Download DirectedDiffusion

Directed Diffusion
for Wireless Sensor Networking
By Chalermek Intanagonwiwat, Ramesh Govindan,
Deborah Estrin, John Heidemann, and Fabio Silva
Presented by: Jin Sun
02/08/2005
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Outline
Introduction
 The problem
 Directed Diffusion Concepts
 Simulation Results
 Summary

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Introduction

A region requires eventmonitoring

Deploy sensors forming a
distributed network

Wireless networking
 Energy-limited nodes

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On event, sensed and/or
processed information
delivered to the inquiring
destination
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The Problem

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?
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A sensor field
Sensor sources
CS240 Presentation
Event
Directed
Diffusion
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
aggregation point
Robust, efficient data distribution in sensor networks




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Additional source
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
 E.g.,
 Data
attribute-value pairs
Type = Wheeled vehicle
Interval = 20 ms
Duration = 10 s
Field = [x1, y1, x2, y2]
// detect vehicle location
// send events every 20ms
// Send for next 10 s
// from sensors in this area
reply
 Using
attribute-value pairs
 E.g., Type = Wheeled vehicle // type of vehicle seen
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Instance = truck
// instance of this type
Intensity = 0.6
// signal amplitude measure
Confidence = 0.85
// confidence in the match
Timestamp = 01:20:34
// event generation time
Field = [x1, y1, CS240
x2, y2]
// from sensors in this area
Presentation
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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
Sink
Sink
Sources



Interest
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
Sink

Gradient
Sink
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
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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
Reinforced gradient
D
Event
Reinforced gradient
B
A sensor field
Sink A
C

From exploratory gradients, reinforce optimal
path for high-rate data download  Unicast
 By
requesting higher-rate-i1 on the optimal path
 Exploratory
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gradients still exist – useful for faults
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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 neighbor’s cache to expire over time
D
Event
M
Src
A
C
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B
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Sink
Link A-M lossy
A reinforces B
B reinforces C …
D need not
A negative reinforces M
M negative reinforces D
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Loop Elimination
P
D

Q
M
A
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
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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
- Floof]ding incurs latency due to high MAC contention, colission
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Event Delivery Ratio under node failures
0%
10%
20%
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 (cont’d)

Disadvantages
 Design
doesn’t 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!
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