SOFA communication protocol (EWSN 2014)

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

Motivation
We want to monitor the density of a crowd during an
outdoor festival using low-cost wireless devices.. Why?
© Alex Prager

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

Motivations
•  Slow changes
•  Sink
•  We are not potatoes!!

Motivations
•  Slow changes
•  Sink
•  High data rate
•  Highly dynamic
•  Thousands of people
•  Decentralized
Extreme Wireless
Sensor Networks

Motivations
•  Slow changes
•  Sink
Extreme Wireless
Sensor NetworksCommunication

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

Communication in EWSN
Can we have an efficient rendezvous without
neighborhood knowledge?

Init
1
3
4
2
Wakeup period
Yes! But not with unicast and broadcast L
n Unicast n Broadcast n Opportunistic anycast

•  Efficient rendezvous
•  Opportunistic anycast
•  Collision reduction
•  Opportunistic rendezvous
•  Application layer support
•  Contiki OS
•  LPL and LPP
SOFA (Stop On First Ack)
communication protocol Implementation

Efficient rendezvous
More neighbors à Shorter rendezvous
Init
1

Init
1
2

Init
1
3
4
2

Init
1
3
4
2
5
6

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

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

Information processing
How to cope with the lack of unicast and
broadcast?

Information processing
•  Select random neighbor
•  Peer sampling
•  Local data exchange
•  Push-pull
•  Mass conservation
•  Diffuse/aggregate
•  Max, averages, percentiles
•  Repeat until convergence
Gossip

Gossip support
•  Select random neighbor to
communicate
•  Neighbor discovery
•  Random selection
Peer sampling

Gossip support
communicate
•  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)

Gossip support
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

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

Evaluation
0
20
40
60
80
100
cardinality
node positions
L R
Experiment setup
MSP430
CC1101
100

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

Energy efficiency
dutycycle(%)
neighborhood size
50 5000
0
2
4
6
8
•  Settings
•  Testbed
•  Simulations
The energy consumption of nodes decreases with density

Exchanged messages
globalmessagerate(msg/sec)
neighborhood size
50 5000
0
50
100
150
200
•  Settings
•  Testbed

Exchanged messages
•  Settings
•  Testbed
•  Simulations
globalmessagerate(msg/sec)
neighborhood size
50 5000
0
50
100
150
200
When bandwidth saturates, SOFA continues
to exchange messages instead of collapsing

Reliability
deliveryratio
neighborhood size
50 5000
0.90
0.95
1
0.85
•  Settings
•  Testbed

Reliability
•  Settings
•  Testbed
•  Simulations
deliveryratio
neighborhood size
50 5000
0.90
0.95
1
0.85
When bandwidth saturates, SOFA continues to
reliably exchange messages instead of collapsing

Mobility
•  Settings
•  Topology: Multi-hop
•  Diameter: ~3 hop
•  Simulations
•  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

Does SOFA fulfill our goal?
•  Normal conditions
•  Unicast and broadcast
•  Routing tree
•  Collection
•  Aggregation
•  Extreme conditions
•  Gossip
•  Diffusion/Aggregation
•  Graph processing
Goal: “We want to monitor the density of a crowd during
an outdoor festival using low-cost wireless devices”

•  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

•  Normal conditions
•  Unicast and broadcast
•  Routing tree
•  Collection
•  Aggregation
•  Extreme conditions
•  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”

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

SOFA communication protocol (EWSN 2014)

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