MOBICOM 2002

1

Directed Diffusion for
Wireless Sensor Networking C. Intanagonwiwat, R.
Govindan, D. Estrin, John Heidemann, and
Fabio Silva

• MOBICOM 2002

2
Contents

• Introduction
• Directed Diffusion
• Interest and Data Naming
• Interest Propagation and Gradients Set-up
• Data Propagation
• Reinforcement
• Simulations
• Conclusion

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Introduction

• Problem How can we get data from the sensors?
• Sensor network
• Frequent Node Failure
• Energy-Constraint
• Request Driven
• Task sink-gtsensors (query dissemination)
• Event sensor source-gtsink
• Data Centric
• Communication is for named data
• Diffusion closely resembles some ad-hoc routing

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Interest and Data Naming

• Interest/Query
• Type tank
• Interval 10ms (event data rate, 100 events per
  second)
• Rect -100, 100, 200, 400
• Timestamp 01 20 40
• ExpiresAt 01 30 40
• Data/Reply
• Type tank
• Instance 150, 220
• Location 125, 220
• Intensity 0.6
• Confidence 0.85
• Timestamp 012040
• Named using Attribute-Value Pairs

Duration10 min (time to cache)
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Interest Propagation and Gradients Set-up

• Sink periodically broadcasts interest
• Exploratory interest with a large interval
• Low data rate (few data packets are need in unit
  time)
• Neighbors update interest-cache and forwards the
  interest
• Flooding
• Directional flooding based on location.
• Directional Propagation based on previously
  cached data
• Gradients set-up
• Gradients are set up to the upstream neighbors
• Weight data rate

Interest(type) Timestamp Gradient1(data rate) Gradient2 .. Duration

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Exploratory Gradient
Exploratory Request Gradient
Event
Bidirectional gradients established on all links
through flooding
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Data Propagation

• If Event occurs,
• Search interest cache for matching interest
  entry
• Compute the highest event rate among all its
  gradients,
• and Sample events at this rate
• And Send data to the relevant neighbors
• Receiving node
• Find matching entry in interest cache, no match
  silent drop
• Check and add data cache (loop prevention)
• Re-send message with appropriate rate
  (down-conversion)

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Exploratory events
Exploratory event initial interest? ?? event
Instance 150, 220
Source
Instance 150, 220
Sink
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Positive Reinforcement

• After sink starts receiving exploratory events,
  Reinforces one particular neighbor for real data
• Is achieved by data driven local rules
• Example of such a rule
• Receives previously unseen event from a neighbor
• Sink re-send original interest with a smaller
  interval (higher data rate)
• Receiving node also reinforce at least one
  neighbor
• Using data cache
• Example neighbor from which it first received
  the latest event matching the interest

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Positive Reinforcement (Contd)
Source
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Positive Reinforcement (Contd)
Source
Instance 150,300
We reinforce that neighbor if it is sending new
events
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Positive Reinforcement (contd)

• Its possible more than one path being reinforced
• Selects empirically low-delay path
• When one path delivers event faster,
• Sink uses this path for high-quality data

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Negative Reinforcement

• Negatively reinforce a path
• To time-out data gradient unless it is explicitly
  reinforced
• To explicitly send negative reinforcement message
• Local repair for failed paths
• When C detects its failure, negatively reinforce
  failed link and reinforce another path

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Simulations

• Vehicle tracking system in ns-2
• 3 Metrics
• Average dissipated energy
• Average delay
• One way latency between transmitting events and
  receiving it
• Distinct-event delivery ratio
• These metrics are studied as a function of
  network size.

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Parameter setting

• Sensor field 50 nodes in 160m x 160m square
• Radio range is 40m
• Keep the average density of sensor nodes constant
• 5 sources and 5 sinks (? low load)
• Each source generates two events per second
• Rate for exploratory events is one event per 50
  seconds
• Window for negative reinforcement is 2 seconds
• 1.6Mb/s 802.11 MAC
• Energy model
• Idle time 35mW
• Receiving power 395mW
• Transmission power 660mW

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Average dissipated energy

• Multiple path
• Reinforcement is very aggressive
• Negative reinforcement is very conservative
• Listening energy

Omniscient multicast is idealized scheme, but has
no data aggregation.
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Average delay
Reinforcement rules seem to be finding the low
delay paths
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Event Delivery Ratio with node failures
Turn off 1020 nodes for 30 seconds,
repeatedly Each source sees different vehicles
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Average delay with node failures
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Average dissipated energy with node failures
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Negative reinforcement
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Duplicate suppression
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High idle radio power
ATT Wavelan 1.6W (for transmission), 1.2W (for
reception), 1.15W (for idle time)
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Conclusions

• Directed Diffusion is significant energy
  efficient.
• Directed Diffusion is stable under node failures.
• Performance depends on sensor radio MAC layers.

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Acknowledged problems

• Experiments did not evaluate operation under high
  load
• Reinforcing multiple routes leads to wasteful
  excess transmissions
• Experiments used the wrong MAC layer