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Similar a SOFA communication protocol (EWSN 2014)
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SOFA communication protocol (EWSN 2014)
- 1. 1Challenge the future SOFA: Communication in Extreme Wireless Sensor Networks Marco Cattani, M. Zuniga, M. Woehrle, K. Langendoen Embedded Software Group, Delft University of Technology
- 2. 2Challenge the future Motivation We want to monitor the density of a crowd during an outdoor festival using low-cost wireless devices.. Why? © Alex Prager
- 3. 3Challenge the future Motivations • Traditional WSN • Power efficient • Compact hardware • Low data rate • Slow changes • Few tens of nodes • Sink We want to monitor the density of a crowd in an open- air festival using low-cost wireless devices
- 4. 4Challenge the future Motivations • Traditional WSN • Power efficient • Compact hardware • Low data rate • Slow changes • Few tens of nodes • Sink We want to monitor the density of a crowd in an open- air festival using low-cost wireless devices • We are not potatoes!!
- 5. 5Challenge the future Motivations • Traditional WSN • Power efficient • Compact hardware • Low data rate • Slow changes • Few tens of nodes • Sink • We are not potatoes!! • High data rate • Highly dynamic • Thousands of people • Decentralized We want to monitor the density of a crowd in an open- air festival using low-cost wireless devices Extreme Wireless Sensor Networks
- 6. 6Challenge the future Motivations • Traditional WSN • Power efficient • Compact hardware • Low data rate • Slow changes • Few tens of nodes • Sink • We are not potatoes!! • High data rate • Highly dynamic • Thousands of people • Decentralized We want to monitor the density of a crowd in an open- air festival using low-cost wireless devices Extreme Wireless Sensor NetworksCommunication
- 7. 7Challenge the future Motivations • Traditional WSN • Power efficient • Compact hardware • Low data rate • Slow changes • Few tens of nodes • Sink • We are not potatoes!! • High data rate • Highly dynamic • Thousands of people • Decentralized We want to monitor the density of a crowd in an open- air festival using low-cost wireless devices Extreme Wireless Sensor NetworksCommunication
- 8. 8Challenge the future Communication challenges • Bandwidth is trade for energy efficiency • To reduce bandwidth overhead WSN • exploits neighborhood information • Synchronize nodes’ wakeups • Bandwidth is to scarce to be wasted • We can not rely on neighborhood information Traditional WSN Extreme WSN
- 9. 9Challenge the future Communication in EWSN Can we have an efficient rendezvous without neighborhood knowledge?
- 10. 10Challenge the future Communication in EWSN Init 1 3 4 2 Wakeup period Yes! But not with unicast and broadcast L n Unicast n Broadcast n Opportunistic anycast
- 11. 11Challenge the future Communication in EWSN • Efficient rendezvous • Opportunistic anycast • Collision reduction • Opportunistic rendezvous • Application layer support • Contiki OS • LPL and LPP SOFA (Stop On First Ack) communication protocol Implementation
- 12. 12Challenge the future Efficient rendezvous More neighbors à Shorter rendezvous n Unicast n Broadcast n Opportunistic anycast Init 1
- 13. 13Challenge the future Efficient rendezvous More neighbors à Shorter rendezvous n Unicast n Broadcast n Opportunistic anycast Init 1 2
- 14. 14Challenge the future Efficient rendezvous More neighbors à Shorter rendezvous n Unicast n Broadcast n Opportunistic anycast Init 1 3 4 2
- 15. 15Challenge the future Efficient rendezvous More neighbors à Shorter rendezvous n Unicast n Broadcast n Opportunistic anycast Init 1 3 4 2 5 6
- 16. 16Challenge the future Model opportunistic anycast More neighbors (N) à Shorter rendezvous (R) E(R) = Tw / 1+N (n) • Nodes’ wake-up period (Tw) • Uniform random variables • Independent • Identically distributed • Rendezvous time (R) • First Order statistic • Beta (1,N) time(ms) neighborhood size 50 1000 0 50 100 150 200 experimental results
- 17. 17Challenge the future Collision reduction Transmission Back-Off (TBO) transforms a collision into a successful data exchange • Listen for incoming beacons instead of CCA • If a beacon is received, become a receiver • Less collision among initiators • Even shorter rendezvous! Init B B B D A A D Rendezvous Data exchange 1 2 Init TBO TBO
- 18. 18Challenge the future Information processing How to cope with the lack of unicast and broadcast?
- 19. 19Challenge the future Information processing • Select random neighbor • Peer sampling • Local data exchange • Push-pull • Mass conservation • Diffuse/aggregate • Max, averages, percentiles • Repeat until convergence Gossip
- 20. 20Challenge the future Gossip support • Select random neighbor to communicate • Neighbor discovery • Random selection Peer sampling
- 21. 21Challenge the future Gossip support • Select random neighbor to communicate • Neighbor discovery • Random selection Peer sampling Opportunistic peer sampling • Add random delays to the nodes’ wake-ups • Use opportunistic anycast to select nodes • No neighbor discovery • Select the most efficient neighbor (to rendezvous)
- 22. 22Challenge the future Gossip support • Select random neighbor to communicate • Neighbor discovery + random selection • Difficult in EWSN Peer sampling Opportunistic peer sampling 0 50 100 0 200 400 600 800 Node ID Nodescore Observed Average percentile
- 23. 23Challenge the future Gossip support 2-way data exchange • Rendezvous once, exchange information twice (2x1) • Improve convergence speed • Selects quality links • 2-way rendezvous + 3-way handshake A D B B B D A
- 24. 24Challenge the future Evaluation 0 20 40 60 80 100 cardinality node positions L R Experiment setup MSP430 CC1101 100
- 25. 25Challenge the future Energy efficiency dutycycle(%) neighborhood size 50 5000 0 2 4 6 8 • Settings • Topology: Clique • Message rate: 0.5 • Wakeup period: 1 s • Wakeup time: 10 ms • Testbed • Size: 5-100 nodes
- 26. 26Challenge the future Energy efficiency dutycycle(%) neighborhood size 50 5000 0 2 4 6 8 • Settings • Topology: Clique • Message rate: 0.5 • Wakeup period: 1 s • Wakeup time: 10 ms • Testbed • Size: 5-100 nodes • Simulations • Size: 5-450 nodes The energy consumption of nodes decreases with density
- 27. 27Challenge the future Exchanged messages globalmessagerate(msg/sec) neighborhood size 50 5000 0 50 100 150 200 • Settings • Topology: Clique • Message rate: 0.5 • Wakeup period: 1 s • Wakeup time: 10 ms • Testbed • Size: 5-100 nodes
- 28. 28Challenge the future Exchanged messages • Settings • Topology: Clique • Message rate: 0.5 • Wakeup period: 1 s • Wakeup time: 10 ms • Testbed • Size: 5-100 nodes • Simulations • Size: 5-450 nodes globalmessagerate(msg/sec) neighborhood size 50 5000 0 50 100 150 200 When bandwidth saturates, SOFA continues to exchange messages instead of collapsing
- 29. 29Challenge the future Reliability deliveryratio neighborhood size 50 5000 0.90 0.95 1 0.85 • Settings • Topology: Clique • Message rate: 0.5 • Wakeup period: 1 s • Wakeup time: 10 ms • Testbed • Size: 5-100 nodes
- 30. 30Challenge the future Reliability • Settings • Topology: Clique • Message rate: 0.5 • Wakeup period: 1 s • Wakeup time: 10 ms • Testbed • Size: 5-100 nodes • Simulations • Size: 5-450 nodes deliveryratio neighborhood size 50 5000 0.90 0.95 1 0.85 When bandwidth saturates, SOFA continues to reliably exchange messages instead of collapsing
- 31. 31Challenge the future Mobility • Settings • Topology: Multi-hop • Message rate: 0.5 • Wakeup period: 1 s • Wakeup time: 10 ms • Diameter: ~3 hop • Simulations • Size: 15-300 nodes • Density: 5-100 nodes • BonnMotion’s random waypoint • Static (0 m/s) • Walking (1.5 m/s) • Biking (7 m/s) • Almost identical performance • Energy efficiency • Exchanged messages • Reliability Without the need of neighbors’ information, SOFA is resilient to mobility
- 32. 32Challenge the future Does SOFA fulfill our goal? • Normal conditions • Unicast and broadcast • Routing tree • Collection • Aggregation • Extreme conditions • Opportunistic anycast • Gossip • Diffusion/Aggregation • Graph processing Goal: “We want to monitor the density of a crowd during an outdoor festival using low-cost wireless devices”
- 33. 33Challenge the future Does SOFA fulfill our goal? • Expected result à • Legend n 1st percentile n 50th percentile 100th percentile − Data exchange Demo of SOFA running a gossip protocol to compute in which percentiles nodes’ values are
- 34. 34Challenge the future Does SOFA fulfill our goal? • Normal conditions • Unicast and broadcast • Routing tree • Collection • Aggregation • Neighbor discovery • Extreme conditions • Opportunistic anycast • Gossip • Diffusion/Aggregation • Graph processing • Neighborhood size estimation • Poster #8 • Full presentation at IPSN! Goal: “We want to monitor the density of a crowd during an outdoor festival using low-cost wireless devices”
- 35. 35Challenge the future Conclusions • Under extreme conditions traditional WSN do not scale • We proposed SOFA, an opportunistic communication protocol that: • Make an efficient use of bandwidth • Reduce the number of collision • Support gossiping