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The Design of an The Design of an Acquisitional Query Acquisitional Query Processor for Sensor Processor for Sensor

NetworksNetworksCS851 Presentation 2005CS851 Presentation 2005

Presented by: Gang ZhouPresented by: Gang Zhou

University of VirginiaUniversity of Virginia

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OutlineOutline

Application Structure & Design GoalsApplication Structure & Design Goals Acquisitional Query LanguageAcquisitional Query Language Power-Aware OptimizationPower-Aware Optimization Power Sensitive Dissemination and Power Sensitive Dissemination and

RoutingRouting Processing QueriesProcessing Queries Conclusions and Future Work Conclusions and Future Work DiscussionDiscussion

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Application StructureApplication Structure

Queries Queries submitted in PCsubmitted in PC

Parsed, Parsed, optimized in PCoptimized in PC

Disseminated Disseminated and processed in and processed in networknetwork

Results flow Results flow back through the back through the routing treerouting tree

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Design GoalsDesign Goals

Provide a query processor-like Provide a query processor-like interfaceinterface to sensor networks to sensor networks

Use acquisitional techniques to Use acquisitional techniques to reduce reduce power consumptionpower consumption compared to traditional passive compared to traditional passive systemssystems

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How?How?

What is meant by acquisitional What is meant by acquisitional techniques?techniques? Where, when, and how often data is Where, when, and how often data is

acquired and delivered to query processing acquired and delivered to query processing operatorsoperators

Four related questionsFour related questions When should samples be taken?When should samples be taken? What sensors have relevant data?What sensors have relevant data? In what order should samples be taken?In what order should samples be taken? Is it worth to process and relay samples?Is it worth to process and relay samples?

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What’s the big deal?What’s the big deal?

Radio is Radio is expensiveexpensive

Sensing takes Sensing takes significant energysignificant energy

Four Energy Four Energy Levels:Levels: SnoozingSnoozing ProcessingProcessing Processing and Processing and

receivingreceiving TransmittingTransmitting

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RoadmapRoadmap

Application Structure & Design GoalsApplication Structure & Design Goals Acquisitional Query LanguageAcquisitional Query Language Power-Aware OptimizationPower-Aware Optimization Power Sensitive Dissemination and Power Sensitive Dissemination and

RoutingRouting Processing QueriesProcessing Queries Conclusions and Future Work Conclusions and Future Work DiscussionDiscussion

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An Acquisitional Query An Acquisitional Query LanguageLanguage

SQL-like queries in the form of SQL-like queries in the form of SELECT-FROM-WHERE SELECT-FROM-WHERE

SELECT nodeid, light, tempSELECT nodeid, light, temp FROM sensors FROM sensors SAMPLE INTERVAL 1s FOR 10s SAMPLE INTERVAL 1s FOR 10s

Sensors viewed as a single tableSensors viewed as a single table Unbounded, continuous data stream of Unbounded, continuous data stream of

valuesvalues Columns are sensor dataColumns are sensor data Rows are individual sensorsRows are individual sensors

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Why Windows?Why Windows?

Sensors table is an unbounded, Sensors table is an unbounded, continuous data streamcontinuous data stream

Operations such as sort and Operations such as sort and symmetric join are not allowed on symmetric join are not allowed on streamsstreams

They are allowed on bounded They are allowed on bounded subsets of the stream (windows)subsets of the stream (windows)

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WindowsWindows Windows in TinyDB are fixed-size Windows in TinyDB are fixed-size

materialization points over sensor streams.materialization points over sensor streams. Materialization points can be used in queriesMaterialization points can be used in queries ExampleExample

CREATECREATESTORAGE POINT recentlight SIZE 8STORAGE POINT recentlight SIZE 8AS (SELECT nodeid, light FROM sensorsAS (SELECT nodeid, light FROM sensorsSAMPLE INTERVAL 10s)SAMPLE INTERVAL 10s)

SELECT COUNT(*)SELECT COUNT(*)FROM sensors AS s, recentlight AS r1FROM sensors AS s, recentlight AS r1WHERE r.nodeid = s.nodeidWHERE r.nodeid = s.nodeidAND s.light < r1.lightAND s.light < r1.lightSAMPLE INTERVAL 10sSAMPLE INTERVAL 10s

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Temporal AggregationTemporal Aggregation Why Aggregation?Why Aggregation?

Reduce the quantity of data that must be Reduce the quantity of data that must be transmitted through the networktransmitted through the network

ExampleExample SELECT WINAVG (volume, 30s, 5s)SELECT WINAVG (volume, 30s, 5s)

FROM sensorsFROM sensorsSAMPLE INTERVAL 1sSAMPLE INTERVAL 1s

Report the average volume over the last 30 seconds Report the average volume over the last 30 seconds once every 5 seconds, sampling once per secondonce every 5 seconds, sampling once per second

How about spacial aggregation or spacial-How about spacial aggregation or spacial-temporal aggregation?temporal aggregation? It’s hard; needs communication; depending on It’s hard; needs communication; depending on

routing tree…routing tree…

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Event-Based QueriesEvent-Based Queries An alternative to continuous polling for dataAn alternative to continuous polling for data ExampleExample

ON EVENT bird-detector(loc):ON EVENT bird-detector(loc):SELECT AVG(light), AVG(temp), event.locSELECT AVG(light), AVG(temp), event.locFROM sensors AS sFROM sensors AS sWHERE dist(s.loc, event.loc) < 10mWHERE dist(s.loc, event.loc) < 10mSAMPLE INTERVAL 2s FOR 30sSAMPLE INTERVAL 2s FOR 30s Currently, events are

only signaled on the local node.

How about a fully distributed event propagation system?

What is the gain?

What is the pay?

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Lifetime-Based QueriesLifetime-Based Queries

ExampleExampleSELECT nodeid, accelSELECT nodeid, accel

FROM sensorsFROM sensorsLIFETIME 30 daysLIFETIME 30 days

The query specifies that the network The query specifies that the network shouldshould Run for as least 30 daysRun for as least 30 days Sampling light and acceleration sensors Sampling light and acceleration sensors

as quick as possible and still maintains as quick as possible and still maintains the life time goalthe life time goal

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Lifetime-Based QueriesLifetime-Based Queries Nodes perform cost-based analysis in order to Nodes perform cost-based analysis in order to

determine data rate for each nodedetermine data rate for each node

???

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Lifetime-Based QueriesLifetime-Based Queries

Tested a mote with a 24 week queryTested a mote with a 24 week query Sample rate was 15.2 seconds per sampleSample rate was 15.2 seconds per sample Took 9 voltage readings over 12 daysTook 9 voltage readings over 12 days

Reasonable to drop the first two data?

Reasonable to use data from the first 12 days to fit a line which covers 168 days?

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RoadmapRoadmap

Application Structure & Design GoalsApplication Structure & Design Goals Acquisitional Query LanguageAcquisitional Query Language Power-Aware OptimizationPower-Aware Optimization Power Sensitive Dissemination and Power Sensitive Dissemination and

RoutingRouting Processing QueriesProcessing Queries Conclusions and Future Work Conclusions and Future Work DiscussionDiscussion

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Power-Aware Power-Aware OptimizationOptimization

Where?Where? Queries optimized by base station before Queries optimized by base station before

disseminationdissemination why? why?

Cost-based optimization to yield lowest overall Cost-based optimization to yield lowest overall power consumptionpower consumption

Cost dominated by sampling and transmittingCost dominated by sampling and transmitting How?How?

Optimizer focuses on ordering joins, Optimizer focuses on ordering joins, selections, and sampling on individual nodesselections, and sampling on individual nodes

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Reordering Sampling and Reordering Sampling and PredicatesPredicates

Consider the queryConsider the query SELECT accel, magSELECT accel, mag

FROM sensorsFROM sensorsWHERE accel > c1WHERE accel > c1AND mag > c2AND mag > c2SAMPLE INTERVAL 1sSAMPLE INTERVAL 1s

Three optionsThree options Measure accel and mag; then process selectMeasure accel and mag; then process select Measure mag; filter; then measure accelMeasure mag; filter; then measure accel Measure accel; filter; then measure magMeasure accel; filter; then measure mag

First option always more expensive. First option always more expensive. Second option is more expensive than third, Second option is more expensive than third,

when Saccel is more selective than Smag.when Saccel is more selective than Smag. Second option can be cheaper if the Smag is Second option can be cheaper if the Smag is

highly selective.highly selective.

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Example 2Example 2

Another exampleAnother exampleSELECT MAX (light)SELECT MAX (light)

FROM sensorsFROM sensorsWHERE mag > xWHERE mag > xSAMPLE INTERVAL 8sSAMPLE INTERVAL 8s

Unless mag > x is very selective, it is cheaper Unless mag > x is very selective, it is cheaper to check if current light is greater than the to check if current light is greater than the previous maximum and then apply the previous maximum and then apply the predicate over mag, rather than first sampling predicate over mag, rather than first sampling mag.mag.

Reordering is called exemplary aggregate Reordering is called exemplary aggregate pushdownpushdown

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Event Query Batching Event Query Batching

Have a queryHave a query ON EVENT e (nodeid)ON EVENT e (nodeid)

SELECT a1SELECT a1

FROM sensors AS sFROM sensors AS s

WHERE s.nodeid = e.nodeidWHERE s.nodeid = e.nodeid

SAMPLE INTERVAL d FOR kSAMPLE INTERVAL d FOR k

Every time e occurs, an instance of the Every time e occurs, an instance of the internal query is started.internal query is started.

Multiple independent instances at the Multiple independent instances at the same time, independent sampling and same time, independent sampling and data deliveringdata delivering

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Solution:Solution: Convert event e into an event stream Convert event e into an event stream Rewrite the internal query as a sliding Rewrite the internal query as a sliding

window join between the event stream window join between the event stream and sensorsand sensors

ON EVENT e (nodeid)ON EVENT e (nodeid)

SELECT a1SELECT a1

FROM sensors AS sFROM sensors AS s

WHERE s.nodeid = e.nodeidWHERE s.nodeid = e.nodeid

SAMPLE INTERVAL d FOR kSAMPLE INTERVAL d FOR k

SELECT s.a1SELECT s.a1

FROM sensors AS s, events AS FROM sensors AS s, events AS ee

WHERE s.nodeid = e.nodeidWHERE s.nodeid = e.nodeid

AND e.type = eAND e.type = e

AND s.time – e.time <= k AND AND s.time – e.time <= k AND s.time > e.times.time > e.time

SAMPLE INTERVAL dSAMPLE INTERVAL d

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RoadmapRoadmap

Application Structure & Design GoalsApplication Structure & Design Goals Acquisitional Query LanguageAcquisitional Query Language Power-Aware OptimizationPower-Aware Optimization Power Sensitive Dissemination and Power Sensitive Dissemination and

RoutingRouting Processing QueriesProcessing Queries Conclusions and Future Work Conclusions and Future Work DiscussionDiscussion

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Semantic Routing TreesSemantic Routing Trees

Why SRT?Why SRT? It is a routing tree designed to allow each It is a routing tree designed to allow each

node to efficiently determine if any of the node to efficiently determine if any of the nodes below it will need to participate in a nodes below it will need to participate in a given query over some constant attributes.given query over some constant attributes.

Used to prune the routing tree.Used to prune the routing tree. What is SRT?What is SRT?

An SRT is an index over constant attribute An SRT is an index over constant attribute A that can be used to locate nodes that A that can be used to locate nodes that have data relevant to the query.have data relevant to the query.

It is an overlay on the network.It is an overlay on the network.

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How to use SRT?How to use SRT? When a query q with a predicate over A When a query q with a predicate over A

arrives at node n, n checks whether any arrives at node n, n checks whether any child’s value of A overlaps the query child’s value of A overlaps the query range of A in q:range of A in q: If yes, forward the query and prepare to If yes, forward the query and prepare to

receive results receive results If no, do not forward qIf no, do not forward q

Is query q applied locally:Is query q applied locally: If yes, execute the queryIf yes, execute the query If not, ignoredIf not, ignored

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How to build SRT?How to build SRT? Flood the SRT build request down the networkFlood the SRT build request down the network

Re-transmitted by every mote until every mote hears itRe-transmitted by every mote until every mote hears it If a node has no childrenIf a node has no children

Choose a parent p; report the value of A to pChoose a parent p; report the value of A to p should it be range?should it be range?

If a node has childrenIf a node has children Forward the request, and wait for replyForward the request, and wait for reply Upon reply from children, choose a parent p; report to Upon reply from children, choose a parent p; report to

p the range of values of A which it and its descendents p the range of values of A which it and its descendents cover cover

Since each constant attribute A may have a Since each constant attribute A may have a separate SRT, is the scheme scalable?separate SRT, is the scheme scalable?

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Evaluation of SRTEvaluation of SRT

SRT are limited to constant SRT are limited to constant attributesattributes

Even so, maintenance is requiredEven so, maintenance is required Possible to use for non-constant Possible to use for non-constant

attributes but cost can be prohibitiveattributes but cost can be prohibitive

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Evaluation of SRTEvaluation of SRT Compared three different strategies for Compared three different strategies for

building tree, random, closest, and clusterbuilding tree, random, closest, and cluster Random: pick a random parent from the nodes Random: pick a random parent from the nodes

with reliable communicationwith reliable communication Closest: pick the parent whose attribute value Closest: pick the parent whose attribute value

(index attribute) is closest(index attribute) is closest Cluster: by snooping siblings’ parent selection, Cluster: by snooping siblings’ parent selection,

each node try to pick the right parent, to minimize each node try to pick the right parent, to minimize the spread of attribute values underneath all of its the spread of attribute values underneath all of its available parentsavailable parents

Report results for two different sensor value Report results for two different sensor value distributions, random and geographicdistributions, random and geographic Random: each attribute value is randomly selected Random: each attribute value is randomly selected

from the interval [0,1000]from the interval [0,1000] Geographic: values among neighbor are highly Geographic: values among neighbor are highly

correlatedcorrelated

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SRT ResultsSRT Results

The Cluster scheme is superior to the random scheme and the closest scheme.

With the geographic distribution, the performance of the cluster scheme is close to the optimal.

Where is the data of SRT’s overhead?

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RoadmapRoadmap

Application Structure & Design GoalsApplication Structure & Design Goals Acquisitional Query LanguageAcquisitional Query Language Power-Aware OptimizationPower-Aware Optimization Power Sensitive Dissemination and Power Sensitive Dissemination and

RoutingRouting Processing QueriesProcessing Queries Conclusions and Future Work Conclusions and Future Work DiscussionDiscussion

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Processing QueriesProcessing Queries Queries have been optimized and Queries have been optimized and

distributed, what more can we do?distributed, what more can we do? Aggregate data that is sent back to the Aggregate data that is sent back to the

rootroot Prioritize data that needs to be sent Prioritize data that needs to be sent

(why??)(why??) Naïve - FIFONaïve - FIFO Winavg – average the two results at the Winavg – average the two results at the

queue’s head to make room for the new dataqueue’s head to make room for the new data Delta – Send result with most changesDelta – Send result with most changes

Adapt data rates and power consumptionAdapt data rates and power consumption

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Prioritization Prioritization ComparisonComparison

Sample rate was 4 Sample rate was 4 times faster than times faster than delivery rate.delivery rate.

Readings Readings generated by generated by shaking the sensorshaking the sensor

Delta seems to be Delta seems to be betterbetter

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AdaptationAdaptation

Not safe to Not safe to assume that assume that network channel network channel is uncontestedis uncontested

TinyDB reduces TinyDB reduces packets sent as packets sent as channel channel contention risescontention rises How much? No How much? No

detail!detail!

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Adaptation Adaptation

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RoadmapRoadmap

Application Structure & Design GoalsApplication Structure & Design Goals Acquisitional Query LanguageAcquisitional Query Language Power-Aware OptimizationPower-Aware Optimization Power Sensitive Dissemination and Power Sensitive Dissemination and

RoutingRouting Processing QueriesProcessing Queries Conclusions and Future Work Conclusions and Future Work DiscussionDiscussion

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Conclusions & Future Conclusions & Future WorkWork

Conclusions:Conclusions: Design of an acquisitional query processor for Design of an acquisitional query processor for

data collection in sensor networksdata collection in sensor networks Evaluation in the context of TinyDBEvaluation in the context of TinyDB

Future Work:Future Work: Selectivity of operators based upon range of Selectivity of operators based upon range of

sensorsensor Exemplary aggregate pushdownExemplary aggregate pushdown More sophisticated prioritization schemesMore sophisticated prioritization schemes Better re-optimization of sample rate based Better re-optimization of sample rate based

upon acquired dataupon acquired data

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DiscussionDiscussion

Is this the best way (right way?) to Is this the best way (right way?) to look at a sensor network?look at a sensor network?

Is their approximation of battery Is their approximation of battery lifetime sufficient?lifetime sufficient?

Was their evaluation of SRT good Was their evaluation of SRT good enough?enough?