1Challenge the future
SOFA: Communication in
Extreme Wireless Sensor Networks
Marco Cattani, M. Zuniga, M. Woehrle, K. Lan...
2Challenge the future
Motivation
We want to monitor the density of a crowd during an
outdoor festival using low-cost wirel...
3Challenge the future
Motivations
•  Traditional WSN
•  Power efficient
•  Compact hardware
•  Low data rate
•  Slow chang...
4Challenge the future
Motivations
•  Traditional WSN
•  Power efficient
•  Compact hardware
•  Low data rate
•  Slow chang...
5Challenge the future
Motivations
•  Traditional WSN
•  Power efficient
•  Compact hardware
•  Low data rate
•  Slow chang...
6Challenge the future
Motivations
•  Traditional WSN
•  Power efficient
•  Compact hardware
•  Low data rate
•  Slow chang...
7Challenge the future
Motivations
•  Traditional WSN
•  Power efficient
•  Compact hardware
•  Low data rate
•  Slow chang...
8Challenge the future
Communication challenges
•  Bandwidth is trade for
energy efficiency
•  To reduce bandwidth
overhead...
9Challenge the future
Communication in EWSN
Can we have an efficient rendezvous without
neighborhood knowledge?
10Challenge the future
Communication in EWSN
Init
1
3
4
2
Wakeup period
Yes! But not with unicast and broadcast L
n Unic...
11Challenge the future
Communication in EWSN
•  Efficient rendezvous
•  Opportunistic anycast
•  Collision reduction
•  Op...
12Challenge the future
Efficient rendezvous
More neighbors à Shorter rendezvous
n Unicast n Broadcast n Opportunistic ...
13Challenge the future
Efficient rendezvous
More neighbors à Shorter rendezvous
n Unicast n Broadcast n Opportunistic ...
14Challenge the future
Efficient rendezvous
More neighbors à Shorter rendezvous
n Unicast n Broadcast n Opportunistic ...
15Challenge the future
Efficient rendezvous
More neighbors à Shorter rendezvous
n Unicast n Broadcast n Opportunistic ...
16Challenge the future
Model opportunistic anycast
More neighbors (N) à Shorter rendezvous (R)
E(R) = Tw / 1+N (n)
•  No...
17Challenge the future
Collision reduction
Transmission Back-Off (TBO) transforms a
collision into a successful data excha...
18Challenge the future
Information processing
How to cope with the lack of unicast and
broadcast?
19Challenge the future
Information processing
•  Select random neighbor
•  Peer sampling
•  Local data exchange
•  Push-pu...
20Challenge the future
Gossip support
•  Select random neighbor to
communicate
•  Neighbor discovery
•  Random selection
P...
21Challenge the future
Gossip support
•  Select random neighbor to
communicate
•  Neighbor discovery
•  Random selection
P...
22Challenge the future
Gossip support
•  Select random neighbor to
communicate
•  Neighbor discovery +
random selection
• ...
23Challenge the future
Gossip support
2-way data exchange
•  Rendezvous once, exchange
information twice (2x1)
•  Improve ...
24Challenge the future
Evaluation
0
20
40
60
80
100
cardinality
node positions
L R
Experiment setup
MSP430
CC1101
100
25Challenge the future
Energy efficiency
dutycycle(%)
neighborhood size
50 5000
0
2
4
6
8
•  Settings
•  Topology: Clique
...
26Challenge the future
Energy efficiency
dutycycle(%)
neighborhood size
50 5000
0
2
4
6
8
•  Settings
•  Topology: Clique
...
27Challenge the future
Exchanged messages
globalmessagerate(msg/sec)
neighborhood size
50 5000
0
50
100
150
200
•  Setting...
28Challenge the future
Exchanged messages
•  Settings
•  Topology: Clique
•  Message rate: 0.5
•  Wakeup period: 1 s
•  Wa...
29Challenge the future
Reliability
deliveryratio
neighborhood size
50 5000
0.90
0.95
1
0.85
•  Settings
•  Topology: Cliqu...
30Challenge the future
Reliability
•  Settings
•  Topology: Clique
•  Message rate: 0.5
•  Wakeup period: 1 s
•  Wakeup ti...
31Challenge the future
Mobility
•  Settings
•  Topology: Multi-hop
•  Message rate: 0.5
•  Wakeup period: 1 s
•  Wakeup ti...
32Challenge the future
Does SOFA fulfill our goal?
•  Normal conditions
•  Unicast and broadcast
•  Routing tree
•  Collec...
33Challenge the future
Does SOFA fulfill our goal?
•  Expected result à
•  Legend
n 1st percentile
n 50th percentile
	
...
34Challenge the future
Does SOFA fulfill our goal?
•  Normal conditions
•  Unicast and broadcast
•  Routing tree
•  Collec...
35Challenge the future
Conclusions
• Under extreme conditions traditional WSN
do not scale
• We proposed SOFA, an opportun...
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SOFA communication protocol (EWSN 2014)

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SOFA communication protocol (EWSN 2014)

  1. 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. 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. 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. 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. 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. 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. 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. 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. 9. 9Challenge the future Communication in EWSN Can we have an efficient rendezvous without neighborhood knowledge?
  10. 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. 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. 12. 12Challenge the future Efficient rendezvous More neighbors à Shorter rendezvous n Unicast n Broadcast n Opportunistic anycast Init 1
  13. 13. 13Challenge the future Efficient rendezvous More neighbors à Shorter rendezvous n Unicast n Broadcast n Opportunistic anycast Init 1 2
  14. 14. 14Challenge the future Efficient rendezvous More neighbors à Shorter rendezvous n Unicast n Broadcast n Opportunistic anycast Init 1 3 4 2
  15. 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. 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. 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. 18. 18Challenge the future Information processing How to cope with the lack of unicast and broadcast?
  19. 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. 20. 20Challenge the future Gossip support •  Select random neighbor to communicate •  Neighbor discovery •  Random selection Peer sampling
  21. 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. 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. 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. 24. 24Challenge the future Evaluation 0 20 40 60 80 100 cardinality node positions L R Experiment setup MSP430 CC1101 100
  25. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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