Overview of Directed Diffusion

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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

• 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

• 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

• Developed by ISI/USC and UCLA is a novel network
  protocol built for info retrieval and data
  dissemination.
• Data generated by nodes gt attributes(A1)
• Sinks/nodes request datagtInterest into n/w
• If A1 Interest then(gradient setup in n/w)
  (Pedestrians)

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contd

• Data pulled towards sinks gtreceiver 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

• 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

• 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

• 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

• 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

• In this organization, individual nodes reduce the
  sampled waveform generated by target (tank etc)
  into a relatively coarse grained event
  description.
• Description gtcodebook value (event code)
• Code-gta timestamp,
• Nodes exchanged this event code

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Method description

• Task conveyed to sensor N/W
• Nodes tasks its 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

• 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 012040//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

• Time stamp (last received matching)
• Gradient field (up to 1/neighbor)
• G.F gt data rate field (requested by
  neighbor)gtinterval attribute
• Durationtimestamp 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(VALUEDIRECTION) Two-way
Gradient setup
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Gradient setup/reinforced path
source
Sink/Interest
Data ..reinforced path
I-Propagation
Initial grad.. setup
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Interest/gradient

• Task type,rect,a duration of 10 minis
  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

• When to reinforce ?(quality/delay matrices can be
  chosen)
• Whom to reinforce ?
• How many to reinforce?
• When to send negative reinforcement

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When??

• 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??

• To reinforce this neighbor, the sink re-sends the
  original interest message but with smaller
  interval (higher data rate).
• Two approaches for reinforce
• Incremental approach- Add min of links to
  existing tree
• Select links so that min energy is used

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How Many

• Node must reinforce at least one neighbor

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Negative Reinforcement

• 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 re-sending
  the interest with low data rate

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Whether to negatively reinforce or not

• 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

• 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

• 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

• (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

• Implementation of Diffusion on resource
  constrained USB motes
• 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

• 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

• 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
MOTE ATMEL 8586 4MHz MCU 8K program memory 512
Bytes Data Memory RFM Radio 900 MHz
PC104 AMD ElanSC400 66MHz CPU 16MB RAM Form
Factor 3.6"  x  3.8"  x  0.6"
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Tiered Testbed

• 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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