vivek kinra cs-wmu1 overview of directed diffusion professor: -dr ajay gupta presented by: -vivek...

vivek kinra CS-WMU 1

Overview of Directed Overview of Directed DiffusionDiffusion

Professor: -Dr Ajay GuptaProfessor: -Dr Ajay Gupta

Presented By: -Vivek KinraPresented By: -Vivek Kinra

CS691 Spring2003CS691 Spring2003


Note: -Various slides of this presentation are created with the help of Note: -Various slides of this presentation are created with the help of presentation slides of UCLA ,USC and various other sourcespresentation slides of UCLA ,USC and various other sources


HistoryHistory

Research started to investigate the Research started to investigate the design of localized algorithm using design of localized algorithm using the Directed Diffusion modelthe Directed Diffusion model

The idea was developed in the The idea was developed in the context of a DARPA study by D.Estrincontext of a DARPA study by D.Estrin

Example of posing query for Example of posing query for tanks/vehicles……..tanks/vehicles……..


Design FeaturesDesign Features

Data centric: -Routing is based on Data centric: -Routing is based on data contained in sensor node and data contained in sensor node and may not need IDmay not need ID

Application focus on the data Application focus on the data generated by sensors. generated by sensors.

Data is named by attributes and Data is named by attributes and applications request data matching applications request data matching certain attribute values.certain attribute values.

Motivated by robustness, scaling and Motivated by robustness, scaling and energy efficiencyenergy efficiency


Directed DiffusionDirected Diffusion

Developed by ISI/USC and UCLA is a Developed by ISI/USC and UCLA is a novel network protocol built for info novel network protocol built for info retrieval and data dissemination.retrieval and data dissemination.

Data generated by nodes => Data generated by nodes => attributes(A1)attributes(A1)

Sinks/nodes request data=>Interest Sinks/nodes request data=>Interest into n/winto n/w

If A1 == Interest then(gradient setup If A1 == Interest then(gradient setup in n/w) (Pedestrians)in n/w) (Pedestrians)


contdcontd

Data pulled towards sinks Data pulled towards sinks =>receiver Initiated routing protocol=>receiver Initiated routing protocol

Example target trackingExample target tracking Intermediate node might aggregate Intermediate node might aggregate

datadata Since all nodes in directed diffusion Since all nodes in directed diffusion

are application aware so It is are application aware so It is completly application oriented.completly application oriented.


contdcontd

It is significantly different from IP It is significantly different from IP style communicationstyle communication

Not infeasible with IP or Ad-hoc Not infeasible with IP or Ad-hoc routingrouting

Imp Feature: - interest, data Imp Feature: - interest, data aggregation and propagation are aggregation and propagation are determined by localized interactiondetermined by localized interaction


Expected Architecture of Sensor Expected Architecture of Sensor Network Network

Required capabilities of sensor node: Required capabilities of sensor node: --

A Match box sized form factorA Match box sized form factor Battery power source Battery power source Power conserving processor clocked Power conserving processor clocked

at several hundred Mhzat several hundred Mhz MemoryMemory Radio modem Radio modem


contdcontd

Energy efficient MAC layerEnergy efficient MAC layer Can have more than 1 or more Can have more than 1 or more

sensors e.g seismic geophones, sensors e.g seismic geophones, infrared dipoles etcinfrared dipoles etc

The Atod conversion on such system The Atod conversion on such system produce 70ksamples/sec and 12 bit produce 70ksamples/sec and 12 bit resolutionresolution


For power issue, common signal For power issue, common signal processing functions offloaded to low processing functions offloaded to low power ASICpower ASIC

Processor woke up only when event of Processor woke up only when event of InterestInterest

A Sensor Node have a GPS receiverA Sensor Node have a GPS receiver The adv. Of these sensors is with very The adv. Of these sensors is with very

cheap in cost they obtain high SNR cheap in cost they obtain high SNR (attenuate with distance). (attenuate with distance).

Also can be deployed in huge amountAlso can be deployed in huge amount


Energy concernEnergy concern

Sensors Deployment falls in two Sensors Deployment falls in two ways: -ways: -

Large complex system deployed far.Large complex system deployed far. Short range hop-hop communication Short range hop-hop communication

is preferred over direct long range.is preferred over direct long range. Local computation to reduce data Local computation to reduce data

before transmission before transmission


ContdContd

In this organization, individual nodes In this organization, individual nodes reduce the sampled waveform reduce the sampled waveform generated by target (tank etc) into a generated by target (tank etc) into a relatively coarse grained “event” relatively coarse grained “event” description.description.

Description =>”codebook value” Description =>”codebook value” (event code)(event code)

Code->a timestamp,……Code->a timestamp,…… Nodes exchanged this event codeNodes exchanged this event code


Method descriptionMethod description

Task conveyed to sensor N/WTask conveyed to sensor N/W Nodes tasks it’s sensorsNodes tasks it’s sensors Matches sampled wave form against Matches sampled wave form against

locally stored librarylocally stored library Sensors in region may coordinate to Sensors in region may coordinate to

pick best estimate.pick best estimate. Packet:-Attributes (type, amplitude, Packet:-Attributes (type, amplitude,

Intensity, region, time stamp……)Intensity, region, time stamp……)


NamingNaming Given Set of Tasks supported by sensor Given Set of Tasks supported by sensor

network selecting a naming scheme is network selecting a naming scheme is first step in designing sensor networks.first step in designing sensor networks.

Basically list of attribute value pairs.Basically list of attribute value pairs. E.g. For tracking animal its attributes E.g. For tracking animal its attributes

should describe tasks like, type of should describe tasks like, type of animal,animal,

geographic location to track, interval geographic location to track, interval for sending updates, duration for which for sending updates, duration for which it was recorded (event occurrence it was recorded (event occurrence time)time)


Data sent in response to Data sent in response to InterestInterest

Type = four legged animal Type = four legged animal Instance = rabbit//instance of typeInstance = rabbit//instance of type location = [125,220]/node locationlocation = [125,220]/node location Intensity = 0.6/signal amplitudeIntensity = 0.6/signal amplitude Confidence = 0.85//confi.. in matchConfidence = 0.85//confi.. in match Timestamp = 01:20:40//event Timestamp = 01:20:40//event

generation timegeneration time


Sink periodically broadcasts an interest Sink periodically broadcasts an interest message to each of its neighbors.message to each of its neighbors.

Initial interest specifies a low data rate Initial interest specifies a low data rate (e.g 1 event/sec)(e.g 1 event/sec)

Interest are diff based on type, rect or Interest are diff based on type, rect or intervalinterval

Every node maintains a interest cache.Every node maintains a interest cache. Interest entries in cache do not contain Interest entries in cache do not contain

info about sink info about sink


Interest entryInterest entry

Time stamp (last received matching)Time stamp (last received matching) Gradient field (up to 1/neighbor)Gradient field (up to 1/neighbor) G.F => data rate field (requested by G.F => data rate field (requested by

neighbor)=>interval attributeneighbor)=>interval attribute Duration=timestamp – expiresATDuration=timestamp – expiresAT No EntryNo Entry No gradientNo gradient


Event

Sink

Have u seen any four leg animal???

QUERY DIFFUSED IN TO INTEREST WHICH IS LIST OF ATTRIBUTE VALUE PAIRS

Interest Propagation (Flooding)

interests


YES I HAVE SEEN ONE….

INTIAL GRADIENTS SETUP(VALUE+DIRECTION)Two-way Gradient setup


Gradient setup/reinforced pathGradient setup/reinforced path

Sink/Interest

source

I-PropagationInitial grad.. setup

Data …..reinforced path


Interest/gradientInterest/gradient

Task ={type,rect,a duration of 10 Task ={type,rect,a duration of 10 min}is instantiated at particular nodemin}is instantiated at particular node

Interval :- event data rateInterval :- event data rate Sink periodically broadcast interest Sink periodically broadcast interest

msg (& refresh interest) to neighbors.msg (& refresh interest) to neighbors. Initial Interest :-{rect,duration Initial Interest :-{rect,duration

attributes,larger interval attribute}attributes,larger interval attribute} Gradient expirationGradient expiration


DATA DELIVERY THROUGH REINFORCED PATH

SINGLE PATH DELIVERY (CAN BE MULTIPATH ALSO)


IN CASE OF NODE FAILURE USE ALTERNATIVE PATHS


ReinforcementReinforcement

When to reinforce ?(quality/delay When to reinforce ?(quality/delay matrices can be chosen)matrices can be chosen)

Whom to reinforce ?Whom to reinforce ?

How many to reinforce?How many to reinforce?

When to send negative When to send negative reinforcementreinforcement


When??When??

Sink initially diffuses a interest for a Sink initially diffuses a interest for a low event-rate.low event-rate.

Once sources starts detect a Once sources starts detect a matching target they send low rate matching target they send low rate events.events.

After the sink starts receiving these After the sink starts receiving these low data rate events it low data rate events it reinforcesreinforces one particular neighbor to draw down one particular neighbor to draw down higher quality.higher quality.


Whom??Whom??

To reinforce this neighbor, the sink To reinforce this neighbor, the sink re-sends the original interest re-sends the original interest message but with smaller interval message but with smaller interval (higher data rate).(higher data rate).

Two approaches for reinforceTwo approaches for reinforce Incremental approach:- Add min # of Incremental approach:- Add min # of

links to existing treelinks to existing tree Select links so that min energy is used Select links so that min energy is used


How ManyHow Many

Node must reinforce at least one Node must reinforce at least one neighborneighbor


Negative ReinforcementNegative Reinforcement

Earlier used A but now B is betterEarlier used A but now B is better One way :- time out all high data One way :- time out all high data

gradients in the n/wgradients in the n/w Sink would periodically reinforce B Sink would periodically reinforce B

and cease A that will degrade the and cease A that will degrade the path to A to lower data ratepath to A to lower data rate

Other way-:Degrade the path to A by Other way-:Degrade the path to A by re-sending the interest with low data re-sending the interest with low data raterate


Whether to negatively reinforce or Whether to negatively reinforce or notnot

N.R those neighbor from which no N.R those neighbor from which no new event have been received.new event have been received.

Or few events are coming.Or few events are coming. Significant experiments are required Significant experiments are required

before deciding which local rule before deciding which local rule achieve an energy efficient global achieve an energy efficient global behaviour behaviour


Issues of ConcernIssues of Concern

Ad hoc, self organizing, adaptive Ad hoc, self organizing, adaptive systems with predictable behaviorsystems with predictable behavior

Collaborative processing, data fusion, Collaborative processing, data fusion, multiple sensory modalities multiple sensory modalities

Data analysis/mining Data analysis/mining


Issues yet to be resolvedIssues yet to be resolved

How to handle congested network?How to handle congested network? Semantics for gradients.Semantics for gradients. Handling of more than one sources.Handling of more than one sources. Negative reinforcement increases Negative reinforcement increases

delay and contention delay and contention


commentscomments (battery life, size, processing power, (battery life, size, processing power,

memory, etc.)? The paper presents a memory, etc.)? The paper presents a motion-detection scenario for sensor motion-detection scenario for sensor networks.networks.

To identify an event sources must match To identify an event sources must match sampled sensor waveforms against sampled sensor waveforms against signatures stored in a local library. signatures stored in a local library.

To be useful, this library may have to store To be useful, this library may have to store several thousand such signatures or more.several thousand such signatures or more.

We could implement "task-centric" sensor We could implement "task-centric" sensor networks, where sensor nodes are focused networks, where sensor nodes are focused on one or two type of event detection. on one or two type of event detection.


Tiny DiffusionTiny Diffusion

Implementation of Diffusion on Implementation of Diffusion on resource constrained USB motesresource constrained USB motes 8 bit CPU, 8k program memory, 512 8 bit CPU, 8k program memory, 512

bytes data memorybytes data memory Subsets of full systemSubsets of full system Retains only gradients and condenses Retains only gradients and condenses

attributes to a single tagattributes to a single tag Entire system runs for less than 5.5 KB Entire system runs for less than 5.5 KB

memorymemory


contdcontd

Tiny OS adds ~3.5 KB and 144 bytes Tiny OS adds ~3.5 KB and 144 bytes of data (inclusive support for radio of data (inclusive support for radio and photo sensorand photo sensor

Diffusion adds ~2k code and 110 Diffusion adds ~2k code and 110 bytes of data to tiny OSbytes of data to tiny OS


Tiny Diffusion FunctionalityTiny Diffusion Functionality

Resource ConstraintResource Constraint Limited Cache size-currently 10 Limited Cache size-currently 10

entries of 2 bytes eachentries of 2 bytes each Limited ability to support multiple Limited ability to support multiple

traffic stream. currently support 5 traffic stream. currently support 5 concurrently active gradientsconcurrently active gradients


TinyOS ImplementationTinyOS Implementation


Gateway ArchitectureGateway Architecture

TINYOS

TinyDiffusion

PhotoData Source

Data Sink

Device Driver

LINUX

DIFFUSION

QueryData Sink

AcousticData Source

TINYOS

Transceiver

RFM

MOTEATMEL 8586 4MHz MCU8K program memory512 Bytes Data MemoryRFM Radio 900 MHz

PC104AMD Elan™SC40066MHz CPU16MB RAMForm Factor: 3.6" x 3.8" x 0.6"


Tiered TestbedTiered Testbed

PC-104+(linux) PC-104+(linux) with MoteNICwith MoteNIC

Tags, Sensor CardTags, Sensor Card UCB Motes UCB Motes

w/TinyOSw/TinyOS Yet to come: Yet to come:

SmartDust (highly SmartDust (highly specialized nodes)specialized nodes)

PS104

TAG

USB Mote

vivek kinra cs-wmu1 overview of directed diffusion professor: -dr ajay gupta presented by: -vivek...

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