02/08/2005cs240 presentation 1 directed diffusion for wireless sensor networking by chalermek...

02/08/2005 CS240 Presentation 1

Directed Diffusion

for Wireless Sensor Networking

By Chalermek Intanagonwiwat, Ramesh Govindan,

Deborah Estrin, John Heidemann, and Fabio Silva

Presented by: Jin Sun

CS240 Presentation 202/08/2005

Outline

Introduction The problem Directed Diffusion Concepts Simulation Results Summary


Introduction A region requires event-

monitoring

Deploy sensors forming a distributed network Wireless networking Energy-limited nodes

On event, sensed and/or processed information delivered to the inquiring destination


The Problem Where should the data be

stored?

How should queries be routed to the stored data?

How should queries for sensor networks be expressed?

Where and how should aggregation be performed?

Event

Event

Sensor sources

Sensor sink

Directed Diffusion

A sensor field

On event, sensed and/or processed information delivered to the inquiring destination


Directed Diffusion

Initial Goals:Propose an application-aware paradigm to

facilitate efficient aggregation, and delivery of sensed data to inquiring destination


Directed Diffusion-how it works

Robust, efficient data distribution in sensor networks name data (not nodes), use physicality diffuse requests and responses across network optimize path with gradient-based feedback additional data can be processed and aggregated within the

network

“How many vehicles do you observe in the southeast quadrant?”

Source

Sink

aggregation point Additional source

Low data rate

High data rate


Directed Diffusion

Data Naming Interests and Gradient Data Propagation Reinforcement

Path establishmentPath failure / recoveryLoop elimination


Data Naming Expressing an Interest

Using attribute-value pairsE.g.,

Data replyUsing attribute-value pairsE.g.,

Type = Wheeled vehicle // detect vehicle locationInterval = 20 ms // send events every 20ms Duration = 10 s // Send for next 10 sField = [x1, y1, x2, y2] // from sensors in this area

Type = Wheeled vehicle // type of vehicle seenInstance = truck // instance of this typeIntensity = 0.6 // signal amplitude measureConfidence = 0.85 // confidence in the matchTimestamp = 01:20:34 // event generation timeField = [x1, y1, x2, y2] // from sensors in this area


Directed Diffusion




Interest Propagation

Inquirer (sink) broadcasts exploratory interest, i1 Intended to discover routes between source and sink

Neighbors update interest-cache and forwards i1 No way of knowing differentiating new interests from

repeated

Sink Sink

Sources Interest


Gradient EstablishmentRouted Data

Sink SinkGradient

Gradient for i1 set up to upstream neighbor No source routes Gradient – a weighted reverse link Low gradient Few packets per unit time needed


Directed Diffusion




Event-data propagation

Event e1 occurs, matches i1 in sensor cache e1 identified based on waveform pattern matching

Interest reply diffused down gradient (unicast) Diffusion initially exploratory (low packet-rate)

Cache filters suppress previously seen data Problem of bidirectional gradient avoided


Directed Diffusion




Reinforcement

From exploratory gradients, reinforce optimal path for high-rate data download Unicast

By requesting higher-rate-i1 on the optimal path

Exploratory gradients still exist – useful for faults

EventEvent

Sink AA sensor field

Reinforced gradient

Reinforced gradient

B

C

D


Path Failure / Recovery

Link failure detected by reduced rate, data loss Choose next best link (i.e., compare links based on

infrequent exploratory downloads) Negatively reinforce lossy link

Either send i1 with base (exploratory) data rate Or, allow neighbor’s cache to expire over time

EventEvent

Sink

Src AC

B

MD

Link A-M lossyA reinforces BB reinforces C …D need notA negative reinforces MM negative reinforces D


M gets same data from both D and P, but P always delivers late due to looping M negatively-reinforces (nr) P, P nr Q, Q nr M Loop {M Q P} eliminated

Conservative nr useful for fault resilience

Loop Elimination

A

QP

D M


Simulation Results

Compare directed diffusion to flooding Omniscient multicast

Key metrics: Average dissipated energy

per node energy dissipation / # events seen by sinks

Average packet delay

latency of event transmission to reception at sink Distinct event delivery

# of distinct events received / # of events originally sent


Average Dissipated Energy

flooding

DiffusionMulticast

In-network aggragation reduces DD redundancy- Flooding is poor because of multiple paths from source to sink


Delay

flooding

Diffusion

Multicast

DD finds least delay paths

- Floof]ding incurs latency due to high MAC contention, colission


Event Delivery Ratio under node failures

0 %

10%20%

Delivery ration degrades with more nodes failures- Graceful degradation indicate efficient negative reinforcement


Summary

Main ContributionsDescription of new networking paradigm

Interests, gradients, reinforcement Benefits of in-network processing Aggregation and nested-queries

Works with multiple sources and sinksCan perform local repairReinforce another path if a node dies


Summary (cont’d)

DisadvantagesDesign doesn’t deal with congestion or lossPeriodic broadcasts of interest reduces

network lifetimeNodes within range of human operator may

die quickly

02/08/2005 CS240 Presentation 24

Thank You!

02/08/2005cs240 presentation 1 directed diffusion for wireless sensor networking by chalermek...

Documents

stored data

data reply

delivery of sensed data

area slide

efficient data distribution

cs240 presentation12

cs240 presentation7

cs240 presentation10