Directed Diffusion for Wireless Sensor Networking

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Directed Diffusion for Wireless Sensor
Networking

• By Chalermek Intanagonwiwat, Ramesh Govindan,
• Deborah Estrin, John Heidemann, and Fabio Silva
• Presented by Jin Sun

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Outline

• Introduction
• The problem
• Directed Diffusion Concepts
• Simulation Results
• Summary

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Introduction

• A region requires event-monitoring
• Deploy sensors forming a distributed network
• Wireless networking
• Energy-limited nodes
• On event, sensed and/or processed information
  delivered to the inquiring destination

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The Problem
A sensor field

• Where should the data be stored?
• How should queries be routed to the stored data?
• How should queries for sensor networks be
  expressed?
• Where and how should aggregation be performed?

Event
Sensor sources
Sensor sink
On event, sensed and/or processed information
delivered to the inquiring destination
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Directed Diffusion

• Initial Goals
• Propose an application-aware paradigm to
  facilitate efficient aggregation, and delivery of
  sensed data to inquiring destination

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Directed Diffusion-how it works
Low data rate
Sink
How many vehicles do you observe in the
southeast quadrant?
High data rate
Source

• Robust, efficient data distribution in sensor
  networks
• name data (not nodes), use physicality
• diffuse requests and responses across network
• optimize path with gradient-based feedback
• additional data can be processed and aggregated
  within the network

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Directed Diffusion

• Data Naming
• Interests and Gradient
• Data Propagation
• Reinforcement
• Path establishment
• Path failure / recovery
• Loop elimination

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Data Naming

• Expressing an Interest
• Using attribute-value pairs
• E.g.,
• Data reply
• Using attribute-value pairs
• E.g.,

Type Wheeled vehicle // detect vehicle
location Interval 20 ms // send events every
20ms Duration 10 s // Send for next 10 s Field
x1, y1, x2, y2 // from sensors in this area
Type Wheeled vehicle // type of vehicle
seen Instance truck // instance of this
type Intensity 0.6 // signal amplitude
measure Confidence 0.85 // confidence in the
match Timestamp 012034 // event generation
time Field x1, y1, x2, y2 // from sensors in
this area
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Directed Diffusion

• Data Naming
• Interests and Gradient
• Data Propagation
• Reinforcement
• Path establishment
• Path failure / recovery
• Loop elimination

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Interest Propagation

• Inquirer (sink) broadcasts exploratory interest,
  i1
• Intended to discover routes between source and
  sink
• Neighbors update interest-cache and forwards i1
• No way of knowing differentiating new interests
  from repeated

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Gradient Establishment
Routed Data

• Gradient for i1 set up to upstream neighbor
• No source routes
• Gradient a weighted reverse link
• Low gradient ? Few packets per unit time needed

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Directed Diffusion

• Data Naming
• Interests and Gradient
• Data Propagation
• Reinforcement
• Path establishment
• Path failure / recovery
• Loop elimination

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Event-data propagation

• Event e1 occurs, matches i1 in sensor cache
• e1 identified based on waveform pattern matching
• Interest reply diffused down gradient (unicast)
• Diffusion initially exploratory (low packet-rate)
• Cache filters suppress previously seen data
• Problem of bidirectional gradient avoided

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Directed Diffusion

• Data Naming
• Interests and Gradient
• Data Propagation
• Reinforcement
• Path establishment
• Path failure / recovery
• Loop elimination

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Reinforcement
Event
D
B

• From exploratory gradients, reinforce optimal
  path for high-rate data download ? Unicast
• By requesting higher-rate-i1 on the optimal path
• Exploratory gradients still exist useful for
  faults

A sensor field
Sink A
C
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Path Failure / Recovery

• Link failure detected by reduced rate, data loss
• Choose next best link (i.e., compare links based
  on infrequent exploratory downloads)
• Negatively reinforce lossy link
• Either send i1 with base (exploratory) data rate
• Or, allow neighbors cache to expire over time

Link A-M lossy A reinforces B B reinforces C D
need not A negative reinforces M M negative
reinforces D
Event
D
M
Src
A
C
Sink
B
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Loop Elimination
Q
P

• M gets same data from both D and P, but P always
  delivers late due to looping
• M negatively-reinforces (nr) P, P nr Q, Q nr M
• Loop M ? Q ? P eliminated
• Conservative nr useful for fault resilience

A
D
M
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Simulation Results

• Compare directed diffusion to
• flooding
• Omniscient multicast
• Key metrics
• Average dissipated energy
• per node energy dissipation / events seen by
  sinks
• Average packet delay
• latency of event transmission to reception at
  sink
• Distinct event delivery
• of distinct events received / of events
  originally sent

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Average Dissipated Energy
flooding
Multicast
Diffusion
In-network aggragation reduces DD redundancy -
Flooding is poor because of multiple paths from
source to sink
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Delay
flooding
Diffusion
Multicast
DD finds least delay paths - Floofding incurs
latency due to high MAC contention, colission
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Event Delivery Ratio under node failures
0
10
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Delivery ration degrades with more nodes
failures - Graceful degradation indicate
efficient negative reinforcement
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Summary

• Main Contributions
• Description of new networking paradigm
• Interests, gradients, reinforcement
• Benefits of in-network processing
• Aggregation and nested-queries
• Works with multiple sources and sinks
• Can perform local repair
• Reinforce another path if a node dies

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Summary (contd)

• Disadvantages
• Design doesnt deal with congestion or loss
• Periodic broadcasts of interest reduces network
  lifetime
• Nodes within range of human operator may die
  quickly

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Thank You!