Scalable Data Aggregation Scalable Data Aggregation for Dynamic Events in Sensor for Dynamic Events in Sensor

NetworksNetworksKai-Wei FanKai-Wei Fan

http://www.cse.ohio-state.edu/~fankhttp://www.cse.ohio-state.edu/~fank

Authors:Authors:Kai-Wei Fan, Sha Liu, and Prasun SinhaKai-Wei Fan, Sha Liu, and Prasun Sinha

Dept of Computer Science and EngineeringDept of Computer Science and EngineeringThe Ohio State UniversityThe Ohio State University

22

Wireless SensorsWireless Sensors

Genesis of Wireless SensorsGenesis of Wireless Sensors Miniaturization of sensing devices and actuatorsMiniaturization of sensing devices and actuators Miniaturization of computing platformsMiniaturization of computing platforms Miniaturization of wireless componentMiniaturization of wireless component

ApplicationsApplications Data Collection NetworksData Collection Networks

Environment Monitoring, Habitat MonitoringEnvironment Monitoring, Habitat Monitoring Event Triggered Networks (focus of this work)Event Triggered Networks (focus of this work)

Military Applications, National Asset ProtectionMilitary Applications, National Asset Protection

ChallengesChallenges Battery powerBattery power Limited bandwidthLimited bandwidth

Berkeley MicaDot

33

Data AggregationData Aggregation

MotivationMotivation Communication cost is higher than computation costCommunication cost is higher than computation cost In-network processing reduces number/size of packetsIn-network processing reduces number/size of packets

ChallengesChallenges Rare & dynamic eventsRare & dynamic events Protocol must use low energy for long network lifetimeProtocol must use low energy for long network lifetime

Related WorkRelated Work Static StructuresStatic Structures Dynamic StructuresDynamic Structures Structure-FreeStructure-Free

44

Data Aggregation ApproachesData Aggregation ApproachesStatic StructureStatic Structure

Routing on a pre-computed structureRouting on a pre-computed structure Suitable for unchanging traffic patternSuitable for unchanging traffic pattern Inappropriate for dynamic eventInappropriate for dynamic event

Long link stretch – avg Long link stretch – avg / / worstworst:: O(log O(log nn) ) // O(n) O(n)[Alon et al., SIAM 95][Alon et al., SIAM 95]

[LEACH, TWC ’02], [PEGASIS, TPDS ’02], [GIST, DCOSS ’06], SM[LEACH, TWC ’02], [PEGASIS, TPDS ’02], [GIST, DCOSS ’06], SMT, MST…T, MST…

55

Data Aggregation ApproachesData Aggregation ApproachesDynamic StructureDynamic Structure

Create a structure dynamicallyCreate a structure dynamically Optimization for a subset of nodesOptimization for a subset of nodes High control overhead for dynamic eventsHigh control overhead for dynamic events

[Directed Diffusion, Mobicom ‘00], [GIT, ICDCS ’02],[Directed Diffusion, Mobicom ‘00], [GIT, ICDCS ’02],[DCTC, Infocom ‘04][DCTC, Infocom ‘04]

66

Data Aggregation ApproachesData Aggregation ApproachesStructure-FreeStructure-Free

Improve aggregation without any structureImprove aggregation without any structure Suitable for dynamic event scenariosSuitable for dynamic event scenarios No guarantee of aggregation for allNo guarantee of aggregation for all

packetspackets

[DAA, Infocom ’06][DAA, Infocom ’06]

77

Our Proposed Approach:Our Proposed Approach:Tree on Directed Acyclic GraphTree on Directed Acyclic Graph

Combine benefits of structured and structure-free Combine benefits of structured and structure-free approachesapproaches

PropertiesProperties Structure-free data aggregationStructure-free data aggregation Packet forwarding on an implicit structurePacket forwarding on an implicit structure Guarantee early aggregation irrespective of network sizeGuarantee early aggregation irrespective of network size

AdvantagesAdvantages Low overhead of structure construction & maintenanceLow overhead of structure construction & maintenance Suitable for dynamic event scenariosSuitable for dynamic event scenarios Scalable in large scale sensor networksScalable in large scale sensor networks

88

ToD - Tree on DAGToD - Tree on DAG

One-Dimension illustrationOne-Dimension illustration

DefinitionDefinition Cell: Cell size is the maximum diameter of eventsCell: Cell size is the maximum diameter of events F-cluster: First-level Cluster. Composed of multiple cellsF-cluster: First-level Cluster. Composed of multiple cells S-cluster: Second-level Cluster. Composed of multiple cellsS-cluster: Second-level Cluster. Composed of multiple cells

Interleaved with F-clustersInterleaved with F-clusters

……

……………………

……………………

……

network

one row instance of the network

Cell

F-cluster S-cluster

99

ToD - Tree on DAGToD - Tree on DAG

sink

S-cluster

S-cluster-head

sink

sink

F-clusters

F-cluster-head

1010

Rule 0: forward packets to F-cluster-head by structure-free data agRule 0: forward packets to F-cluster-head by structure-free data aggregation protocol [Infocom ’06]gregation protocol [Infocom ’06]

Rule 1: event spans two cells, forward to sinkRule 1: event spans two cells, forward to sink

Rule 2: event spans one cell, forward to S-cluster-headRule 2: event spans one cell, forward to S-cluster-head

Dynamic ForwardingDynamic Forwarding

sink

sink

1111

C1

A4 B3

B1 C2

A3

A1 A2 B2

B4 C3 C4

D3

D1 D2

D4 E3

E1 E2

E4 F3

F1 F2

F4

G1 G2 H1 H2 I1 I2

Two-Dimension ToD ConstructionTwo-Dimension ToD Construction

A B C

D

G H I

E F

S1 S2

S3 S4

G3 G4 H3 H4 I3 I4

2Δ2Δ

2Δ

F-Clusters Cells S-Clusters

Δ: Maximum Diameter of an event

1212

Cluster-head SelectionCluster-head Selection

AssumptionsAssumptions Each node knows all nodes and their locations in its F-clusterEach node knows all nodes and their locations in its F-cluster Time synchronization – Low precision.Time synchronization – Low precision.

ApproachApproach Sort list of nodes based on node id: NSort list of nodes based on node id: N Hash current time to a node in the F-clusterHash current time to a node in the F-cluster

F-cluster = N[k] where k = H(current time); F-cluster = N[k] where k = H(current time); F-cluster-heads play the role of S-cluster-headsF-cluster-heads play the role of S-cluster-heads

BenefitsBenefits No cluster-head election/update overheadNo cluster-head election/update overhead Local synchronization – sync only within an F-clusterLocal synchronization – sync only within an F-cluster

1313

Dynamic Forwarding: Aggregating Dynamic Forwarding: Aggregating ClusterCluster

Sharing cluster-headSharing cluster-head F-cluster-head also takes the role of S-F-cluster-head also takes the role of S-

cluster-headcluster-head

BenefitsBenefits Avoids maintenance of S-cluster-headsAvoids maintenance of S-cluster-heads Nodes only need to know the F-cluster-Nodes only need to know the F-cluster-

head in their F-clusterhead in their F-cluster

IllustrationIllustration Assume sink is at bottom left cornerAssume sink is at bottom left corner

S-cluster

F-cluster

S-clusterhead

F-clusterhead

F-cluster & S-clusterhead

F-cluster, aggregating cluster for the S-cluster

1414

Dynamic Forwarding RulesDynamic Forwarding Rules

Nodes send data to their F-cluster-headNodes send data to their F-cluster-head

F-cluster-head forwards data to one/two S-cluster-headsF-cluster-head forwards data to one/two S-cluster-heads depends on which cells sent data to F-cluster-headdepends on which cells sent data to F-cluster-head only need to consider packets from one or two cellsonly need to consider packets from one or two cells

Guarantee aggregation in constant number of stepsGuarantee aggregation in constant number of steps independent of network sizeindependent of network size

1515

Dynamic Forwarding: ExampleDynamic Forwarding: ExampleOne cell scenarioOne cell scenario

S-cluster

Aggregating Cluster

1616

Dynamic Forwarding: ExampleDynamic Forwarding: ExampleTwo cells scenarioTwo cells scenario

S-cluster (S1)

Aggregating Cluster for S1 S-cluster (S2)

Aggregating Cluster for S2

1717

Dynamic Forwarding RulesDynamic Forwarding Rules

(a) (b) (c) (d)

(e) (f) (g) (h)

1818

Experimental ResultsExperimental Results

Evaluated ProtocolsEvaluated Protocols ToDToD Data Aware Anycast (DAA) (iData Aware Anycast (DAA) (i

ncludes RW)ncludes RW) Shortest Path Tree (SPT)Shortest Path Tree (SPT) SPT with Delay (SPT-D)SPT with Delay (SPT-D)

Testbed ConfigurationTestbed Configuration 105 Mica2-based motes105 Mica2-based motes 15 * 7 grid network15 * 7 grid network TX Range: 2 grid-neighbor (mTX Range: 2 grid-neighbor (m

ax 12 neighbors)ax 12 neighbors)

Evaluated MetricEvaluated Metric Normalized Number of TrNormalized Number of Tr

ansmissionsansmissions

ParametersParameters Maximum DelayMaximum Delay

ToD, DAA, SPT-DToD, DAA, SPT-D

Event SizeEvent Size

SourcesngContributiofNumber

onsTransmissiTotalofNumber

1919

Experiment Results - DelayExperiment Results - Delay

All nodes are sourcesAll nodes are sources Data rate: 0.1 pkt/sData rate: 0.1 pkt/s Data payload: 20 bytesData payload: 20 bytes 2 F-clusters in ToD2 F-clusters in ToD

Key observationsKey observations ToD performs better than ToD performs better than

DAADAA SPT-D is sensitive to the SPT-D is sensitive to the

delaydelay

2020

Experiment Results – Event SizeExperiment Results – Event Size

12 ~ 78 sources12 ~ 78 sources Data rate: 0.1 pkt/sData rate: 0.1 pkt/s Data payload: 20 bytesData payload: 20 bytes SPT-D delay: 6sSPT-D delay: 6s

Key observationsKey observations ToD performs bestToD performs best High variation of SPT-D: LHigh variation of SPT-D: L

ong stretch problemong stretch problem

2121

Simulation ResultsSimulation Results

Evaluated ProtocolsEvaluated Protocols ToDToD Data Aware Anycast (DAA)Data Aware Anycast (DAA) Shortest Path Tree (SPT)Shortest Path Tree (SPT) Optimal Aggregation Tree (OPT)Optimal Aggregation Tree (OPT)

Evaluated MetricEvaluated Metric Normalized Number of Normalized Number of

TransmissionsTransmissions

ParametersParameters Event SizeEvent Size Network SizeNetwork Size Cell SizeCell Size

SourcesngContributiofNumber

onsTransmissiTotalofNumber

2222

Simulation Results – Event SizeSimulation Results – Event Size

2000m X 1200m2000m X 1200m(35 X 58 grid network)(35 X 58 grid network)

TX Range: 50m (8 neigTX Range: 50m (8 neighbors)hbors)

Event moves at 10m/sEvent moves at 10m/s Data rate: 0.2 pkt/sData rate: 0.2 pkt/s Data payload: 50 bytesData payload: 50 bytes

Key ObservationsKey Observations TOD performs close to OPTOD performs close to OP

TT

2323

Simulation Results – Network SizeSimulation Results – Network Size

Vary the distance from thVary the distance from the event to sink: 400 ~ 16e event to sink: 400 ~ 1600m00m

Key ObservationsKey Observations SPT & DAA performance gSPT & DAA performance g

oes down with distanceoes down with distance ToD & OPT remain steadyToD & OPT remain steady

2000m

1200m400m

2424

Simulation Results – Cell SizeSimulation Results – Cell Size

Event Size: 200m, 400m, 6Event Size: 200m, 400m, 600m in diameter00m in diameter

Vary cell size from 50m to Vary cell size from 50m to 800m800m

Key ObservationsKey Observations ToD performs best on averagToD performs best on averag

e when the cell size is smaller e when the cell size is smaller than the event sizethan the event size

Larger cell size: bad for traffic Larger cell size: bad for traffic from sources to cluster-heads from sources to cluster-heads

Smaller cell size: bad for traffiSmaller cell size: bad for traffic from cluster-heads to sinkc from cluster-heads to sink

2525

ConclusionConclusion

Structure-Free AggregationStructure-Free Aggregation Dynamic Forwarding on ToD for ScalabilityDynamic Forwarding on ToD for Scalability Efficient Aggregation without overhead of structure comEfficient Aggregation without overhead of structure com

putation and maintenanceputation and maintenance

Future WorkFuture Work Dynamic Forwarding for irregular network topologyDynamic Forwarding for irregular network topology Early aggregation irrespective of event sizeEarly aggregation irrespective of event size

2626

Q&AQ&A