur, R. R. Choudhury, and I. Gupta. Smart Gossip: An Adaptive Gossip-based Broadcasting Service for Sensor Networks. MASS’06. P. Levis, N. Patel, D. Culler, and S. Shenker. Trickle: A Self-Regulating Algorithm for Code Propagation and Maintenance in Wireless Sensor Networks. NSDI’04. L. Li, R. Ramjee, M. Buddhikot, and S. Miller. Network Coding-Based Broadcast in Mobile Ad-hoc Networks. INFOCOM’07. M. J. Miller, C. Sengul, and I. Gupta. Exploring the Energy-Latency Trade-Off for Broadcasts in Energy-Saving Sensor Networks. ICDCS’05. F. Stann, J. Heidemann, R. Shroff, and M. Z. Murtaza. RBP: Robust Broadcast Propagation in Wireless Networks. SenSys’06

6. Network Model and Assumptions Shuo Guo @ University of Minnesota • 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 Shuo Guo @ University of Minnesota Fast data dissemination: shorter flooding delay Less transmission redundancy: less energy cost

8. Tree-based Simple Solution Shuo Guo @ University of Minnesota • 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 Shuo Guo @ University of Minnesota • 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 Shuo Guo @ University of Minnesota 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 Shuo Guo @ University of Minnesota 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 Linear Complexity! Shuo Guo @ University of Minnesota

13. How to Determine Early Packets? A √ B B’s delay distribution p-quantile t Dp Early Packets’ EPD Late Packets’ EPD Shuo Guo @ University of Minnesota 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 A’s first try to B Active State Dormant State A is expected to transmit twice! … t Shuo Guo @ University of Minnesota

15. How to Determine Early Packets? A √ B √ B’s delay distribution p-quantile t Dp Early Packets’ EPD Late Packets’ EPD Shuo Guo @ University of Minnesota 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! Shuo Guo @ University of Minnesota

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 Shuo Guo @ University of Minnesota

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

19. Evaluation Shuo Guo @ University of Minnesota • 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 • p-persistent back-off scheme

20. Simulation Results Improved Pure Flooding Flooding delay vs. Duty Cycle Shuo Guo @ University of Minnesota

21. Simulation Results Improved Pure Flooding Improved Pure Flooding Opportunistic Flooding Flooding delay vs. Duty Cycle Shuo Guo @ University of Minnesota

22. Simulation Results Improved Pure Flooding Improved Pure Flooding Improved Pure Flooding Opportunistic Flooding Opportunistic Flooding Optimal Delay Bound Flooding delay vs. Duty Cycle Shuo Guo @ University of Minnesota

23. Simulation Results Improved Pure Flooding Energy Cost vs. Duty Cycle Shuo Guo @ University of Minnesota

24. Simulation Results Improved Pure Flooding 60% Opportunistic Flooding Energy Cost vs. Duty Cycle Shuo Guo @ University of Minnesota

25. Simulation Results Improved Pure Flooding 60% Opportunistic Flooding Optimal Energy Bound Energy Cost vs. Duty Cycle Shuo Guo @ University of Minnesota

26. Test-bed Performance Improved Pure Flooding Opportunistic Flooding 30% Flooding delay vs. Duty Cycle Energy Cost vs. Duty Cycle Shuo Guo @ University of Minnesota

27. Test-bed Performance Ratio of Opportunistically Early Packets Hop Count 2 Hop Count 4 Hop Count 1 Hop Count 3 Shuo Guo @ University of Minnesota

28. Test-bed Performance Improved Pure Flooding Opportunistic Flooding 30% Flooding delay vs. Duty Cycle Energy Cost vs. Duty Cycle Shuo Guo @ University of Minnesota 28

29. Summary Shuo Guo @ University of Minnesota 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 our design approaches both energy- and delay-optimal bounds

30. Decision Conflict Resolution Shuo Guo @ University of Minnesota • 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 without the overhead caused by using RTS/CTS control packets

31. Decision Conflict Resolution Shuo Guo @ University of Minnesota • Link-quality based back-off scheme • Better link quality, higher chance to send first • 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