TAG: a Tiny Aggregation Service for Ad-Hoc Sensor Networks

1. TAG: a Tiny Aggregation Service for Ad-Hoc Sensor Networks Samuel Madden, Michael J. Franklin, Joseph Hellerstein, Wei Hong Published in 2002 Research funds coming from IBM, Intel, and Microsoft Andrew OfstadECE 256, Spring 2008

2. Motivation • Sensor networks are computers • Applications extract Info from network • Aggregation saves resources • Philosophy: Aggregation as network service • Extract info aggregation to higher level Andrew OfstadECE 256, Spring 2008

3. 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 Andrew OfstadECE 256, Spring 2008

4. 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 Andrew OfstadECE 256, Spring 2008

5. Routing Tree Root P = Parent L = Level P: None L: 0 1 P: 1 L: 1 P: 1 L: 1 2 3 P: 2 L: 2 6 P: 3 L: 2 P: 3 L: 2 4 5 Andrew OfstadECE 256, Spring 2008

6. Query Model • SQL style query syntax • Table called “sensors” • Schema known to base station • Append-only relational database Andrew OfstadECE 256, Spring 2008

7. TAG Query Syntax Example SELECT AVG(volume), room FROM sensors WHERE floor = 6 GROUP BY room HAVING AVG(volume) > threshold EPOCH DURATION 30s Andrew OfstadECE 256, Spring 2008

8. TAG Query Syntax SELECT {agg(expr), attrs} FROM sensors WHERE {selPreds} GROUP BY {attrs} HAVING {havingPreds} EPOCH DURATION i Andrew OfstadECE 256, Spring 2008

9. Aggregate Functions (agg) e(<S, C>) = S/C 3 Components: 1) Merging function f<z> = f(<x>, <y>) 2) Initializer i 3) Evaluator e 4 <S4, C4> f(<S2, C2>, <S3, C3>, <S4, C4>) <S2, C2> f(<S1, C1>, <S2, C2>) = <S1 + S2, C1 + C2> 3 2 <S3, C3> i(x) = <x3, 1> 1 <S1, C1> i(x) = <x1, 1> Andrew OfstadECE 256, Spring 2008

10. 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: Andrew OfstadECE 256, Spring 2008

11. 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 Andrew OfstadECE 256, Spring 2008

12. Algorithm • Two Phases: • Query Distribution • Collection • Single aggregate value every epoch • Aggregation before propagation • Children deliver partial state in time interval specified by parent Andrew OfstadECE 256, Spring 2008

13. Collection Andrew OfstadECE 256, Spring 2008

14. Grouping • Specified by GROUP BY query clause SELECT AVG(volume), room FROM sensors WHERE floor = 6 GROUP BY room HAVING AVG(volume) > threshold EPOCH DURATION 30s • Each sensor reading in single group • Groups partitioned by attributes Andrew OfstadECE 256, Spring 2008

15. Grouping Example Andrew OfstadECE 256, Spring 2008

16. A Few Advantages… • Decreased Communication • Nodes send one message per epoch • “Lost Nodes” can reconnect Andrew OfstadECE 256, Spring 2008

17. Simulation • Done in Java • Java Objects passed as messages Andrew OfstadECE 256, Spring 2008

18. 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 Andrew OfstadECE 256, Spring 2008

19. Optimizations Andrew OfstadECE 256, Spring 2008

20. Improving Loss Tolerance Important because single node loss causes entire subtree loss… • Each node keeps track of neighbor link quality • Keeps proportion of messages received • Chooses parent’ over weaker parent • Node chooses a new parent if no messages received over set time Andrew OfstadECE 256, Spring 2008

21. Loss Tolerance Andrew OfstadECE 256, Spring 2008

22. Realistic Simulation Caching: use old “cached” information when new info not received from children Andrew OfstadECE 256, Spring 2008

23. Implementation • Prototype with TinyOS motes Andrew OfstadECE 256, Spring 2008

24. Conclusions • High Level Query Language • Aggregation and Bandwidth Reduction • Tight network integration, optimizable • Currently working towards “event driven” model Andrew OfstadECE 256, Spring 2008

25. Thanks! Q and A Andrew OfstadECE 256, Spring 2008