Scalable Data Aggregation for Dynamic Events in Sensor Networks

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Scalable Data Aggregation for Dynamic Events in
Sensor Networks

• Kai-Wei Fan
• http//www.cse.ohio-state.edu/fank
• Authors
• Kai-Wei Fan, Sha Liu, and Prasun Sinha
• Dept of Computer Science and Engineering
• The Ohio State University

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Wireless Sensors

• Genesis of Wireless Sensors
• Miniaturization of sensing devices and actuators
• Miniaturization of computing platforms
• Miniaturization of wireless component
• Applications
• Data Collection Networks
• Environment Monitoring, Habitat Monitoring
• Event Triggered Networks (focus of this work)
• Military Applications, National Asset Protection
• Challenges
• Battery power
• Limited bandwidth

Berkeley MicaDot
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Data Aggregation

• Motivation
• Communication cost is higher than computation
  cost
• In-network processing reduces number/size of
  packets
• Challenges
• Rare dynamic events
• Protocol must use low energy for long network
  lifetime
• Related Work
• Static Structures
• Dynamic Structures
• Structure-Free

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Data Aggregation ApproachesStatic Structure

• Routing on a pre-computed structure
• Suitable for unchanging traffic pattern
• Inappropriate for dynamic event
• Long link stretch avg / worst O(log n) /
  O(n)Alon et al., SIAM 95
• LEACH, TWC 02, PEGASIS, TPDS 02, GIST,
  DCOSS 06, SMT, MST

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Data Aggregation ApproachesDynamic Structure

• Create a structure dynamically
• Optimization for a subset of nodes
• High control overhead for dynamic events
• Directed Diffusion, Mobicom 00, GIT, ICDCS
  02,DCTC, Infocom 04

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Data Aggregation ApproachesStructure-Free

• Improve aggregation without any structure
• Suitable for dynamic event scenarios
• No guarantee of aggregation for allpackets
• DAA, Infocom 06

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Our Proposed ApproachTree on Directed Acyclic
Graph

• Combine benefits of structured and structure-free
  approaches
• Properties
• Structure-free data aggregation
• Packet forwarding on an implicit structure
• Guarantee early aggregation irrespective of
  network size
• Advantages
• Low overhead of structure construction
  maintenance
• Suitable for dynamic event scenarios
• Scalable in large scale sensor networks

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ToD - Tree on DAG

• One-Dimension illustration
• Definition
• Cell Cell size is the maximum diameter of
  events
• F-cluster First-level Cluster. Composed of
  multiple cells
• S-cluster Second-level Cluster. Composed of
  multiple cells
• Interleaved with F-clusters

Cell
F-cluster
S-cluster

one row instance of the network



network
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ToD - Tree on DAG
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Dynamic Forwarding

• Rule 0 forward packets to F-cluster-head by
  structure-free data aggregation protocol Infocom
  06
• Rule 1 event spans two cells, forward to sink
• Rule 2 event spans one cell, forward to
  S-cluster-head

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Two-Dimension ToD Construction
C1
B1
C2
A1
A2
B2
C3
C4
A4
B3
A3
B4
S1
S2
D1
D2
E1
E2
F1
F2
A
B
C
D3
D4
E3
E4
F3
F4
S3
S4
D
E
F
G1
G2
H1
H2
I1
I2
G
H
I
G3
G4
H3
H4
I3
I4
2?
2?
2?
F-Clusters
Cells
S-Clusters
? Maximum Diameter of an event
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Cluster-head Selection

• Assumptions
• Each node knows all nodes and their locations in
  its F-cluster
• Time synchronization Low precision.
• Approach
• Sort list of nodes based on node id N
• Hash current time to a node in the F-cluster
• F-cluster Nk where k H(current time)
• F-cluster-heads play the role of S-cluster-heads
• Benefits
• No cluster-head election/update overhead
• Local synchronization sync only within an
  F-cluster

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Dynamic Forwarding Aggregating Cluster

• Sharing cluster-head
• F-cluster-head also takes the role of
  S-cluster-head
• Benefits
• Avoids maintenance of S-cluster-heads
• Nodes only need to know the F-cluster-head in
  their F-cluster
• Illustration
• Assume sink is at bottom left corner

S-cluster
S-clusterhead
F-clusterhead
F-cluster
F-cluster S-clusterhead
F-cluster, aggregating cluster for the S-cluster
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Dynamic Forwarding Rules

• Nodes send data to their F-cluster-head
• F-cluster-head forwards data to one/two
  S-cluster-heads
• depends on which cells sent data to
  F-cluster-head
• only need to consider packets from one or two
  cells
• Guarantee aggregation in constant number of steps
• independent of network size

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Dynamic Forwarding ExampleOne cell scenario
S-cluster
Aggregating Cluster
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Dynamic Forwarding ExampleTwo cells scenario
S-cluster (S1)
Aggregating Cluster for S1
S-cluster (S2)
Aggregating Cluster for S2
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Dynamic Forwarding Rules
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Experimental Results

• Evaluated Protocols
• ToD
• Data Aware Anycast (DAA) (includes RW)
• Shortest Path Tree (SPT)
• SPT with Delay (SPT-D)
• Testbed Configuration
• 105 Mica2-based motes
• 15 7 grid network
• TX Range 2 grid-neighbor (max 12 neighbors)

• Evaluated Metric
• Normalized Number of Transmissions
• Parameters
• Maximum Delay
• ToD, DAA, SPT-D
• Event Size

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Experiment Results - Delay

• All nodes are sources
• Data rate 0.1 pkt/s
• Data payload 20 bytes
• 2 F-clusters in ToD
• Key observations
• ToD performs better than DAA
• SPT-D is sensitive to the delay

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Experiment Results Event Size

• 12 78 sources
• Data rate 0.1 pkt/s
• Data payload 20 bytes
• SPT-D delay 6s
• Key observations
• ToD performs best
• High variation of SPT-D Long stretch problem

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Simulation Results

• Evaluated Protocols
• ToD
• Data Aware Anycast (DAA)
• Shortest Path Tree (SPT)
• Optimal Aggregation Tree (OPT)

• Evaluated Metric
• Normalized Number of Transmissions
• Parameters
• Event Size
• Network Size
• Cell Size

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Simulation Results Event Size

• 2000m X 1200m(35 X 58 grid network)
• TX Range 50m (8 neighbors)
• Event moves at 10m/s
• Data rate 0.2 pkt/s
• Data payload 50 bytes
• Key Observations
• TOD performs close to OPT

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Simulation Results Network Size

• Vary the distance from the event to sink 400
  1600m
• Key Observations
• SPT DAA performance goes down with distance
• ToD OPT remain steady

2000m
1200m
400m
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Simulation Results Cell Size

• Event Size 200m, 400m, 600m in diameter
• Vary cell size from 50m to 800m
• Key Observations
• ToD performs best on average when the cell size
  is smaller than the event size
• Larger cell size bad for traffic from sources to
  cluster-heads
• Smaller cell size bad for traffic from
  cluster-heads to sink

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Conclusion

• Structure-Free Aggregation
• Dynamic Forwarding on ToD for Scalability
• Efficient Aggregation without overhead of
  structure computation and maintenance
• Future Work
• Dynamic Forwarding for irregular network topology
• Early aggregation irrespective of event size

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QA