DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile...
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![Page 1: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/1.jpg)
1DNA Research Group
CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor
Networks
Abhinav Kamra, Vishal Misra and Dan Rubenstein
Columbia University
![Page 2: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/2.jpg)
2ACM SenSys 2007
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)
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6
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6MAX distributive query
Two types of queries: Duplicate-sensitive: e.g. SUM, COUNT Duplicate-insensitive: e.g. MIN, MAX
![Page 3: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/3.jpg)
3ACM SenSys 2007
Traditional data aggregation
Goal: Combine data values while routing to the sink
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Example aggregation using SUM query
![Page 4: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/4.jpg)
4ACM SenSys 2007
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
![Page 5: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/5.jpg)
5ACM SenSys 2007
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
![Page 6: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/6.jpg)
6ACM SenSys 2007
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
![Page 7: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/7.jpg)
7ACM SenSys 2007
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
![Page 8: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/8.jpg)
8ACM SenSys 2007
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)
![Page 9: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/9.jpg)
9ACM SenSys 2007
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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•Arranging the nodes in a virtual binary tree
•Any node can be root / sink00
01
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10ACM SenSys 2007
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 L1h10
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h2
h3
0001
h1
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h2
h3
h4h4
Node h4 joins
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Chooses h1 as parent
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11ACM SenSys 2007
CountTorrent: Data combining After a label is assigned to each node
All labels can be merged to form ε
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ε17
Data combining using SUM query
Works for any distributive query type
![Page 12: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/12.jpg)
12ACM SenSys 2007
CountTorrent: Data combining Aggregating with tuples
Tuple = (binary label, aggregate value) pair
Labels differ only in last bit merge tuples
Label1 = Prefix(Label2) Ignore Label2(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
![Page 13: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/13.jpg)
13ACM SenSys 2007
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
![Page 14: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/14.jpg)
14ACM SenSys 2007
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
![Page 15: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/15.jpg)
15ACM SenSys 2007
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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16ACM SenSys 2007
Simulations / ExperimentsCompare 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
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17ACM SenSys 2007
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
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18ACM SenSys 2007
Bandwidth usage:Comparison with TAG/Sketches
CountTorrent bandwidth usage increases with loss rate: More packets sent to stabilize the aggregate estimate at nodes
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19ACM SenSys 2007
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)
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20ACM SenSys 2007
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
![Page 21: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/21.jpg)
21ACM SenSys 2007
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
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22ACM SenSys 2007
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
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23ACM SenSys 2007
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
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24ACM SenSys 2007
Thanks for your patience !
For more informationDNA Research Lab, Columbia University
http://dna-wsl.cs.columbia.edu/
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25ACM SenSys 2007
Extra Slides
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26ACM SenSys 2007
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
![Page 27: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/27.jpg)
27ACM SenSys 2007
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
![Page 28: DNA Research Group 1 CountTorrent: Ubiquitous Access to Query Aggregates in Dynamic and Mobile Sensor Networks Abhinav Kamra, Vishal Misra and Dan Rubenstein.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3e5503460f94a17418/html5/thumbnails/28.jpg)
28ACM SenSys 2007
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
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29ACM SenSys 2007
java SketchAggSensor/network/SensorNetwork -sketch -sensors 100 -size 100 -random -radius 20 -rounds 100 -linkloss $ll |head -100 >q-$ll