March 6th, 2008Andrew Ofstad ECE 256, Spring 2008 TAG: a Tiny Aggregation Service for Ad-Hoc Sensor...

March 6th, 2008 Andrew OfstadECE 256, Spring 2008

TAG: a Tiny Aggregation Service for Ad-Hoc Sensor

NetworksSamuel Madden, Michael J.

Franklin,Joseph Hellerstein, Wei Hong

Published in 2002Research funds coming from IBM, Intel, and

Microsoft


Motivation

• Sensor networks are computers• Applications extract Info from network

• Aggregation saves resources• Philosophy: Aggregation as network service

• Extract info aggregation to higher level


TAG Contributions

• Basic query interface (like SQL)

• Power and time efficient distribution of queries

• Query Language Abstraction leads to Optimizations

• High level language leads to network loss techniques


Ad-Hoc Routing

• Doesn’t matter, as long as:– Query requests deliverable to all nodes

– Provides routes from every node to root

– No duplicate messages received by node

• TAG uses a routing tree


Routing Tree

1

2 3

56 4

RootP: NoneL: 0

P: 1L: 1

P: 1L: 1

P: 3L: 2

P: 3L: 2

P: 2L: 2

P = ParentL = Level


Query Model

• SQL style query syntax• Table called “sensors”• Schema known to base station• Append-only relational database


TAG Query Syntax Example

SELECT AVG(volume), room FROM sensorsWHERE floor = 6GROUP BY roomHAVING AVG(volume) > thresholdEPOCH DURATION 30s


TAG Query Syntax

SELECT {agg(expr), attrs} FROM sensorsWHERE {selPreds}GROUP BY {attrs}HAVING {havingPreds}EPOCH DURATION i


Aggregate Functions (agg)

3 Components:1) Merging function

f<z> = f(<x>, <y>)2) Initializer i3) Evaluator e

4

3 2

1

<S1, C1>

i(x) = <x1, 1>

<S2, C2>

f(<S1, C1>, <S2, C2>)= <S1 + S2, C1 + C2>

<S4, C4>

f(<S2, C2>, <S3, C3>, <S4, C4>)

<S3, C3>

i(x) = <x3, 1>

e(<S, C>) = S/C


Classes of Aggregates

• Duplicate Sensitivity• Exemplary or Summary• Monotonicity• Amount of Partial State Information

– Distributive– Algebraic– Holistic– Unique– Content Sensitive

Classification based on four categories:

QuickTime™ and a decompressor

are needed to see this picture.


Attribute Catalog• Queries contain named attributes

• Each mote has catalog of supported attributes

• Assume central query processor stores attributes of motes

• Mote returns NULL if attribute not supported

• Increases scalability


Algorithm• Two Phases:

– Query Distribution– Collection

• Single aggregate value every epoch

• Aggregation before propagation• Children deliver partial state in time interval specified by parent


Collection


Grouping

• Specified by GROUP BY query clause

SELECT AVG(volume), room FROM sensorsWHERE floor = 6GROUP BY roomHAVING AVG(volume) > thresholdEPOCH DURATION 30s

• Each sensor reading in single group

• Groups partitioned by attributes


Grouping Example




A Few Advantages…

• Decreased Communication• Nodes send one message per epoch

• “Lost Nodes” can reconnect


Simulation• Done in Java• Java Objects passed as messages




Optimizations

• Snooping on Shared Channels– Nodes can overhear missed query– Nodes can exclude certain messages

• Hypothesis Testing– Somewhat query specific– Nodes don’t send data if irrelevant

– Ex: root finds level 1 min, then uses “having” to narrow down query


Optimizations




Improving Loss Tolerance

Important because single node loss causes entire subtree loss…

1. Each node keeps track of neighbor link quality

• Keeps proportion of messages received

• Chooses parent’ over weaker parent

2. Node chooses a new parent if no messages received over set time


Loss Tolerance




Realistic Simulation



Caching: use old “cached” information when new info not received from children


Implementation

• Prototype with TinyOS motes




Conclusions

• High Level Query Language• Aggregation and Bandwidth Reduction

• Tight network integration, optimizable

• Currently working towards “event driven” model


Thanks!

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March 6th, 2008Andrew Ofstad ECE 256, Spring 2008 TAG: a Tiny Aggregation Service for Ad-Hoc Sensor...

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