CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks

Abhinav Kamra, Vishal Misra and Dan Rubenstein - Columbia University(ACM SenSys 2007)

Presenter : Justin

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A few definitions

Distributive queries (e.g. MIN, MAX, COUNT, SUM) Form: f(p U q) = f( f(p), f(q) ), (p, q = set of

disjoint nodes) e.g. Sum: f(p U q) = |p| + |q|

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1011

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A few definitions (cont.) Two types of queries:

Duplicate-sensitive: e.g. SUM, COUNT Duplicate-insensitive: e.g. MIN, MAX

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Traditional data aggregation Goal: Combine data values while routing to

the sink Two schemes

Tree-based Multi-path

example

Tree-based(spanning tree)

Multi-path(DAG)

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Traditional data aggregation (cont.)

Tree-based Error-prone in dynamic networks Not accurate in failure-prone settings

Multi-path Bandwidth overkill in stable networks Have to avoid duplicate and redundant data Still loses accuracy in high mobility/loss scenarios

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CountTorrent: An adaptive approach Adapt to network conditions:

Stable networks: accurate tree-based aggregation Dynamic networks: multi-path aggregation,

accuracy degrades gracefully

Completely distributed: local decisions Can compute duplicate-sensitive and

duplicate-insensitive query aggregates

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CountTorrent: A conceptual overview Divide and conquer strategy Arrange information in a hierarchy using a

(prefix-free) binary labeling Combine disjoint information Adapt the labeling as network changes

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CountTorrent: Label assignment Each node is assigned a unique (binary) label

by its parent. When a new node joins:

Chooses one of its neighbors as parent Parent splits its label S into 2 separate labels S0

and S1: Child given label S1

h2

h3

h10

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h4h2

h3

h10

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Node h4 joinsChooses h1 as parent

0100

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CountTorrent: Data combining After a label is assigned to each node

All labels can be merged to form ε

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011

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400 11

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CountTorrent: Data combining Aggregating with tuples

Definition: Tuple = (binary label, aggregate value) pair

Labels differ only in last bit merge tuples

(11, 5)(01, 3)(001, 2) (011, 1)(10, 3)(001, 2)

(011, 1)(11, 5)(10, 3)(001, 2)

(011, 1)(1, 8)(001, 2)

Node A Node B

Neighbors exchange tuples randomly

Merge any tuples if possible

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CountTorrent: Variants Static CountTorrent

Exchange tuples with neighbors Dynamic CountTorrent

Addiction tuple cache contains tuples associated with the node’s children and its own label.

b

c

a(10,4)

(1100,2) (1100,2)(111,3)

(1101,1)

(111,3)……

d

(10,4)

(1100,2)

(1101,1)(111,3)

(10,4)(1100,0)

c

a

(111,3)……

d

(10,4)

(1101,1)(111,3)

Send up

node b leaves

node a reset value of

“1100” into 0

node d require new label from a

c

a(10,4)(11,3)

(11,3)……

d

(10,4)

(1101,1)(11,3)

Update down

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CountTorrent: Fine-tuning Random exchange is not efficient:

Convergence is slow Optimizations:

Intelligent Selection Carefully choose data to send to neighbors Minimize redundant and duplicate tuple exchanges

Preferred Diffusion Carefully choose neighbor to send data to Fast convergence in stable networks

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CountTorrent: Intelligent Selection Node A sending to neighbor B

Remembers what was sent to B Remembers what was received from B Only send tuples that are useful for B

(11, 5)(01, 3)(0010, 2) (10, 3)(001, 2)Node A Node B

(11, 5)(10, 3)(001, 2)

(1, 8)(001, 2)

Node a won’t send (11,5) again

(11, 5)(01, 3)(0010, 2)

Node a won’t send (0010,2) to node b, since “001” is the prefix of “0010”

(11, 5)(001, 2)(01, 3)(0010, 2)

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CountTorrent: Preferred Diffusion Preferential forwarding:

If any tuple useful for parent Send Else, if any tuple useful for a child Send Else, send to another neighbor

Stable networks: Mimics tree-based aggregation

Dynamic network: mix of tree-based and multi-path

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Simulations & Experiments Simulations:

Compare with other aggregation methods

Effect of Node joins & failures Aggregation in a mobile network

Experiments on Tossim & motes: CountTorrent implementation on

Crossbow micaz motes

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CountTorrent accuracy

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Bandwidth usage

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Adapting to node joins & failures

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COUNT aggregate in a mobile network

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CountTorrent on TOSSIM

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CountTorrent on micaz motes

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Conclusions Robust: Accurate even in lossy networks Adaptive: Data communication adapts to

changing topology Handles mobility: Close to accurate

aggregates Bandwidth-efficient: adapts to the stability

of the network to maintain accuracy Ubiquitous: All nodes get the aggregate

by design