Chalermek Intanagonwiwat (USC/ISI)

1
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
2
The Goal

• 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

3
The Challenge Dynamics!

• The physical world is dynamic
• Dynamic operating conditions
• Dynamic availability of resources
• particularly energy!
• Devices must adapt automatically to the
  environment
• Too many devices for manual configuration
• Environmental conditions are unpredictable
• Unattended and un-tethered operation is key to
  many applications

4
Energy is the bottleneck resource

• Communication VS Computation Cost Pottie 2000
• E a R4
• 10 m 5000 ops/transmitted bit
• 100 m 50,000,000 ops/transmitted bit
• Avoid communication over long distances
• Cannot assume global knowledge, cannot
  pre-configure networks
• Achieve desired global behavior through localized
  interactions
• Empirically adapt to observed environment
• Can leverage data processing/aggregation inside
  the network

5
Our Approach Directed Diffusion

• In-network data processing (e.g., aggregation,
  caching)
• Distributed algorithms using localized
  interactions
• Application-aware communication primitives
• expressed in terms of named data (not in terms of
  the nodes generating or requesting data)

6
Application Example Remote Surveillance

• Interrogation
• 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

7
Basic Directed Diffusion
Setting up gradients
Source
Sink
Interest Interrogation
Gradient Who is interested
8
Basic Directed Diffusion
Sending data and Reinforcing the best path
Source
Sink
Low rate event
Reinforcement Increased interest
9
Directed Diffusion and Dynamics
Source
Sink
Recovering from node failure
Low rate event
Reinforcement
High rate event
10
Directed Diffusion and Dynamics
Source
Sink
Stable path
Low rate event
High rate event
11
Local Behavior Choices

• For propagating interests
• In our example, flood
• More sophisticated behaviors possible e.g. based
  on cached information, GPS

• For data transmission
• Multi-path delivery with selective quality along
  different paths
• probabilistic forwarding
• single-path delivery, etc.

• For setting up gradients
• data-rate gradients are set up towards neighbors
  who send an interest.
• Others possible probabilistic gradients, energy
  gradients, etc.

• For reinforcement
• reinforce paths, or parts thereof, based on
  observed delays, losses, variances etc.
• other variants inhibit certain paths because
  resource levels are low

12
Initial simulation study of diffusion

• Key metric
• Average Dissipated Energy per event delivered
• indicates energy efficiency and network lifetime
• Compare diffusion to
• flooding
• centrally computed tree (omniscient multicast)

13
Diffusion Simulation Details

• 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
• 660 mW in transmission, 395 mW in reception, and
  35 mw in idle

14
Diffusion Simulation

• Surveillance application
• 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

15
Average Dissipated Energy (Standard 802.11 energy
model)
0.14
Diffusion
0.12
Omniscient Multicast
Flooding
0.1
0.08
Average Dissipated Energy
(Joules/Node/Received Event)
0.06
0.04
0.02
0
0
50
100
150
200
250
300
Network Size
Standard 802.11 is dominated by idle energy
16
Average Dissipated Energy (Sensor radio energy
model)
0.018
0.016
Flooding
0.014
0.012
0.01
0.008
(Joules/Node/Received Event)
Omniscient Multicast
Average Dissipated Energy
0.006
Diffusion
0.004
0.002
0
0
50
100
150
200
250
300
Network Size
Diffusion can outperform flooding and even
omniscient multicast. WHY ?
17
Impact of In-network Processing
0.025
Diffusion Without Suppression
0.02
0.015
(Joules/Node/Received Event)
Average Dissipated Energy
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.
18
Impact of Negative Reinforcement
0.012
0.01
Diffusion Without Negative Reinforcement
0.008
Average Dissipated Energy
(Joules/Node/Received Event)
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
19
Summary of Diffusion Results

• Under the investigated scenarios, diffusion
  outperformed omniscient multicast and flooding
• Application-level data dissemination has the
  potential to improve energy efficiency
  significantly
• Duplicate suppression is only one simple example
  out of many possible ways.
• Aggregation (in progress)
• All layers have to be carefully designed
• Not only network layer but also MAC and
  application level
• Experimentation on our testbed in progress

20
More information

• SCADDS project
• http//www.isi.edu/scadds
• ns-2 network simulator (with diffusion supports)
• http//www.isi.edu/nsnam/dist/ns-src-snapshot.tar.
  gz
• Our testbed and software
• http//www.isi.edu/scadds/testbeds.html