1 / 47

Supporting Aggregate Queries Over Ad-Hoc Wireless Sensor Networks

Supporting Aggregate Queries Over Ad-Hoc Wireless Sensor Networks. Samuel Madden UC Berkeley. With Robert Szewczyk, Michael Franklin, and David Culler. WMCSA June 21, 2002. Motivation: Sensor Nets and In-Network Query Processing. Many Sensor Network Applications are Data Oriented

boaz
Download Presentation

Supporting Aggregate Queries Over Ad-Hoc Wireless Sensor Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Supporting Aggregate Queries Over Ad-Hoc Wireless Sensor Networks Samuel Madden UC Berkeley With Robert Szewczyk, Michael Franklin, and David Culler WMCSAJune 21, 2002

  2. Motivation: Sensor Nets and In-Network Query Processing • Many Sensor Network Applications are Data Oriented • Queries Natural and Efficient Data Processing Mechanism • Easy (unlike embedded C code) • Enable optimizations through abstraction • Aggregates Common Case • E.g. Which rooms are in use? • In-network processing a must • Sensor networks power and bandwidth constrained • Communication dominates power cost • Not subject to Moore’s law!

  3. Overview • Background • Sensor Networks • Our Approach: Tiny Aggregation (TAG) • Overview • Expressiveness • Illustration • Optimizations • Grouping • Current Status & Future Work

  4. Overview • Background • Sensor Networks • Our Approach: Tiny Aggregation (TAG) • Overview • Expressiveness • Illustration • Optimizations • Grouping • Current Status & Future Work

  5. Background: Sensor Networks • A collection of small, radio-equipped, battery powered, networked microprocessors • Typically Ad-hoc & Multihop Networks • Single devices unreliable • Very low power; tiny batteries power for months • Apps: Environment Monitoring, Personal Nets, Object Tracking • Data processing plays a key role!

  6. Berkeley Mica Motes & TinyOS • TinyOS operating system (services) • 4Mhz Processor • 4K RAM, 512K EEPROM, 128K code space • Single channel CSMA half-duplex radio @ 40kbits • Lossy: 20% loss @ 5ft in Ganesan et al. • Communication Very Expensive: 800 instrs/bit

  7. Overview • Background • Sensor Networks • Our Approach: Tiny Aggregation (TAG) • Overview • Expressiveness • Illustration • Optimizations • Grouping • Current Status & Future Work

  8. The Tiny Aggregation (TAG) Approach • Push declarative queries into network • Impose a hierarchical routing tree onto the network • Divide time into epochs • Every epoch, sensors evaluate query over local sensor data and data from children • Aggregate local and child data • Each node transmits just once per epoch • Pipelined approach increases throughput • Depending on aggregate function, various optimizations can be applied

  9. SELECT AVG(light) FROM sensors WHERE sound < 100 GROUP BY roomNo HAVING AVG(light) < 50 SQL Primer • SQL is an established declarative language; not wedded to it • Some extensions clearly necessary, e.g. for sample rates • We adopt a basic subset: • ‘sensors’ relation (table) has • One column for each reading-type, or attribute • One row for each externalized value • May represent an aggregation of several individual readings SELECT {aggn(attrn), attrs} FROM sensors WHERE {selPreds} GROUP BY {attrs} HAVING {havingPreds} EPOCH DURATION s

  10. Aggregation Functions • Standard SQL supports “the basic 5”: • MIN, MAX, SUM, AVERAGE, and COUNT • We support any function conforming to: Aggn={fmerge, finit, fevaluate} Fmerge{<a1>,<a2>}  <a12> finit{a0}  <a0> Fevaluate{<a1>}  aggregate value (Merge associative, commutative!) Partial Aggregate Example: Average AVGmerge {<S1, C1>, <S2, C2>}  < S1 + S2 , C1 + C2> AVGinit{v}  <v,1> AVGevaluate{<S1, C1>}  S1/C1

  11. Query Propagation • TAG propagation agnostic • Any algorithm that can: • Deliver the query to all sensors • Provide all sensors with one or more duplicate free routes to some root • Paper describes simple flooding approach • Query introduced at a root; rebroadcast by all sensors until it reaches leaves • Sensors pick parent and level when they hear query • Reselect parent after k silent epochs Query 1 P:0, L:1 2 3 P:1, L:2 P:1, L:2 4 P:2, L:3 6 P:3, L:3 5 P:4, L:4

  12. 1 2 3 4 5 Illustration: Pipelined Aggregation SELECT COUNT(*) FROM sensors Depth = d

  13. 1 2 3 4 5 Illustration: Pipelined Aggregation SELECT COUNT(*) FROM sensors Epoch 1 1 Sensor # 1 1 1 Epoch # 1

  14. 1 2 3 4 5 Illustration: Pipelined Aggregation SELECT COUNT(*) FROM sensors Epoch 2 3 Sensor # 1 2 2 Epoch # 1

  15. 1 2 3 4 5 Illustration: Pipelined Aggregation SELECT COUNT(*) FROM sensors Epoch 3 4 Sensor # 1 3 2 Epoch # 1

  16. 1 2 3 4 5 Illustration: Pipelined Aggregation SELECT COUNT(*) FROM sensors Epoch 4 5 Sensor # 1 3 2 Epoch # 1

  17. 1 2 3 4 5 Illustration: Pipelined Aggregation SELECT COUNT(*) FROM sensors Epoch 5 5 Sensor # 1 3 2 Epoch # 1

  18. Discussion 1 • Result is a stream of values • Ideal for monitoring scenarios • One communication / node / epoch • Symmetric power consumption, even at root • New value on every epoch • After d-1 epochs, complete aggregation • Given a single loss, network will recover after at most d-1 epochs • With time synchronization, nodes can sleep between epochs, except during small communication window 2 3 4 5

  19. Simulation Result Simulation Results 2500 Nodes 50x50 Grid Depth = ~10 Neighbors = ~20 Some aggregates require dramatically more state!

  20. Optimization: Channel Sharing • Insight: Shared channel enables optimizations • Suppress messages that won’t affect aggregate • E.g., in a MAX query, sensor with value v hears a neighbor with value ≥ v, so it doesn’t report • Applies to all such exemplary aggregates • Learn about query advertisements it missed • If a sensor shows up in a new environment, it can learn about queries by looking at neighbors messages. • Root doesn’t have to explicitly rebroadcast query!

  21. Optimization: Hypothesis Testing • Insight: Root can provide information that will suppress readings that cannot affect the final aggregate value. • E.g. Tell all the nodes that the MIN is definitely < 50; nodes with value ≥ 50 need not participate. • Works for any linear aggregate function • How is hypothesis computed? • Blind guess • Statistically informed guess • Observation over first few levels of tree / rounds of aggregate

  22. Optimization: Use Multiple Parents • For duplicate insensitive (e.g. MAX), or partitionable (e.g. COUNT) aggregates, • Send (part of) aggregate to all parents • Decreases variance • Dramatically, when there are lots of parents • No extra cost, since all messages broadcast

  23. Grouping • Value-based, complete partitioning of records • If query is grouped, sensors apply predicate to local readings on each epoch • Aggregate records tagged with group • When a child record (with group) is received: • If it belongs to a stored group, merge with existing record for that group • If not, just store it • At the end of each epoch, transmit one record per group

  24. Overview • Background • Sensor Networks • Our Approach: Tiny Aggregation (TAG) • Overview • Expressiveness • Illustration • Optimizations • Grouping • Current Status & Future Work

  25. Status & Future Work • Status • Simple simulator • Complete set of experiments, including behavior of algorithms in the face of loss • Generalization of algorithms beyond complete pipelining • Taxonomy of aggregates to allow optimizations on functional properties • Basic implementation (shown in demo) • Future work • Expressiveness issues • Aggregates over temporal data • Nested queries, e.g MAX(AVG(1000 readings) @ each node) • Correctness Issues in The Face Of Loss • How does the user know which nodes are and are not included in an aggregate?

  26. Summary • Declarative queries for aggregates • Straightforward, familiar interface • Enables optimizations • Snooping techniques for exemplary aggregates • Multiple parents for partitionable aggregates • Pipelined, epoch based algorithm • Streaming Results • Symmetric communication • Low-power friendly

  27. Questions?

  28. Grouping • GROUP BY expr • expr is an expression over one or more attributes • Evaluation of expr yields a group number • Each reading is a member of exactly one group Example: SELECT max(light) FROM sensors GROUP BY TRUNC(temp/10) Result:

  29. Having • HAVING preds • preds filters out groups that do not satisfy predicate • versus WHERE, which filters out tuples that do not satisfy predicate • Example: SELECT max(temp) FROM sensors GROUP BY light HAVING max(temp) < 100 Yields all groups with temperature under 100

  30. Group Eviction • Problem: Number of groups in any one iteration may exceed available storage on sensor • Solution: Evict! • Choose one or more groups to forward up tree • Rely on nodes further up tree, or root, to recombine groups properly • What policy to choose? • Intuitively: least popular group, since don’t want to evict a group that will receive more values this epoch. • Experiments suggest: • Policy matters very little • Evicting as many groups as will fit into a single message is good

  31. Simulation Environment • Java-based simulation & visualization for validating algorithms, collecting data. • Coarse grained event based simulation • Sensors arranged on a grid, radio connectivity by Euclidian distance • Communication model • Lossless: All neighbors hear all messages • Lossy: Messages lost with probability that increases with distance • Symmetric links • No collisions, hidden terminals, etc.

  32. Simulation Screenshot

  33. Experimental Results • Experiments with simulator • Performance of basic TAG • Benefits of hypothesis testing • Effect of loss • Most experiments in terms of bytes or messages sent, since message transmission is the dominant cost • Depends on radio being turned off between epochs and aggregation functions being cheap

  34. Experiment: Basic TAG Dense Packing, Ideal Communication

  35. Experiment: Hypothesis Testing Uniform Value Distribution, Dense Packing, Ideal Communication

  36. Experiment: Effects of Loss

  37. Experiment: Benefit of Cache

  38. 1 2 3 4 5 Pipelined Aggregates Value from 2 produced at time t arrives at 1 at time (t+1) • After query propagates, during each epoch: • Each sensor samples local sensors once • Combines them with PSRs from children • Outputs PSR representing aggregate state in the previous epoch. • After (d-1) epochs, PSR for the whole tree output at root • d = Depth of the routing tree • If desired, partial state from top k levels could be output in kth epoch • To avoid combining PSRs from different epochs, sensors must cache values from children Value from 5 produced at time t arrives at 1 at time (t+3)

  39. 1 2 4 3 5 Pipelining Example

  40. 1 2 4 3 5 Pipelining Example Epoch 0 <4,0,1> <5,0,1>

  41. 1 2 4 3 5 Pipelining Example Epoch 1 <2,0,2> <4,1,1> <3,0,2> <5,1,1>

  42. 1 2 4 3 5 Pipelining Example <1,0,3> Epoch 2 <2,0,4> <4,2,1> <3,1,2> <5,2,1>

  43. 1 2 4 3 5 Pipelining Example <1,0,5> Epoch 3 <2,1,4> <4,3,1> <3,2,2> <5,3,1>

  44. 1 2 4 3 5 Pipelining Example Epoch 4 <1,1,5> <2,2,4> <4,4,1> <3,3,2> <5,4,1>

  45. Optimization: Delta Compression • If a sensor’s reading is unchanged from previous epoch, it need not transmit. • Parents assume value is unchanged • Leverage child value cache • Periodic heartbeats to handle disconnection • Extension: if a sensor’s reading is unchanged by more than some threshold, it need not transmit • Similar to hypothesis testing with AVERAGE • Really future work: See C. Olsten, “Best-Effort Cache Synchronization”, SIGMOD 2002.

  46. Taxonomy of Aggregates • TAG insight: classifying aggregates according to various functional properties • Yields a general set of optimizations that can automatically be applied

More Related