3/14/2005 CS851: Data Services for Advanced System Applications 1 DFuse: A Framework for Distributed Data Fusion Rajnish Kumar, Matthew Wolenetz, Bikash

3/14/2005 CS851: Data Services for Advanced System Applications

1

DFuse: A Framework for

Distributed Data Fusion

Rajnish Kumar, Matthew Wolenetz, Bikash Agarwalla, JunSuk Shin, Phillip Hutto,

Arnab Paul, and Umakishore RamachandranGeorgia Institute of Technology

Presented by Lei Fang, Mar 14, 2005

3/14/2005 2CS851: Data Services for Advanced System Applications

OutlinesOutlines

MotivationMotivation DFuse DFuse

ArchitectureArchitecture Fusion ModuleFusion Module Placement ModulePlacement Module ImplementationImplementation EvaluationEvaluation

ConclusionConclusion


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Future Wireless Sensor Networks

More powerful hardware Resource-demand applications

• Streaming media• Surveillance• Image-based tracking• interactive vision

Hierarchal data fusion


Future Wireless Sensor Networks

Hierarchal data fusion


Data Fusion Challenges

Why is developing fusion applications challenging?

Time sensitive• Synchronization of the data from multiple streams

Distributed computing• Distributed fusion

Limited power• In-network aggregation• Power-aware routing• Dynamic relocation of fusion functions


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DFuseDFuse

Fusion API: Help to develop Fusion API: Help to develop distributed sensor fusion distributed sensor fusion applicationsapplications

Fusion Placement: Support Fusion Placement: Support automatic management of fusion automatic management of fusion point placement and migrationpoint placement and migration


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DFuse Architecture - Input

Target applicationsTarget applications HierarchicalHierarchical fusion functions on fusion functions on time time

sequencedsequenced data items data items Input Input

Task GraphTask Graph• Vertex: Vertex: source, sink, or immediate fusion points• Assume data sources are known at query time

Fusion Function• Transform a correlated set of inputs to a “fused” output item• May contract or expand the streaming data• Each fusion point performs one fusion function


DFuse Architecture - Components

Data Fusion Module Data Fusion Module Implement fusion API: Implement fusion API: allows any

synthesis operation to on stream of data to be specified as a fusion function

Fusion Placement Module Fusion Placement Module Determine the best placement of fusion

functions


DFuse Architecture


DFuse Architecture – Working principles

Fusion Module provides fusion API which Fusion Module provides fusion API which allows any synthesis operation to on stream of data to be specified as a fusion function. The user inputs the task graph and fusion functions

to DFuse at some designated node (root node)

The placement module at the root node maps the fusion functions onto the physical network Subject to some cost function Periodically re-evaluate the health of fusion point

nodes and their neighbors, and moves the fusion functions


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DFuse – Fusion Module

Fusion API CapabilitiesFusion API Capabilities Structure ManagementStructure Management

• Fusion Channel – Abstracts inputs and Fusion Channel – Abstracts inputs and encapsulates a general fusion function encapsulates a general fusion function (filter, collage, …) (filter, collage, …)

• provide data buffering and synchronization provide data buffering and synchronization facilities facilities

• E.g. items are fused and accessed by E.g. items are fused and accessed by timestamptimestamp



Fusion APIFusion API Correlation controlCorrelation control

• Specify and collect correlation sets (related Specify and collect correlation sets (related input items supplied to the fusion function)input items supplied to the fusion function)

Computation managementComputation management• Handle the specification, application and Handle the specification, application and

migration of fusion functionsmigration of fusion functions Memory managementMemory management

• Caching, prefetching, and buffer Caching, prefetching, and buffer managementmanagement



Fusion APIFusion API Failure/latency handlingFailure/latency handling

• Allow partial fusionAllow partial fusion• Accept a variable number of input itemsAccept a variable number of input items

Status and feedback handlingStatus and feedback handling• Allow interaction between fusion functions Allow interaction between fusion functions

and data sourcesand data sources• Supply Supply status informationstatus information via “status” register via “status” register• Support Support a command seta command set via “command” via “command”

registerregister


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Placement Module – Role Assignment

Distributed role assignment algorithmsDistributed role assignment algorithms Map a fusion function to a network nodeMap a fusion function to a network node Three roles: end point, relay, fusion pointThree roles: end point, relay, fusion point



Role Assignment HeuristicRole Assignment Heuristic Phase 1: Naive role assignment

• Start at root node• Algorithm: Naive Tree Building

Upon receiving the application task graph:if (I can host the root fusion function) { accept the root fusion function; for each input {

delegate subtree to “richest” neighbors; }else delegate entire tree to the “richest” neighbor;



Role Assignment HeuristicRole Assignment Heuristic Phase 2: Optimization (Role Transfer)

Improve overall health of the overlay network

• Health is valued by cost function (power, data transfer rate)• Each fusion node periodically broadcasts its health• If a neighboring node has better health, it informs the fusion node• The fusion node transfer fusion task to the neighbor with best

health

Phase 3: Maintenance (application is running)


Placement Module – Sample Cost Functions

The lower the cost function value, the better the node health!

Minimize transmission cost 1 (MT1):•

Minimize power variance (MPV):• CMPV( k ) = 1/power ( k )

Minimize Transmission cost vs. power (MTP):• CMTP ( k, f ) = CMT1 ( k, f ) * CMPV ( k )

Minimize transmission cost 2 (MT2):• CMT2( k, f ) = ( power ( k ) > threshold ? (CMT1 ( k, f ) :

Infinity )

How to transfer the role ?


Placement Module – Local Minima

Two simple metrics Simple hop-count Fusion data expansion or contraction information

Two types of optimization Linear: All inputs to a fusion node are coming from one

relay node Triangular: Multiple paths for inputs to reach a fusion

point


Placement Module –Optimization

Linear Optimization

1000

1000

1000 1000

1500

SourceRelay Fusion Point(data contraction)

Sink

1000

1000

1500 1500

SourceFusion Point

Relay Sink

1000

1000

1500 1500

SourceFusion Point(data expansion)

Relay Sink

1000

1000

1000 1000

1500

SourceRelay Fusion Point Sink


Placement Module –Optimization

Triangular Optimization

1000

1000

1000

1500

fp1

Relay

fp3(data contraction)

fp4

fp2

1000

1000

1000

1500

fp1

fp3

fp2


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DFuse – Implementation

Built on top of Stampede Stampede provides distributed threads, timestamps,

registers with synchronization, automatic garbage collection, communication channels, etc.

All buffers implemented as Stampede channels Map the fusion attribute to the timestamp of channel Status and command registers implemented with

Stampede registers

Decouple the fusion and placement module Interface: Built in communication channel and protocol


D-Stampede

Distributed programming system for Stream-based ubiquitous computing

Key Features Index and correlate different data streams temporally. Perform automatic distributed garbage collection of

unnecessary stream data. Provide parallelism Support distributed heterogeneous components. Dynamic application


D-Stampede Hardware model

Skiff

Skiff

camera

camera

Data Aggregators

Sensors

Actuators Unix / Linux / NT cluster

Channels / queuesChannels / queues

SensorFusion


D-Stampede Computational model-Thread/Channel GraphComputational model-Thread/Channel Graph

thread

Channel

thread

thread

threadthread

Channel

Channel

Channel

i_conn

o_conn

•many to many connections

•time sequenced data

•correlation of streams

•put(ts, item)

•get(ts, item)

•consume(ts)


D-Stampede ImplementationImplementation


D-Stampede Programming with D-StampedeProgramming with D-Stampede


Implementation – Fusion Module

Built on top of Stampede Functions:

createFC/destroyFC attachFC/detachFC: connect/disconnect

application thread with specified fusion channel

getFCItem/putFCItem: request/put fused data items


Implementation – Placement Module

Event-based simulation Input: Application task graph, network

topology Assumes ideal MAC and perfect routing

knowledge Simulates the fusion placement heuristic

• Naïve tree building• Optimization• Maintenance


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Evaluation – Fusion API cost


Evaluation – FP migration cost


Evaluation – Placement Algorithm Implement the tracker application with fusion Implement the tracker application with fusion

API and deploy it on the iPAQ farmAPI and deploy it on the iPAQ farm The location of filter and collage determined by

placement module movePC API: role transfer

Data Sources


Evaluation – Simulation Placement module adjusts power

level based on number of the amount of data sent and Based on ORiNOCO 802.11b PC card specs Only consider network communication costs

Optimization phase: runs for 2s Re-evaluate health every 100ms

During maintenance phase, role transfer evaluated every 50s


Evaluation – Traffic and Runtime


Evaluation – Traffic and Runtime


Evaluation – Power Variance


Evaluation – Runtime/Energy/Role Transfers


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ConclusionsConclusions

Future sensor networks will support data fusion applications

DFuse provides fusion API to ease the development such applications

Distributed fusion placement increases application runtime by dynamic role assignment


CritiquesCritiques Inflexible:

• DFuse can’t be implemented in current sensor network• Assume data sources are known before• Stationary sources and sinks• Complicated, resource demand• Assume every two node in the network is reachable• Migrate a particular fusion channel is a local decision

and only immediate neighbor nodes are queried to see if their heath for hosting the fusion channel would be better.

Insufficient evaluation:• Only consider network communication cost when Only consider network communication cost when

evaluate power consumptionevaluate power consumption• Implemented on iPAQ farm, but most of the work via

event-based simulation on desktop


Related workRelated work--- Data Fusion --- Data Fusion

InterfaceInterface TAG (Tiny Aggregation): TAG (Tiny Aggregation): PProvides a simple SQL-like declarative language for expressing

aggregation queries over streaming sensor data. Intelligently distributes and executes aggregation queries in the sensor

network in a time and power-efficient manner. Example:

Difference between Tag query and SQL query Output of a Tag query is a stream of values Record : <group id, aggregate value>. Each group is time-stamped and the readings

used to compute an aggregate record all belong to the same time interval, or epoch.

SELECT AVG(volume),room FROM sensorsWHERE floor = 6GROUP BY roomHAVING AVG(volume) > thresholdEPOCH DURATION 30s

SELECT (agg(expr), attrs) FROM sensorsWHERE {selPreds}GROUP BY {attrs}HAVING {havingPreds}EPOCH DURATION i


Related workRelated work--- Data Fusion --- Data Fusion

InterfaceInterface TAG (Tiny Aggregation): TAG (Tiny Aggregation): Structure of Data aggregationStructure of Data aggregation

• Three pieces:– Merging function f– Initializer i– Evaluator e

• General form: <z> = f(<x>,<y>)– <x>, <y>, <z> are multi-valued partial state record– Example:

f- for Average Each partial state record consists of <SUM, COUNT>

f(<S1, C1>,<S2,C2>) = <S1+S2, C1+C2>i- How to initiate a state record for a single sensor value

Eg. i(x) = <x,1> :Average over a sensor value of xe- Compute the actual value of aggregation e(<S,C>)

Pros:Pros:• Proven to give more accurate results then centralized approach• In-network aggregation: Save powerIn-network aggregation: Save power• SimpleSimple

Cons:Cons:• Static mapping fusions rolesStatic mapping fusions roles


Related workRelated work--- Dynamic Role --- Dynamic Role

AssignmentAssignment Generic Role Assignment for WSN

ArchitectureArchitecture


Related workRelated work--- Dynamic Role --- Dynamic Role

AssignmentAssignment Generic Role Assignment for WSN

Example: In-network aggregation Example: In-network aggregation

Rules/AlgorithmRules/Algorithm


Documents

3/14/2005 CS851: Data Services for Advanced System Applications 1 DFuse: A Framework for Distributed Data Fusion Rajnish Kumar, Matthew Wolenetz, Bikash