1. DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein Columbia University

2. ACM SenSys 2007 2 A few definitions  Distributive queries (e.g. MIN, MAX, COUNT, SUM)  Form: f(p U q) = f( f(p), f(q) )  e.g. Sum: f(p U q) = |p| + |q| (p, q = set of disjoint nodes) 3 4 6 6 5 3 5 6 MAX distributive query  Two types of queries:  Duplicate-sensitive: e.g. SUM, COUNT  Duplicate-insensitive: e.g. MIN, MAX

3. ACM SenSys 2007 3 Traditional data aggregation  Goal: Combine data values while routing to the sink 2 1 5 1 2 1 5 1 2 3 1 6 9 Example aggregation using SUM query

4. ACM SenSys 2007 4 Data aggregation in sensor networks: Tree-based schemes  e.g. TAG1 [Madden02], Directed Diffusion [Govindan00]  Setup a spanning tree  Aggregate in-network along the paths  Pros:  Low bandwidth usage  Small message size  Cons:  High cost of communication failures  Bottleneck near the sink (root)  Lacks accuracy in high failure scenarios network spanning tree

5. ACM SenSys 2007 5 Data aggregation in sensor networks: Multi-path schemes  e.g. TAG2 [Madden02], Synopsis Diffusion [Nath04]  Setup a DAG  Partial results to multiple neighbors  Pros:  Robust to failures  High Reliability  Cons:  High bandwidth usage  Redundant and duplicate transmissions  Lacks accuracy in high failure scenarios network DAG

6. ACM SenSys 2007 6 What is lacking so far?  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 Different aggregation approaches Different network conditions

7. ACM SenSys 2007 7 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

8. ACM SenSys 2007 8 Network model  Traditional setup:  Set of connected sensor nodes (20 – 1000)  Nodes can join, leave, fail  Limited communication range (10s of meters)  Each node has a small buffer (~64 kB)

9. ACM SenSys 2007 9 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 0 1 10 11 Arranging the nodes in a virtual binary tree Any node can be root / sink 00 01

10. ACM SenSys 2007 10 Observation: Labeling is Prefix-free CountTorrent: Label assignment  Each node is assigned a unique (binary) label by its parent: Implicitly building a tree  When a new node joins  Chooses one of its neighbors as parent  Parent splits its label L into 2 separate labels L0 and L1: Child given label L1 h1h1 0 10 11 h2h2 h3h3 00 01 h1h1 10 11 h2h2 h3h3 h4h4 h4h4 Node h 4 joins 0 Chooses h 1 as parent

11. ACM SenSys 2007 11 CountTorrent: Data combining  After a label is assigned to each node  All labels can be merged to form ε 00 01 1 0 1 10 00 01 11 1 4 7 5 1 7 9 8 9 ε 17 Data combining using SUM query Works for any distributive query type

12. ACM SenSys 2007 12 CountTorrent: Data combining  Aggregating with tuples  Tuple = (binary label, aggregate value) pair  Labels differ only in last bit  merge tuples  Label 1 = Prefix(Label 2 )  Ignore Label 2 (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) Basic CountTorrent Strategy Neighbors randomly exchange tuples Merge whenever possible Node A Node B

13. ACM SenSys 2007 13 Fine-tuning CountTorrent  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

14. ACM SenSys 2007 14 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)(001, 2) (10, 3)(001, 2) (11, 5)(10, 3)(001, 2) (11, 5)(01, 3)(001, 2)(1, 8)(001, 2) ? Passive reception (wireless) can save transmissions Node A Node B

15. ACM SenSys 2007 15 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

16. ACM SenSys 2007 16 Simulations / Experiments Compare the accuracy and resilience of CountTorrent 1. Simulations:  Compare with other aggregation methods  Effect of Node joins/failures  Aggregation in a mobile network 2. Experiments on Tossim / motes:  CountTorrent implementation on Crossbow micaz motes

17. ACM SenSys 2007 17  100 nodes randomly placed in a 100x100 area  Communication range of 20  100 simulation runs  Accuracy = estimated/correct aggregate CountTorrent accuracy: Comparison with TAG/Sketches  CountTorrent mean results are accurate and 0 variance  Other approaches:  Lose accuracy with high loss rates  Have large variance

18. ACM SenSys 2007 18 Bandwidth usage: Comparison with TAG/Sketches  CountTorrent bandwidth usage increases with loss rate: More packets sent to stabilize the aggregate estimate at nodes

19. ACM SenSys 2007 19 Adapting to node joins/failures  As nodes join/leave  CountTorrent updates nodes’ labels  Query aggregate gets updated  100 node network: Nodes join and leave  Network size goes from 100 to 500 and back to 100  Each node is running CountTorrent  Aggregate = Average of estimates at all live nodes  Note: TAG/Sketches estimates do not adapt dynamically (will not work with changing topology)

20. ACM SenSys 2007 20 COUNT aggregate in a mobile network  As nodes move  CountTorrent repairs hierarchy tree  Query aggregate continuously updated  100 node network in a 100x100 grid  Nodes move according to RWPB (Random WayPoint Border) mobility model  Aggregate = Average of estimates at all live nodes

21. ACM SenSys 2007 21 CountTorrent on TOSSIM  50 nodes in a 5x10 grid  20 random nodes fail (at t=25) and come back (at t=50)  CountTorrent COUNT aggregate adapts to the changing topology

22. ACM SenSys 2007 22 CountTorrent on micaz motes  15 nodes in a 3x5 grid  7 random nodes fail (at t=25) and come back (at t=50)  CountTorrent COUNT aggregate adapts to the changing topology

23. ACM SenSys 2007 23 Conclusions  We propose CountTorrent  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

24. ACM SenSys 2007 24 Thanks for your patience ! For more information DNA Research Lab, Columbia University http://dna-wsl.cs.columbia.edu/

25. ACM SenSys 2007 25 Extra Slides

26. ACM SenSys 2007 26 CountTorrent: An adaptive approach  Two major components:  Hierarchical data aggregation (divide-and-rule)  Adaptive routing (repair as topology changes)  Adapt to network conditions:  Stable networks: accurate tree-based aggregation  Dynamic networks: multi-path aggregation, accuracy degrades gracefully

27. ACM SenSys 2007 27 CountTorrent Features  Adapts according to network conditions  Good network conditions: Tree-based  Very dynamic network: Multi-path  Aggregation decoupled with routing  Completely distributed (unsynchronized)  Accurate query results in good network conditions

28. ACM SenSys 2007 28 Classification of previous approaches  Tree-based vs Multi-path  Aggregation via routing vs decoupled from routing  Approximate aggregation vs best-effort  Synchronized vs unsynchronized protocols

29. ACM SenSys 2007 29  java SketchAggSensor/network/SensorNet work -sketch -sensors 100 -size 100 - random -radius 20 -rounds 100 -linkloss $ll |head -100 >q-$ll