sofa communication protocol (ewsn 2014)

1 Challenge 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 Challenge 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 Challenge 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 Challenge 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 Challenge 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 Challenge 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 Communication

7 Challenge 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 Communication

8 Challenge 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 Challenge the future

Communication in EWSN

Can we have an efficient rendezvous without neighborhood knowledge?

10 Challenge the future

Communication in EWSN

Init1

34

2

Wakeup period

Yes! But not with unicast and broadcast L n Unicast n Broadcast n Opportunistic anycast

11 Challenge 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 Challenge the future

Efficient rendezvous

More neighbors à Shorter rendezvous n Unicast n Broadcast n Opportunistic anycast

Init1

13 Challenge the future

Efficient rendezvous

More neighbors à Shorter rendezvous n Unicast n Broadcast n Opportunistic anycast

Init12

14 Challenge the future

Efficient rendezvous

More neighbors à Shorter rendezvous n Unicast n Broadcast n Opportunistic anycast

Init1

34

2

15 Challenge the future

Efficient rendezvous

More neighbors à Shorter rendezvous n Unicast n Broadcast n Opportunistic anycast

Init1

34

2

56

16 Challenge 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 size50 1000

0

50

100

150

200experimental results

17 Challenge 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!

InitB B B D A

A D

Rendezvous Data exchange

1

2

Init

TBO

TBO

18 Challenge the future

Information processing

How to cope with the lack of unicast and broadcast?

19 Challenge 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 Challenge the future

Gossip support

•  Select random neighbor to communicate •  Neighbor discovery •  Random selection

Peer sampling

21 Challenge 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 Challenge the future

Gossip support

•  Select random neighbor to communicate •  Neighbor discovery +

random selection •  Difficult in EWSN

Peer sampling

Opportunistic peer sampling

0 50 1000

200

400

600

800

Node ID

Node

sco

re

ObservedAveragepercentile

23 Challenge 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 Challenge the future

Evaluation

020406080

100

card

inal

ity

node positions

L R

Experiment setup

MSP430 CC1101

100

25 Challenge the future

Energy efficiency

duty

cyc

le (%

)

neighborhood size50 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 Challenge the future

Energy efficiency

duty

cyc

le (%

)

neighborhood size50 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 Challenge the future

Exchanged messages

glob

al m

essa

ge ra

te (m

sg/s

ec)

neighborhood size50 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 Challenge 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

glob

al m

essa

ge ra

te (m

sg/s

ec)

neighborhood size50 5000

0

50

100

150

200

When bandwidth saturates, SOFA continues to exchange messages instead of collapsing

29 Challenge the future

Reliability

deliv

ery

ratio

neighborhood size50 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 Challenge 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

deliv

ery

ratio

neighborhood size50 5000

0.90

0.95

1

0.85

When bandwidth saturates, SOFA continues to reliably exchange messages instead of collapsing

31 Challenge 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 Challenge 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 Challenge 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 Challenge 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 Challenge 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