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

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. A few definitions. Distributive queries (e.g. MIN, MAX, COUNT, SUM)

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

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  1. CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks AbhinavKamra, VishalMisra and Dan Rubenstein - Columbia University (ACM SenSys 2007) Presenter : Justin

  2. 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| 11 4 2 10 6 5 21 3 1 9

  3. A few definitions (cont.) • Two types of queries: • Duplicate-sensitive: e.g. SUM, COUNT • Duplicate-insensitive: e.g. MIN, MAX

  4. 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)

  5. 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

  6. 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

  7. 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 1 10 0 11

  8. 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 00 0 0 h1 h1 01 10 10 h2 h2 h4 Node h4 joins Chooses h1 as parent 11 11 h3 h3

  9. CountTorrent: Data combining • After a label is assigned to each node • All labels can be merged to form ε ε 21 0 11 11 2 00 2 00 1 4 4 11 10 6 100 010 5 10 3 6 011 101 1 01 9

  10. (001, 2) (10, 3) (011, 1) (11, 5) (001, 2) (1, 8) (011, 1) CountTorrent: Data combining • Aggregating with tuples • Definition: Tuple = (binary label, aggregate value) pair • Labels differ only in last bit  merge tuples Node A Node B (001, 2) (01, 3) (11, 5) (001, 2) (10, 3) (011, 1) • Neighbors exchange tuples randomly • Merge any tuples if possible

  11. 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. (10,4) (11,3) (10,4) a node d require new label from a node a reset value of “1100” into 0 Send up (10,4) (1100,2) (10,4) (10,4) (1100,0) (10,4) (1100,2) (111,3) (1101,1) a a (11,3) (1101,1) d c (1100,2) node b leaves (11,3) …… b Update down (111,3) (1101,1) (111,3) (1101,1) d c d c (111,3) …… (111,3) ……

  12. 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

  13. 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 Node A Node B (0010, 2) (01, 3) (11, 5) (001, 2) (10, 3) Node a won’t send (11,5) again (001, 2) (10, 3) (11, 5) (0010, 2) (01, 3) (11, 5) (001, 2) (1, 8) (0010, 2) (01, 3) (11, 5) (001, 2) Node a won’t send (0010,2) to node b, since “001” is the prefix of “0010”

  14. 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

  15. 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

  16. CountTorrent accuracy

  17. Bandwidth usage

  18. Adapting to node joins & failures

  19. COUNT aggregate in a mobile network

  20. CountTorrent on TOSSIM

  21. CountTorrent on micaz motes

  22. 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

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