1. 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. 2 / 20 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

3. 3 / 20 Funneling Effect  Many-to-one traffic pattern causes congestion in the routing funnel

4. 4 / 20 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

5. 5 / 20 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. 6 / 20 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. 7 / 20 Siphon  Add capacity on-demand by deploying a multi-radio overlay mesh based on “virtual sinks”

8. 8 / 20 1 7 6 8 4 5 2 3 Virtual Sink Discovery Physical Sink Virtual Sink Mote VS Neighbor Default Route 2. Virtual sink advertises according to scope(using VS-TTL field). 1. Physical Sink initiates Virtual Sink discovery. 3.Nodes add Virtual Sink neighbor associations.

9. 9 / 20 Traffic Redirection Physical Sink Virtual Sink Uncongested Mote VS Neighbor Default Route Congested Mote 7 6 5 2 3 1 8 4 1. Congestion detected. (node initiated or “Post-Facto”) 1 8 4 2. Traffic redirected to neighborhood Virtual Sink (with redirection bit). 3. Redirected traffic sent on the overlay mesh to the Physical Sink.

10. 10 / 20 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. 11 / 20 Virtual Sink Advertisement Scope Simulation w/ 30 nodes 1 Virtual Sink Several randomized topologies

12. 12 / 20 Virtual Sink Deployment Density 2 – 3 Virtual Sinks needed

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

14. 14 / 20 Traffic Redirection Guidelines

15. 15 / 20 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. 16 / 20 Result Virtual sinks increase fidelity and energy tax savings always-on virtual sinks on-demand

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

18. 18 / 20 Load Balancing Residual Energy = Remaining Energy Initial Energy Placing Virtual Sinks spreads the traffic load more equally 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%

19. 19 / 20 Conclusion  Contribution  Boosts Fidelity to the application during periods of traffic overload  Provides a positive Energy Tax Savings in the face of network congestion.

20. 20 / 20 Thanks for listening.