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

1. Siphon: Overload Traffic Management using Multi-RadioVirtual Sinks in Sensor Networks Chieh-Yih Wan, Intel Research Shane B. Eisenman, Columbia University Andrew T. Campbell, Dartmouth College Jon Crowcroft, Cambridge University Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

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 Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

3. Funneling Effect • Many-to-one traffic pattern causes congestion in the routing funnel Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

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 Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

5. Congestion Collapse Results from a 55 node Mica2 indoor testbed (office environment) * From “Mitigating Congestion in Wireless Sensor Networks”, SenSys’04. Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

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 offerincreased fidelity during periods of congestion and traffic overload in sensor networks Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

7. 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 Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

8. 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 Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

9. Siphon • Add capacity on-demand by deploying a multi-radio overlay mesh based on “virtual sinks” Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

10. 1. Physical Sink initiates Virtual Sink discovery. 6 2. Virtual sink advertises according to scope. 8 1 2 7 5 • Nodes add Virtual Sink neighbor associations. 3 4 Virtual Sink Discovery Physical Sink Virtual Sink Mote VS Neighbor Armstrong Project, Columbia University http://comet.columbia.edu/armstrong Default Route

11. 1. Congestion detected. 2. Traffic redirected to neighborhood Virtual Sink. 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 Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

12. Design Considerations • Virtual Sink placement • Advertisement scope • Placement density • Guidelines on when to redirect traffic to the Virtual Sink Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

13. Virtual Sink Advertisement Scope • Simulation w/ 30 nodes • 1 Virtual Sink • Several randomized topologies Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

14. 2 – 3 Virtual Sinks needed Virtual Sink Deployment Density Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

15. Traffic Redirection Guidelines • Redirect to Virtual Sinks only when local congestion is inferred, via channel load estimate or buffer occupancy threshold. • Virtual sink neighbor must have link quality < A% worse than that of the default next hop to avoid forcing the use of lossy links. • To avoid the possibility of routing loops, Virtual Sink neighbors that are upstream from the congested node are not used. Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

16. TestBed Details • 48 Mica2 motes in a 6x8 multi-hop grid (grid calibration: 1-hop  >80%, 2-hop  <20%) • Stargate platform with IEEE 802.11b and Mica2 • TinyOS-1.1.0 (Surge, MultiHopRouter) • Performance Intuition: Energy Tax and Fidelity performance should improve with increasing load and with the addition of Virtual Sinks. Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

17. Uniform Packet Generation (where 48 nodes are srcs) 2 55% Fid. Boost 1 25% Fid. Boost 5% 45% Tax Reduction 10% 25% Tax Reduction Virtual sinks increase fidelity and energy tax savings Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

18. 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 Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

19. Energy Usage • Virtual Sink Usage Cost = Energy w/ VS Energy w/o VS Using Virtual sinks reduces the cost of delivering packets to the Physical Sink Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

20. Load Balancing • NS2 Simulation • 70 nodes uniformly dist’d • 3 Virtual Sinks randomly • 1/3 VS is the Physical Sink Residual Energy = Remaining Energy Initial Energy Complementary CDF shows the probability a given node has a residual energy higher than X% Placing Virtual Sinks spreads the traffic load more equally Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

21. Related Work • First-generation congestion control algorithms • Hierarchical Sensor Networks • XScale (expedited Delivery) • Tenet (hierarchy for scalability) • Intel • Clustering algorithms in MANET Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

22. Conclusion • Contribution • BoostsFidelity to the application during periods of traffic overload • Provides a positive Energy Tax Savings in the face of network congestion. • Interoperates with existing congestion control schemes (e.g. CODA) • Siphon algorithms more generally apply to heterogeneous/hierarchical sensor networks (storage, aggr, comp.) Armstrong Project, Columbia University http://comet.columbia.edu/armstrong

23. Thanks for listening. Contact: shane@ee.columbia.edu Armstrong Project, Columbia University http://comet.columbia.edu/armstrong