Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks

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Directed DiffusionA Scalable and Robust
Communication Paradigm for Sensor Networks
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Motivation

• Properties of Sensor Networks
• Data centric
• No central authority
• Resource constrained
• Nodes are tied to physical locations
• Nodes may not know the topology
• Nodes are generally stationary
• How can we get data from the sensors?

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Directed Diffusion

• Data centric
• Individual nodes are unimportant
• Request driven
• Sinks place requests as interests
• Sources satisfying the interest can be found
• Intermediate nodes route data toward sinks
• Localized repair and reinforcement
• Multi-path delivery for multiple sources, sinks,
  and queries

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Motivating Example

• Sensor nodes are monitoring animals
• Users are interested in receiving data for all
  4-legged creatures seen in a rectangle
• Users specify the data rate

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Interest and Event Naming

• Query/interest
• Typefour-legged animal
• Interval20ms (event data rate)
• Duration10 seconds (time to cache)
• Rect-100, 100, 200, 400
• Reply
• Typefour-legged animal
• Instance elephant
• Location 125, 220
• Intensity 0.6
• Confidence 0.85
• Timestamp 012040
• Attribute-Value pairs, no advanced naming scheme

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Directed Diffusion

• Sinks broadcast interest to neighbors
• Initially specify a low data rate just to find
  sources for minimal energy consumptions
• Interests are cached by neighbors
• Gradients are set up pointing back to where
  interests came from
• Once a source receives an interest, it routes
  measurements along gradients

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Interest Propagation

• Flood interest
• Constrained or Directional flooding based on
  location is possible
• Directional propagation based on previously
  cached data

Gradient
Source
Interest
Sink
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Data Propagation

• Multipath routing
• Consider each gradients link quality

Gradient
Source
Data
Sink
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Reinforcement

• Reinforce one of the neighbor after receiving
  initial data.
• Neighbor who consistently performs better than
  others
• Neighbor from whom most events received

Gradient
Source
Data
Reinforcement
Sink
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Negative Reinforcement

• Explicitly degrade the path by re-sending
  interest with lower data rate.
• Time out Without periodic reinforcement, a
  gradient will be torn down

Gradient
Source
Data
Reinforcement
Sink
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Summary of the protocol
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Sampling forwarding

• Sensors match signature waveforms from codebook
  against observations
• Sensors match data against interest cache,
  compute highest event rate request from all
  gradients, and (re) sample events at this rate
• Receiving node
• Find matching entry in interest cache
• If no match, silently drop
• Check and update data cache (loop prevention,
  aggregation)
• Resend message along all the active gradients,
  adjusting the frequency if necessary

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Design Considerations
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Evaluation

• ns2 simulation
• Modified 802.11 MAC for energy use calculation
• Idle time 35mW
• Receive 395mw
• Transmit 660mw
• Baselines
• Flooding
• Omniscient multicast A source multicast its
  event to all sources using the shortest path
  multicast tree
• Do not consider the tree construction cost

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• Simulate node failures
• No overload
• Random node placement
• 50 to 250 nodes (increment by 50)
• 50 nodes are deployed in 160m 160m
• Increase the sensor field size to keep the
  density constant for a larger number of nodes
• 40m radio range

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Metrics

• Average dissipated energy
• Ratio of total energy expended per node to number
  of distinct events received at sink
• Measures average work budget
• Average delay
• Average one-way latency between event
  transmission and reception at sink
• Measures temporal accuracy of location estimates
• Both measured as functions of network size

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Average Dissipated Energy
They claim diffusion can outperform omniscient
multicast due to in-network processing
suppression. For example, multiple sources can
detect a four-legged animal in one area.
0.018
0.016
Flooding
0.014
0.012
0.01
0.008
Omniscient Multicast
(Joules/Node/Received Event)
Average Dissipated Energy
0.006
Diffusion
0.004
0.002
0
0
50
100
150
200
250
300
Network Size
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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
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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
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Average Dissipated Energy (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
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Average energy and delay

• Average delay is misleading
• Directed Diffusion is better than Omniscient
  Multicast?
• Why dont they suppress messages in Omniscient
  Multicast as done in Directed Diffusion?
• Topology has little path diversity

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Failures

• Dynamic failures
• 10-20 failure at any time
• Each source sends different signals
• lt20 delay increase, fairly robust
• Energy efficiency improves
• Reinforcement maintains adequate number of high
  quality paths
• Shouldnt it be done in the first place?

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Analysis

• Energy gains are dependent on 802.11 energy
  assumptions
• Can the network always deliver at the interests
  requested rate?
• Can diffusion handle overloads?
• Does reinforcement actually work?

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Conclusions

• Data-centric communication between sources and
  sinks
• Aggregation and duplicate suppression
• More thorough performance evaluation is required

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Extensions

• One-phase pull
• Propagate interest
• A receiving node pick the link that delivered the
  interest first
• Assumes the link bidirectionality

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• Push diffusion
• Sink does not flood interest
• Source detecting events disseminate exploratory
  data across the network
• Sink having corresponding interest reinforces one
  of the paths

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TEEN (Threshold-sensitive Energy Efficient sensor
Network protocol) IPDPS01

• Push-based data centric protocol
• Nodes immediately transmit a sensed value
  exceeding the threshold to its cluster head that
  forwards the data to the sink

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LEACH HICSS00

• Proposed for continuous data gathering protocol
• Divide the network into clusters
• Cluster head periodically collect
  aggregate/compress the data in the cluster using
  TDMA
• Periodically rotate cluster heads for load
  balancing

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Discussions

• Criteria to evaluate data-centric routing
  protocols?
• Or, what do we need to try to optimize? Energy
  consumption? Data timeliness? Resilience?
  Confidence of event detection? Too many
  objectives already? Can we pick just one or two?

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Questions?