Directed Diffusion
Chalermek Intanagonwiwat (USC/ISI)
Ramesh Govindan (USC/ISI)
Deborah Estrin (USC/ISI and UCLA)
DARPA Sponsored SCADDS project
http://www.isi.edu/scadds
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The Goal
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Embed numerous devices to
monitor and interact with
physical world
Network these devices so that
they can coordinate to
perform higher-level tasks
Requires robust distributed
systems of tens of thousands
of devices
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The Challenge: Dynamics!
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The physical world is dynamic
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Devices must adapt automatically to the
environment
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Dynamic operating conditions
Dynamic availability of resources
• … particularly energy!
Too many devices for manual configuration
Environmental conditions are unpredictable
Unattended and un-tethered operation is key to
many applications
Energy is the bottleneck resource
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Communication VS Computation Cost [Pottie 2000]
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E α R4
10 m: 5000 ops/transmitted bit
100 m: 50,000,000 ops/transmitted bit
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Avoid communication over long distances
• Cannot assume global knowledge, cannot preconfigure networks
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Achieve desired global behavior through localized
interactions
Empirically adapt to observed environment
Can leverage data processing/aggregation inside the
network
Our Approach: Directed
Diffusion
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In-network data processing (e.g., aggregation,
caching)
• Distributed algorithms using localized interactions
• Application-aware communication primitives
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expressed in terms of named data (not in terms of the
nodes generating or requesting data)
Application Example: Remote
Surveillance
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Interrogation:
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e.g., “Give me periodic reports about animal location in region A
every t seconds”
Interrogation is propagated to sensor nodes in region A
Sensor nodes in region A are tasked to collect data
Data are sent back to the users every t seconds
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Basic Directed Diffusion
Setting up gradients
Source
Sink
Interest = Interrogation
Gradient = Who is interested
Basic Directed Diffusion
Sending data and Reinforcing the best path
Source
Sink
Low rate event
Reinforcement = Increased interest
Directed Diffusion and
Dynamics
Source
Sink
Recovering
from node failure
Low rate event
High rate event
Reinforcement
Directed Diffusion and
Dynamics
Source
Sink
Stable path
Low rate event
High rate event
Local Behavior Choices
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For propagating interests
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data-rate gradients are set
up towards neighbors who
send an interest.
Others possible:
probabilistic gradients,
energy gradients, etc.
For data transmission
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In our example, flood
More sophisticated
behaviors possible: e.g.
based on cached
information, GPS
For setting up gradients
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Multi-path delivery with
selective quality along
different paths
probabilistic forwarding
single-path delivery, etc.
For reinforcement
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reinforce paths, or parts
thereof, based on observed
delays, losses, variances
etc.
other variants: inhibit
certain paths because
resource levels are low
Initial simulation study of
diffusion
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Key metric
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Average Dissipated Energy per event delivered
• indicates energy efficiency and network lifetime
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Compare diffusion to
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flooding
centrally computed tree (omniscient multicast)
Diffusion Simulation Details
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Simulator: ns-2
Network Size: 50-250 Nodes
Transmission Range: 40m
Constant Density: 1.95x10-3 nodes/m2 (9.8 nodes in radius)
MAC: Modified Contention-based MAC
Energy Model: Mimic a realistic sensor radio [Pottie 2000]
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660 mW in transmission, 395 mW in reception, and 35 mw in idle
Diffusion Simulation
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Surveillance application
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5 sources are randomly selected within a 70m x 70m
corner in the field
5 sinks are randomly selected across the field
High data rate is 2 events/sec
Low data rate is 0.02 events/sec
Event size: 64 bytes
Interest size: 36 bytes
All sources send the same location estimate for base
experiments
Average Dissipated Energy
(Standard 802.11 energy
model)
Average Dissipated Energy
(Joules/Node/Received Event)
0.14
Diffusion
0.12
Flooding
Omniscient Multicast
0.1
0.08
0.06
0.04
0.02
0
0
50
100
150
200
250
300
Network Size
Standard 802.11 is dominated by idle energy
Average Dissipated Energy
(Sensor radio energy model)
Average Dissipated Energy
(Joules/Node/Received Event)
0.018
0.016
Flooding
0.014
0.012
0.01
0.008
Omniscient Multicast
0.006
0.004
Diffusion
0.002
0
0
50
100
150
200
250
300
Network Size
Diffusion can outperform flooding and even omniscient multicast.
WHY ?
Average Dissipated Energy
(Joules/Node/Received Event)
Impact of In-network
Processing
0.025
Diffusion Without
Suppression
0.02
0.015
0.01
Diffusion With
Suppression
0.005
0
0
50
100
150
200
250
300
Network Size
Application-level suppression allows diffusion to reduce traffic
and to surpass omniscient multicast.
Average Dissipated Energy
(Joules/Node/Received Event)
Impact of Negative
Reinforcement
0.012
0.01
Diffusion Without
Negative Reinforcement
0.008
0.006
0.004
Diffusion With Negative
Reinforcement
0.002
0
0
50
100
150
200
250
300
Network Size
Reducing high-rate paths in steady state is critical
Summary of Diffusion Results
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Under the investigated scenarios, diffusion
outperformed omniscient multicast and flooding
• Application-level data dissemination has the
potential to improve energy efficiency significantly
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All layers have to be carefully designed
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Duplicate suppression is only one simple example out of
many possible ways.
Aggregation (in progress)
Not only network layer but also MAC and application
level
Experimentation on our testbed in progress
More information
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SCADDS project
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ns-2: network simulator (with diffusion supports)
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http://www.isi.edu/scadds
http://www.isi.edu/nsnam/dist/ns-src-snapshot.tar.gz
Our testbed and software
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http://www.isi.edu/scadds/testbeds.html
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