Overview of Directed
Diffusion
Professor: -Dr Ajay Gupta
Presented By: -Vivek Kinra
CS691 Spring2003
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Note: -Various slides of this presentation are created with the help of
presentation slides of UCLA ,USC and various other sources
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History
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Research started to investigate the design
of localized algorithm using the Directed
Diffusion model
The idea was developed in the context of
a DARPA study by D.Estrin
Example of posing query for
tanks/vehicles……..
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Design Features
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Data centric: -Routing is based on data
contained in sensor node and may not
need ID
Application focus on the data generated
by sensors.
Data is named by attributes and
applications request data matching certain
attribute values.
Motivated by robustness, scaling and
energy efficiency
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Directed Diffusion
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Developed by ISI/USC and UCLA is a novel
network protocol built for info retrieval
and data dissemination.
Data generated by nodes =>
attributes(A1)
Sinks/nodes request data=>Interest into
n/w
If A1 == Interest then(gradient setup in
n/w) (Pedestrians)
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contd
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Data pulled towards sinks =>receiver
Initiated routing protocol
Example target tracking
Intermediate node might aggregate data
Since all nodes in directed diffusion are
application aware so It is completly
application oriented.
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contd
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It is significantly different from IP style
communication
Not infeasible with IP or Ad-hoc routing
Imp Feature: - interest, data aggregation
and propagation are determined by
localized interaction
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Expected Architecture of Sensor
Network
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Required capabilities of sensor node: A Match box sized form factor
Battery power source
Power conserving processor clocked at
several hundred Mhz
Memory
Radio modem
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contd
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Energy efficient MAC layer
Can have more than 1 or more sensors e.g
seismic geophones, infrared dipoles etc
The Atod conversion on such system
produce 70ksamples/sec and 12 bit
resolution
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For power issue, common signal processing
functions offloaded to low power ASIC
Processor woke up only when event of
Interest
A Sensor Node have a GPS receiver
The adv. Of these sensors is with very cheap
in cost they obtain high SNR (attenuate with
distance).
Also can be deployed in huge amount
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Energy concern
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Sensors Deployment falls in two ways: Large complex system deployed far.
Short range hop-hop communication is
preferred over direct long range.
Local computation to reduce data
before transmission
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Contd
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In this organization, individual nodes
reduce the sampled waveform generated
by target (tank etc) into a relatively coarse
grained “event” description.
Description =>”codebook value” (event
code)
Code->a timestamp,……
Nodes exchanged this event code
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Method description
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Task conveyed to sensor N/W
Nodes tasks it’s sensors
Matches sampled wave form against
locally stored library
Sensors in region may coordinate to pick
best estimate.
Packet:-Attributes (type, amplitude,
Intensity, region, time stamp……)
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Naming
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Given Set of Tasks supported by sensor
network selecting a naming scheme is first
step in designing sensor networks.
Basically list of attribute value pairs.
E.g. For tracking animal its attributes
should describe tasks like, type of animal,
geographic location to track, interval for
sending updates, duration for which it was
recorded (event occurrence time)
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Data sent in response to
Interest
Type = four legged animal
 Instance = rabbit//instance of type
 location = [125,220]/node location
 Intensity = 0.6/signal amplitude
 Confidence = 0.85//confi.. in match
 Timestamp = 01:20:40//event
generation time
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Sink periodically broadcasts an interest message
to each of its neighbors.
Initial interest specifies a low data rate (e.g 1
event/sec)
Interest are diff based on type, rect or interval
Every node maintains a interest cache.
Interest entries in cache do not contain info
about sink
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Interest entry
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Time stamp (last received matching)
Gradient field (up to 1/neighbor)
G.F => data rate field (requested by
neighbor)=>interval attribute
Duration=timestamp – expiresAT
No Entry
No gradient
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Event
interests
Sink
Have u seen any four leg animal???
QUERY DIFFUSED IN TO INTEREST WHICH IS LIST OF ATTRIBUTE VALUE PAIRS
Interest Propagation (Flooding)
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YES I HAVE SEEN ONE….
INTIAL GRADIENTS SETUP(VALUE+DIRECTION)
Two-way Gradient setup
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Gradient setup/reinforced path
source
Sink/Interest
I-Propagation
Initial grad..
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Data …..reinforced
path
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Interest/gradient
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Task ={type,rect,a duration of 10 min}is
instantiated at particular node
Interval :- event data rate
Sink periodically broadcast interest msg (&
refresh interest) to neighbors.
Initial Interest :-{rect,duration
attributes,larger interval attribute}
Gradient expiration
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DATA DELIVERY THROUGH REINFORCED PATH
SINGLE PATH DELIVERY (CAN BE MULTIPATH ALSO)
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IN CASE OF NODE FAILURE USE ALTERNATIVE PATHS
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Reinforcement
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When to reinforce ?(quality/delay matrices
can be chosen)
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Whom to reinforce ?
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How many to reinforce?
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When to send negative reinforcement
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When??
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Sink initially diffuses a interest for a low
event-rate.
Once sources starts detect a matching
target they send low rate events.
After the sink starts receiving these low
data rate events it reinforces one
particular neighbor to draw down higher
quality.
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Whom??
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To reinforce this neighbor, the sink resends the original interest message but
with smaller interval (higher data rate).
Two approaches for reinforce
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Incremental approach:- Add min # of links to
existing tree
Select links so that min energy is used
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How Many
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Node must reinforce at least one neighbor
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Negative Reinforcement
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Earlier used A but now B is better
One way :- time out all high data
gradients in the n/w
Sink would periodically reinforce B and
cease A that will degrade the path to A to
lower data rate
Other way-:Degrade the path to A by resending the interest with low data rate
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Whether to negatively reinforce or
not
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N.R those neighbor from which no new
event have been received.
Or few events are coming.
Significant experiments are required
before deciding which local rule achieve
an energy efficient global behaviour
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Issues of Concern
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Ad hoc, self organizing, adaptive systems
with predictable behavior
Collaborative processing, data fusion,
multiple sensory modalities
Data analysis/mining
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Issues yet to be resolved
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How to handle congested network?
Semantics for gradients.
Handling of more than one sources.
Negative reinforcement increases delay
and contention
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comments
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(battery life, size, processing power, memory,
etc.)? The paper presents a motion-detection
scenario for sensor networks.
To identify an event sources must match
sampled sensor waveforms against signatures
stored in a local library.
To be useful, this library may have to store
several thousand such signatures or more.
We could implement "task-centric" sensor
networks, where sensor nodes are focused on
one or two type of event detection.
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Tiny Diffusion
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Implementation of Diffusion on resource
constrained USB motes
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8 bit CPU, 8k program memory, 512 bytes
data memory
Subsets of full system
Retains only gradients and condenses
attributes to a single tag
Entire system runs for less than 5.5 KB
memory
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contd
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Tiny OS adds ~3.5 KB and 144 bytes of
data (inclusive support for radio and photo
sensor
Diffusion adds ~2k code and 110 bytes of
data to tiny OS
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Tiny Diffusion Functionality
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Resource Constraint
Limited Cache size-currently 10 entries of
2 bytes each
Limited ability to support multiple traffic
stream. currently support 5 concurrently
active gradients
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TinyOS Implementation
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Gateway Architecture
Photo
Data Source
Data Sink
TinyDiffusion
TINYOS
Acoustic
Data Source
Query
Data Sink
DIFFUSION
LINUX
Device
Driver
MOTE
ATMEL 8586 4MHz
MCU
8K program memory
512 Bytes Data
Memory
RFM Radio 900 MHz
RFM
Transceiver
TINYOS
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PC104
AMD Elan™SC400
66MHz CPU
16MB RAM
Form Factor:
3.6" x 3.8" x 0.6"
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Tiered Testbed
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PC-104+(linux) with
MoteNIC
Tags, Sensor Card
UCB Motes w/TinyOS
Yet to come:
SmartDust (highly
specialized nodes)
PS104
TAG
USB Mote
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