The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
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
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
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
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
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
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