Download Directed Diffusion: A Scalable and Robust Communication

Directed Diffusion: A Scalable and Robust
Communication Paradigm for Sensor Networks
Charlmek Intanagonwiwat Ramesh Govindan
Deborah Estrin
Presentation By : Hardik Shah
.
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.
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
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.
Directed Diffusion
Data dissemination paradigm for
distributed network of sensors.
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.
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).)

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).
Architectural elements
Naming Scheme
 Interest propagation
 Data propagation
 Data caching and aggregation
 Reinforcement

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)
Interest propagation
Flooding.
 Location aware routing (or geo casting).
 Directional propagation on previously
cached data.

In paper they have used flooding approach.
Event
Source
interests
Sink
QUERY DIFFUSED IN
TO INTEREST
WHICH IS LIST OF
ATTRIBUTE VALUE
PAIRS
Interest Propagation (Flooding)
Have u seen any four
leg animal???
YES I HAVE
SEEN ONE….
INTIAL GRADIENTS
SETUP(VALUE+DIRECTION)
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.)

DATA DELIVERY THROUGH REINFORCED PATH
SINGLE PATH DELIVERY (CAN BE MULTIPATH ALSO)
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.
IN CASE OF NODE FAILURE USE ALTERNATIVE PATHS
Reinforcement

When to reinforce ?(quality/delay matrices can be
chosen)

Whom to reinforce ?

How many to reinforce?

When to send negative reinforcement ?
TinyOS Implementation
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.

Comparison of Directed Diffusion to flooding and omniscient multicast
Work is influenced by



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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.
What it Proposes?

A simple architecture that uses a topologicalindependent naming for low-level
communication to achieve flexible, yet highly
energy efficient application designs.
Discusses
Design space of protocols underlying directed
diffusion.
(Where every sensor is task aware and possibly
knows where it is.)
Evaluates
Design questions concerning naming and innetwork processing encountered in deploying
a sensor network and presents experimental
results.
Issues of Concern
Ad hoc, self organizing, adaptive systems
with predictable behavior
 Collaborative processing, data fusion,
multiple sensory modalities
 Data analysis/mining

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)

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.

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

Possible Areas of Future Work
Adaptation to local node densities.
 How to map diffusion’s parameters to
Diffusion needs?
 Diffusion to work on Asymmetric links.
 Intelligence in filters for decision making.

Reference
Design and implementation of INS.
 Location aware routing.
 Geocasting in mobile ad hoc networks
Location based multicast algorithms
 Query localization techniques for ondemand routing protocols in ad-hoc net.
 Declarative routing.

More Information

SCADDS project


ns-2: network simulator


http://www.isi.edu/scadds
http://www.isi.edu/nsnam/dist/ns-src-snapshot.tar.gz
testbed and software

http://www.isi.edu/scadds/testbeds.html