Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks Charlmek Intanagonwiwat Ramesh Govindan Deborah Estrin - PowerPoint PPT Presentation

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Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks Charlmek Intanagonwiwat Ramesh Govindan Deborah Estrin

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Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks Charlmek Intanagonwiwat Ramesh Govindan Deborah Estrin Presentation By : Hardik Shah – PowerPoint PPT presentation

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Title: Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks Charlmek Intanagonwiwat Ramesh Govindan Deborah Estrin


1
Directed Diffusion A Scalable and Robust
Communication Paradigm for Sensor
NetworksCharlmek Intanagonwiwat Ramesh
Govindan Deborah Estrin
  • Presentation By Hardik Shah

2
.
  • .

Goal Embed computing (computing
device/sensors) close enough to the environment
to sense (detect) the phenomena, monitor and take
actions accordingly .
Key Issue Embedding the sensors in the
physical world requires network of such nodes
to co-ordinate to perform distributed sensing of
environmental phenomena.
3
Motivation
  • Energy Efficiency
  • Infeasible to transmit time-series data even
    hop-by-hop.
  • Perform local computation and reduce data before
    transmission.
  • Scalability
  • Requires thousands of sensors to coordinate to
    reach the decision.
  • Decisions should be done as much local as
    possible.
  • Robustness
  • Handle changing environment situations

4
Architectural Requirements
  • Application aware communication premetives
    (expressed in terms of named data not in terms of
    node who request data)
  • Achieve locality for decision making.
  • (and reduce the communication)
  • Application centric, data-driven networks.
  • Achieve desired global behavior through localized
    interactions, without global state.

5
Directed Diffusion
  • Data dissemination paradigm for distributed
    network of sensors.

6
Assumptions
  • Sensor network's lower level communication is
    topology independent.( not like IP networks mean
    logical connectivity distinct from physical
    geography).
  • Data aggregation is task dependant.( set of
    tasks defined by application (or set of
    applications) which defines interests for
    network)
  • Naming scheme decides the expressiveness and
    effectiveness of communication.

7
Basic Directed Diffusion concepts
  • Communication for named data not for those who
    produces (its not our concern!)
  • Query generates (virtually from any node in the
    network) interest (collection of attribute value
    pair)
  • For specific data (which tries to map with
    events supported by network ).
  • Interest diffused locally based on the naming
    scheme (its most imp since communication done
    for named data (hierarchical /flat)( mit ins
    uses hierarchical approach).)

8
  • This sets the gradients (within network) to draw
    events matching the interest.
  • Gradient represents both direction towards data
    matching and status of demand with desired update
    rate (active/inactive).

9
Architectural elements
  • Naming Scheme
  • Interest propagation
  • Data propagation
  • Data caching and aggregation
  • Reinforcement

10
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)

11
Interest propagation
  • Flooding.
  • Location aware routing (or geo casting).
  • Directional propagation on previously cached
    data.
  • In paper they have used flooding approach.

12
Event
Source
interests
Sink
Have u seen any four leg animal???
QUERY DIFFUSED IN TO INTEREST WHICH IS LIST OF
ATTRIBUTE VALUE PAIRS
Interest Propagation (Flooding)
13
YES I HAVE SEEN ONE.
INTIAL GRADIENTS SETUP(VALUEDIRECTION)
14
Data Propagation
  • Reinforcement to single path delivery.
  • Multi path delivery with selective quality.
  • Probabilistic forwarding with multi path
    delivery.
  • For selecting neighbor who gave first or
    either who has highest energy or lowest delay can
    be chosen. (Its application dependant.)

15
DATA DELIVERY THROUGH REINFORCED PATH
SINGLE PATH DELIVERY (CAN BE MULTIPATH ALSO)
16
Data caching and aggregation
  • Robust data delivery in case of node failure.
  • Validate with timestamps.
  • May use hierarchical scheme with one or more
    entry for distinct interest.

17
IN CASE OF NODE FAILURE USE ALTERNATIVE PATHS
18
Reinforcement
  • When to reinforce ?(quality/delay matrices can be
    chosen)
  • Whom to reinforce ?
  • How many to reinforce?
  • When to send negative reinforcement ?

19
TinyOS Implementation
20
(No Transcript)
21
Summary of results
  • Diffusion has achieved same delay of omniscient
    multicast.
  • Application level data dissemination has
    potential for energy saving.
  • This work did not develop the software
    architecture necessary for realizing attributes
    for in networking processing in an operational
    system.

22
Comparison of Directed Diffusion to
flooding and omniscient multicast
23
Work is influenced by
  • Multicast routing join techniques for interest
    propagation spt tree construction (or shared
    tree) for deciding reinforcement policies.
  • Declarative routing is similar in approach except
    no filters used.
  • Intentional naming system of mit has similar
    concept for naming as directed diffusion(but
    hierarchical not flat attribute value pair.)
  • In network processing for local repair is similar
    to router assist for localized error recovery.

24
What it Proposes?
  • A simple architecture that uses a topological-
    independent naming for low-level communication
    to achieve flexible, yet highly energy efficient
    application designs.

25
Discusses
  • Design space of protocols underlying directed
    diffusion.
  • (Where every sensor is task aware and possibly
    knows where it is.)

26
Evaluates
  • Design questions concerning naming and
    in-network processing encountered in deploying a
    sensor network and presents experimental results.

27
Issues of Concern
  • Ad hoc, self organizing, adaptive systems with
    predictable behavior
  • Collaborative processing, data fusion, multiple
    sensory modalities
  • Data analysis/mining

28
Issues yet to be resolved
  • How to handle congested network?
  • Semantics for gradients.
  • (Variant of D.D. Is gradient directed
    diffusion.)
  • Handling of more than one sources.
  • Negative reinforcement increases delay and
    contention (D.D. Uses mac layer unicast)

29
Optimization
  • Create processing points in the network.
  • High level interests/queries for activity
    triggers lower level local queries for particular
    data modalities and signatures (e.g. acoustic and
    vibration patterns that are mapped to the
    activity of interest)As opposed to generating
    detailed queries at sink points and relying on
  • opportunistic aggregation alone.

30
Work In Progress
  • Multi path reinforcing multiple upstream
    neighbors for load balancing and robustness.
  • Disjoint paths selection.
  • Opportunistic aggregation of source data
  • Managing gradients/resources.
  • Tiny diffusion for Motes.
  • Diffusion under mobility objects, nodes

31
Possible Areas of Future Work
  • Adaptation to local node densities.
  • How to map diffusions parameters to Diffusion
    needs?
  • Diffusion to work on Asymmetric links.
  • Intelligence in filters for decision making.

32
Reference
  • Design and implementation of INS.
  • Location aware routing.
  • Geocasting in mobile ad hoc networks
  • Location based multicast algorithms
  • Query localization techniques for on-demand
    routing protocols in ad-hoc net.
  • Declarative routing.

33
More Information
  • SCADDS project
  • http//www.isi.edu/scadds
  • ns-2 network simulator
  • http//www.isi.edu/nsnam/dist/ns-src-snapshot.tar.
    gz
  • testbed and software
  • http//www.isi.edu/scadds/testbeds.html
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