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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
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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
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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
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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…
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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]
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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]
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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
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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
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ToD - Tree on DAGToD - Tree on DAG
sink
S-cluster
S-cluster-head
sink
sink
F-clusters
F-cluster-head
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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
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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
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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
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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
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Dynamic Forwarding: ExampleDynamic Forwarding: ExampleOne cell scenarioOne cell scenario
S-cluster
Aggregating Cluster
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Dynamic Forwarding: ExampleDynamic Forwarding: ExampleTwo cells scenarioTwo cells scenario
S-cluster (S1)
Aggregating Cluster for S1 S-cluster (S2)
Aggregating Cluster for S2
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Dynamic Forwarding RulesDynamic Forwarding Rules
(a) (b) (c) (d)
(e) (f) (g) (h)
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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
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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
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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
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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
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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
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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
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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
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Q&AQ&A
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