Siphon: Overload Traffic Management using Multi-Radio Virtual Sinks in Sensor Networks

1. Siphon: Overload Traffic Management using Multi-Radio Virtual Sinks in Sensor Networks Chieh-Yih Wan, Intel Research, et al. SenSys ’05 Presented by Hanjoon Kim

2. The Problem • Observations • Funneling Effect limits performance • Congestion Collapse • Existing congestion control techniques are effective at reducing packet loss, but do little to help data fidelity • Result • We live in a fidelity-limited world • Broader Challenge • How do we increase fidelity in sensor networks • Siphon’s Contribution • To offerincreased fidelity during periods of congestion and traffic overload in sensor networks

3. Funneling Effect • Many-to-one traffic pattern causes congestion in the routing funnel

4. The Problem • Observations • Funneling Effect limits performance • Congestion Collapse • Existing congestion control techniques are effective at reducing packet loss, but do little to help data fidelity • Result • We live in a fidelity-limited world • Broader Challenge • How do we increase fidelity in sensor networks • Siphon’s Contribution • To offerincreased fidelity during periods of congestion and traffic overload in sensor networks

5. Existing Congestion Control Techniques • Fusion, CODA, ESRT use rate control and packet drop techniques to control congestion * From results presented in “CODA: Congestion Detection and Avoidance in Sensor Networks”, SenSys’03

6. The Problem • Observations • Funneling Effect limits performance • Congestion Collapse • Existing congestion control techniques are effective at reducing packet loss, but do little to help data fidelity • Result • We live in a fidelity-limited world • Broader Challenge • How do we increase fidelity in sensor networks • Siphon’s Contribution • To offer increased fidelity during periods of congestion and traffic overload in sensor networks

7. Siphon • Add capacity on-demand by deploying a multi-radio overlay mesh based on “virtual sinks”

8. 1. Physical Sink initiates Virtual Sink discovery. 6 2. Virtual sink advertises according to scope(using VS-TTL field). 8 1 2 7 5 • Nodes add Virtual Sink neighbor associations. 3 4 Virtual Sink Discovery Physical Sink Virtual Sink Mote VS Neighbor Default Route

9. 1. Congestion detected. (node initiated or “Post-Facto”) 2. Traffic redirected to neighborhood Virtual Sink (with redirection bit). 6 8 8 1 1 2 3. Redirected traffic sent on the overlay mesh to the Physical Sink. 7 5 3 Physical Sink 4 4 Virtual Sink Uncongested Mote Congested Mote VS Neighbor Default Route Traffic Redirection

10. Design Considerations • Virtual Sink placement • Advertisement scope (VS-TTL setting) • Placement density (How many VSs needed) • Guidelines on when to redirect traffic to the Virtual Sink • Congestion threshold • Detection method (node initiated, “Post-Facto”)

11. Virtual Sink Advertisement Scope • Simulation w/ 30 nodes • 1 Virtual Sink • Several randomized topologies

12. 2 – 3 Virtual Sinks needed Virtual Sink Deployment Density

13. Traffic Redirection Guidelines • 70 % is appropriate threshold in this simulation • But in real world 20-30% is appropriate

14. Traffic Redirection Guidelines

15. TestBed Details • 48 Mica2 motes in a 6x8 multi-hop grid • Stargate platform with IEEE 802.11b and Mica2 • TinyOS-1.1.0 (Surge, MultiHopRouter)

16. Result on-demand always-on virtual sinks Virtual sinks increase fidelity and energy tax savings

17. Sparse Packet Generation (where 3 nodes are srcs) 20% Fidelity Boost • Generic data dissemination app. • Results avg. 5 arbitrary placements of 1 Virtual Sink 2x reduction in pkt loss Siphon provides improved performance versus rate-limit/pkt drop techniques

18. Load Balancing Residual Energy = Remaining Energy Initial Energy • NS2 Simulation • 70 nodes uniformly dist’d • 3 Virtual Sinks randomly • 1/3 VS is the Physical Sink Complementary CDF shows the probability a given node has a residual energy higher than X% Placing Virtual Sinks spreads the traffic load more equally

19. Conclusion • Contribution • BoostsFidelity to the application during periods of traffic overload • Provides a positive Energy Tax Savings in the face of network congestion.

20. Thanks for listening.