Routing and Data Dissemination

Outline

Motivation and Challenges Basic Idea of Three Routing and Data

Dissemination schemes in Sensor Networks

Some Thoughts on Comparison of the Data dissemination schemes

Differences with Current Differences with Current NetworksNetworks

Difficult to pay special attention to any individual node: Collecting information within the specified region Collaboration between neighbors

Sensors may be inaccessible: embedded in physical structures. thrown into inhospitable terrain.

Differences with Current Differences with Current NetworksNetworks

Sensor networks deployed in very large ad hoc manner No static infrastructure

They will suffer substantial changes as nodes fail: battery exhaustion accidents new nodes are added.

Overall Design of Sensor Networks

Internet technology coupled with ad-hoc routing mechanism Each node has one IP address Each node can run applications and services Nodes establish an ad-hoc network amongst

themselves when deployed Application instances running on each node

can communicate with each other

Why Different and Difficult?

Content based and data centric Where are nodes whose temperatures will

exceed more than 10 degrees for next 10 minutes?

Tell me the location of the object ( with interest specification) every 100ms for 2 minutes.

Why Different and Difficult?

Multiple sensors collaborate to achieve one goal.

Intermediate nodes can perform data aggregation and caching in addition to routing.where, when, how?

Why Different and Difficult?

Not node-to-node packet switching, but node-to-node data propagation.

High level tasks are needed: At what speed and in what direction was that

elephant traveling? Is it the time to order more inventory?

Challenges

Energy-limited nodes Computation

Aggregate data Suppress redundant routing information

Communication Bandwidth-limited Energy-intensive

Goal: Minimize energy dissipation

Challenges

Scalability: ad-hoc deployment in large scale Fully distributed w/o global knowledge Large numbers of sources and sinks

Robustness: unexpected sensor node failures

Dynamically Change: no a-priori knowledge sink mobility target moving

Challenges

Topology or geographically issue Time : out-of-date data is not valuable Value of data is a function of time, location,

and its real sensor data. Is there a need for some general techniques

for different sensor applications? Small-chip based sensor nodes Large sensors, e.g., radar Moving sensors, e.g., robotics

Directed Diffusion

A Scalable and Robust Communication Paradigm for Sensor Networks

C. IntanagonwiwatR. Govindan

D. Estrin

Application Example: RemoteSurveillance

e.g., “Give me periodic report e.g., “Give me periodic reportss about ani about ani mal location in region A every t seconds” mal location in region A every t seconds”

Tell me in what direction that vehicle in Tell me in what direction that vehicle in region Y is moving?region Y is moving?

Basic Idea

In-network data processing (e.g., aggregation, caching)

Distributed algorithms using localized interactions

Application-aware communication primitives expressed in terms of named data

Elements of Directed Diffusion

Source and Sink Data

Data is named using attribute-value pairs

Interests Record indicating desire of certain types of

information is called interest

Gradients Gradients is set up within the network

designed to data matching the interest.

NamingNaming

Content based naming Tasks are named by a list of attribute – value pairs Task description specifies an interest for data

matching the attributes Animal tracking:

Interest ( Task ) DescriptionType = four-legged animalInterval = 20 msDuration = 1 minuteLocation = [-100, -100; 200, 400]

RequestRequest

Node dataType =four-legged animalInstance = elephantLocation = [125, 220]Confidence = 0.85Time = 02:10:35

ReplyReply

Interest The sink periodically broadcasts interest

messages to each of its neighbors Every node maintains an interest cache

Each item corresponds to a distinct interest No information about the sink Interest aggregation : identical type,

completely overlap rectangle attributes Each entry in the cache has several fields

Timestamp: last received matching interest Several gradients: data rate, duration, direction

Source

Sink

Interest = Interrogation

Gradient = Who is interested(data rate , duration, direction)

Setting Up Gradient

Neighbor’s choices :1. Flooding 2. Geographic routing3. Cache data to direct interests

Data Propagation

Sensor node computes the highest requested event rate among all its outgoing gradients

When a node receives a data: Find a matching interest entry in its cache

Examine the gradient list, send out data by rate Cache keeps track of recent seen data items

(loop prevention) Data message is unicast individually to the

relevant neighbors

Source

Sink

Reinforcing the Best Path

Low rate event Reinforcement = Increased interest

The neighbor reinforces a path:1. At least one neighbor2. Choose the one from whom it first received the latest event (low delay)3. Choose all neighbors from which new events were recently received

Local Behavior Choices

For propagating interests In the example, floodIn the example, flood More sophisticated behaviors possible: e.g.

based on cached information, GPS

For setting up gradients data-rate gradients are set up towards data-rate gradients are set up towards

neighbors who send an interestneighbors who send an interest.. Others possible: probabilistic

gradients, energy gradients, etc.

Local Behavior Choices

For data transmission Multi-path delivery with selective quality Multi-path delivery with selective quality

along different pathsalong different paths probabilistic forwarding single-path delivery, etc.

For reinforcement reinforce paths based on observed delaysreinforce paths based on observed delays losses, variances etc.

Initial simulation study of diffusion

Key metric Average Dissipated Energy per event delivered

indicates energy efficiency and network lifetime

Compare diffusiondiffusion to floodingflooding centrally computed tree (omniscient multicastomniscient multicast)

Diffusion Simulation Details

Simulator: -2ns-2ns - Network Size: 50 250 Nodes Transmission Range: 40m Constant Density: 1.95x10-3 nnnnnnn2 98( . nodes

in radius) MAC: Modified Contention-based MAC Energy Model: Mimic a realistic sensor radio [Pottie 2

000] 660 mW in transmission, 395 mW in reception

Diffusion Simulation

Surveillance application 5 70sources are randomly selected within a

n n n nnnnnn nn nnn nnnnn7 0 5 sinks are randomly selected across the field nnnn nn n nnnnnnnnnn2/ nnnnnnnnnn0.02/ nnnnn:64 nnnnn:36

Average Dissipated Energy

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

0 50 100 150 200 250 300

Ave

rag

e D

issi

pat

ed E

ner

gy

(Jo

ule

s/N

od

e/R

ecei

ved

Eve

nt)

Network Size

DiffusionDiffusion

Omniscient MulticastOmniscient Multicast

FloodingFlooding

Diffusion can outperform flooding and even omniscient multicast.Diffusion can outperform flooding and even omniscient multicast.

Comments

Primary concern is energy Simulations only

Only use five sources and five sinks How to exam scalability?