Implementation and Research Issues in Query Processing for Wireless Sensor Networks

1
Implementation and Research Issues in Query
Processing for Wireless Sensor Networks

• Wei Hong
• Intel Research, Berkeley
• whong_at_intel-research.net

Sam Madden MIT madden_at_csail.mit.edu
Adapted by L.B.
2
Declarative Queries

• Programming Apps is Hard
• Limited power budget
• Lossy, low bandwidth communication
• Require long-lived, zero admin deployments
• Distributed Algorithms
• Limited tools, debugging interfaces
• Queries abstract away much of the complexity
• Burden on the database developers
• Users get
• Safe, optimizable programs
• Freedom to think about apps instead of details

3
TinyDB Prototype declarativequery processor

• Platform Berkeley Motes TinyOS
• Continuous variant of SQL TinySQL
• Power and data-acquisition based in-network
  optimization framework
• Extensible interface for aggregates, new types of
  sensors

4
TinyDB Revisited
SELECT MAX(mag) FROM sensors WHERE mag gt
thresh SAMPLE PERIOD 64ms

• High level abstraction
• Data centric programming
• Interact with sensor network as a whole
• Extensible framework
• Under the hood
• Intelligent query processing query optimization,
  power efficient execution
• Fault Mitigation automatically introduce
  redundancy, avoid problem areas

App
Query, Trigger
Data
TinyDB
5
Feature Overview

• Declarative SQL-like query interface
• Metadata catalog management
• Multiple concurrent queries
• Network monitoring (via queries)
• In-network, distributed query processing
• Extensible framework for attributes, commands and
  aggregates
• In-network, persistent storage

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Architecture
TinyDB GUI
JDBC
TinyDB Client API
DBMS
PC side
0
Mote side
0
TinyDB query processor
2
1
3
8
4
5
6
Sensor network
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Data Model

• Entire sensor network as one single,
  infinitely-long logical table sensors
• Columns consist of all the attributes defined in
  the network
• Typical attributes
• Sensor readings
• Meta-data node id, location, etc.
• Internal states routing tree parent, timestamp,
  queue length, etc.
• Nodes return NULL for unknown attributes
• On server, all attributes are defined in
  catalog.xml
• Discussion other alternative data models?

8
Query Language (TinySQL)

• SELECT ltaggregatesgt, ltattributesgt
• FROM sensors ltbuffergt
• WHERE ltpredicatesgt
• GROUP BY ltexprsgt
• SAMPLE PERIOD ltconstgt ONCE
• INTO ltbuffergt
• TRIGGER ACTION ltcommandgt

9
Comparison with SQL

• Single table in FROM clause
• Only conjunctive comparison predicates in WHERE
  and HAVING
• No subqueries
• No column alias in SELECT clause
• Arithmetic expressions limited to column op
  constant
• Only fundamental difference SAMPLE PERIOD clause

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TinySQL Examples
Find the sensors in bright nests.
Sensors

• SELECT nodeid, nestNo, light
• FROM sensors
• WHERE light gt 400
• EPOCH DURATION 1s

1
Epoch Nodeid nestNo Light
0 1 17 455
0 2 25 389
1 1 17 422
1 2 25 405
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TinySQL Examples (cont.)
Count the number occupied nests in each loud
region of the island.
Epoch region CNT() AVG()
0 North 3 360
0 South 3 520
1 North 3 370
1 South 3 520
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Event-based Queries

• ON event SELECT
• Run query only when interesting events happens
• Event examples
• Button pushed
• Message arrival
• Bird enters nest
• Analogous to triggers but events are user-defined

13
Query over Stored Data

• Named buffers in Flash memory
• Store query results in buffers
• Query over named buffers
• Analogous to materialized views
• Example
• CREATE BUFFER name SIZE x (field1 type1, field2
  type2, )
• SELECT a1, a2 FROM sensors SAMPLE PERIOD d INTO
  name
• SELECT field1, field2, FROM name SAMPLE PERIOD d

14
Inside TinyDB
Multihop Network
Query Processor
10,000 Lines Embedded C Code 5,000 Lines
(PC-Side) Java 3200 Bytes RAM (w/ 768 byte
heap) 58 kB compiled code (3x larger than 2nd
largest TinyOS Program)
Filterlight gt 400
Schema
TinyOS
TinyDB
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Tree-based Routing

• Tree-based routing
• Used in
• Query delivery
• Data collection
• In-network aggregation
• Relationship to indexing?

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Sensor Network Research

• Very active research area
• Cant summarize it all
• Focus database-relevant research topics
• Some outside of Berkeley
• Other topics that are itching to be scratched
• But, some bias towards work that we find
  compelling

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Topics

• In-network aggregation
• Acquisitional Query Processing
• Heterogeneity
• Intermittent Connectivity
• In-network Storage
• Statistics-based summarization and sampling
• In-network Joins
• Adaptivity and Sensor Networks
• Multiple Queries

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Topics

• In-network aggregation
• Acquisitional Query Processing
• Heterogeneity
• Intermittent Connectivity
• In-network Storage
• Statistics-based summarization and sampling
• In-network Joins
• Adaptivity and Sensor Networks
• Multiple Queries

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Tiny Aggregation (TAG)

• In-network processing of aggregates
• Common data analysis operation
• Aka gather operation or reduction in
  programming
• Communication reducing
• Operator dependent benefit
• Across nodes during same epoch
• Exploit query semantics to improve efficiency!

Madden, Franklin, Hellerstein, Hong. Tiny
AGgregation (TAG), OSDI 2002.
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Basic Aggregation

• In each epoch
• Each node samples local sensors once
• Generates partial state record (PSR)
• local readings
• readings from children
• Outputs PSR during assigned comm. interval
• At end of epoch, PSR for whole network output at
  root
• New result on each successive epoch
• Extras
• Predicate-based partitioning via GROUP BY

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Illustration Aggregation
SELECT COUNT() FROM sensors
Interval 4
Sensor
Epoch
1 2 3 4 5
4 1
3
2
1
4
Interval
1
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Illustration Aggregation
SELECT COUNT() FROM sensors
Interval 3
Sensor
1 2 3 4 5
4 1
3 2
2
1
4
2
Interval
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Illustration Aggregation
SELECT COUNT() FROM sensors
Interval 2
Sensor
1 2 3 4 5
4 1
3 2
2 1 3
1
4
1
3
Interval
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Illustration Aggregation
SELECT COUNT() FROM sensors
Interval 1
5
Sensor
1 2 3 4 5
4 1
3 2
2 1 3
1 5
4
Interval
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Illustration Aggregation
SELECT COUNT() FROM sensors
Interval 4
Sensor
1 2 3 4 5
4 1
3 2
2 1 3
1 5
4 1
Interval
1
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Aggregation Framework

• As in extensible databases, TinyDB supports any
  aggregation function conforming to

Aggnfinit, fmerge, fevaluate Finit a0 ?
lta0gt Fmerge lta1gt,lta2gt ? lta12gt Fevaluate lta1gt
? aggregate value
Partial State Record (PSR)
Example Average AVGinit v ?
ltv,1gt AVGmerge ltS1, C1gt, ltS2, C2gt ? lt S1
S2 , C1 C2gt AVGevaluateltS, Cgt ? S/C
Restriction Merge associative, commutative
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Taxonomy of Aggregates

• TAG insight classify aggregates according to
  various functional properties
• Yields a general set of optimizations that can
  automatically be applied

Drives an API!
Property Examples Affects
Partial State MEDIAN unbounded, MAX 1 record Effectiveness of TAG
Monotonicity COUNT monotonic AVG non-monotonic Hypothesis Testing, Snooping
Exemplary vs. Summary MAX exemplary COUNT summary Applicability of Sampling, Effect of Loss
Duplicate Sensitivity MIN dup. insensitive, AVG dup. sensitive Routing Redundancy
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Use Multiple Parents

• Use graph structure
• Increase delivery probability with no
  communication overhead
• For duplicate insensitive aggregates, or
• Aggs expressible as sum of parts
• Send (part of) aggregate to all parents
• In just one message, via multicast
• Assuming independence, decreases variance

SELECT COUNT()
of parents n E(cnt) n (c/n
p2) Var(cnt) n (c/n)2 p2 (1 p2) V/n
P(link xmit successful) p P(success from A-gtR)
p2 E(cnt) c p2 Var(cnt) c2 p2 (1
p2) ? V
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Multiple Parents Results

• Better than previous analysis expected!
• Losses arent independent!
• Insight spreads data over many links

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Acquisitional Query Processing (ACQP)

• TinyDB acquires AND processes data
• Could generate an infinite number of samples
• An acqusitional query processor controls
• when,
• where,
• and with what frequency data is collected!
• Versus traditional systems where data is provided
  a priori

Madden, Franklin, Hellerstein, and Hong. The
Design of An Acqusitional Query Processor.
SIGMOD, 2003.
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ACQP Whats Different?

• How should the query be processed?
• Sampling as a first class operation
• How does the user control acquisition?
• Rates or lifetimes
• Event-based triggers
• Which nodes have relevant data?
• Index-like data structures
• Which samples should be transmitted?
• Prioritization, summary, and rate control

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Operator Ordering Interleave Sampling Selection
At 1 sample / sec, total power savings could be
as much as 3.5mW ? Comparable to processor!

• SELECT light, mag
• FROM sensors
• WHERE pred1(mag)
• AND pred2(light)
• EPOCH DURATION 1s

• E(sampling mag) gtgt E(sampling light)
• 1500 uJ vs. 90 uJ

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Exemplary Aggregate Pushdown

• SELECT WINMAX(light,8s,8s)
• FROM sensors
• WHERE mag gt x
• EPOCH DURATION 1s

• Novel, general pushdown technique
• Mag sampling is the most expensive operation!

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Topics

• In-network aggregation
• Acquisitional Query Processing
• Heterogeneity
• Intermittent Connectivity
• In-network Storage
• Statistics-based summarization and sampling
• In-network Joins
• Adaptivity and Sensor Networks
• Multiple Queries

35
Heterogeneous Sensor Networks

• Leverage small numbers of high-end nodes to
  benefit large numbers of inexpensive nodes
• Still must be transparent and ad-hoc
• Key to scalability of sensor networks
• Interesting heterogeneities
• Energy battery vs. outlet power
• Link bandwidth Chipcon vs. 802.11x
• Computing and storage ATMega128 vs. Xscale
• Pre-computed results
• Sensing nodes vs. QP nodes

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Computing Heterogeneity with TinyDB

• Separate query processing from sensing
• Provide query processing on a small number of
  nodes
• Attract packets to query processors based on
  service value
• Compare the total energy consumption of the
  network

• No aggregation
• All aggregation
• Opportunistic aggregation
• HSN proactive aggregation

Mark Yarvis and York Liu, Intels Heterogeneous
Sensor Network Project, ftp//download.intel.com/r
esearch/people/HSN_IR_Day_Poster_03.pdf.
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5x7 TinyDB/HSN Mica2 Testbed
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Data Packet Saving

• How many aggregators are desired?
• Does placement matter?

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Occasionally Connected Sensornets
internet
TinyDB Server
GTWY
TinyDB QP
Mobile GTWY
Mobile GTWY
GTWY
GTWY
TinyDB QP
TinyDB QP
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Occasionally Connected Sensornets Challenges

• Networking support
• Tradeoff between reliability, power consumption
  and delay
• Data custody transfer duplicates?
• Load shedding
• Routing of mobile gateways
• Query processing
• Operation placement in-network vs. on mobile
  gateways
• Proactive pre-computation and data movement
• Tight interaction between networking and QP

Fall, Hong and Madden, Custody Transfer for
Reliable Delivery in Delay Tolerant Networks,
http//www.intel-research.net/Publications/Berkele
y/081220030852_157.pdf.
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Distributed In-network Storage

• Collectively, sensornets have large amounts of
  in-network storage
• Good for in-network consumption or caching
• Challenges
• Distributed indexing for fast query dissemination
• Resilience to node or link failures
• Graceful adaptation to data skews
• Minimizing index insertion/maintenance cost

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Example DIM

• Functionality
• Efficient range query for multidimensional data.
• Approaches
• Divide sensor field into bins.
• Locality preserving mapping from m-d space to
  geographic locations.
• Use geographic routing such as GPSR.
• Assumptions
• Nodes know their locations and network boundary
• No node mobility

Xin Li, Young Jin Kim, Ramesh Govindan and Wei
Hong, Distributed Index for Multi-dimentional
Data (DIM) in Sensor Networks, SenSys 2003.
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Statistical Techniques

• Approximations, summaries, and sampling based on
  statistics and statistical models
• Applications
• Limited bandwidth and large number of nodes -gt
  data reduction
• Lossiness -gt predictive modeling
• Uncertainty -gt tracking correlations and changes
  over time
• Physical models -gt improved query answering

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Correlated Attributes

• Data in sensor networks is correlated e.g.,
• Temperature and voltage
• Temperature and light
• Temperature and humidity
• Temperature and time of day
• etc.

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IDSQ

• Idea task sensors in order of best improvement
  to estimate of some value
• Choose leader(s)
• Suppress subordinates
• Task subordinates, one at a time
• Until some measure of goodness (error bound) is
  met
• E.g. Mahalanobis Distance -- Accounts for
  correlations in axes, tends to favor minimizing
  principal axis

See Scalable Information-Driven Sensor Querying
and Routing for ad hoc Heterogeneous Sensor
Networks. Chu, Haussecker and Zhao. Xerox TR
P2001-10113. May, 2001.
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Model location estimate as a point with
2-dimensional Gaussian uncertainty.
Graphical Representation
Principal Axis
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MQSN Model-based Probabilistic Querying over
Sensor Networks
Joint work with Amol Desphande, Carlos Guestrin,
and Joe Hellerstein
Query Processor
Model
1
3
4
2
5
6
7
8
9
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MQSN Model-based Probabilistic Querying over
Sensor Networks
Query Processor
Model
Consult Model
1
3
4
2
5
6
7
8
9
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MQSN Model-based Probabilistic Querying over
Sensor Networks
Query Processor
Model
Consult Model
1
3
4
2
5
6
7
8
9
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MQSN Model-based Probabilistic Querying over
Sensor Networks
Query Results
Query Processor
Model
Update Model
1
3
4
2
5
6
7
8
9
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Challenges

• What kind of models to use ?
• Optimization problem
• Given a model and a query, find the best set of
  attributes to observe
• Cost not easy to measure
• Non-uniform network communication costs
• Changing network topologies
• Large plan space
• Might be cheaper to observe attributes not in
  query
• e.g. Voltage instead of Temperature
• Conditional Plans
• Change the observation plan based on observed
  values

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MQSN Current Prototype

• Multi-variate Gaussian Models
• Kalman Filters to capture correlations across
  time
• Handles
• Range predicate queries
• sensor value within x,y, w/ confidence
• Value queries
• sensor value x, w/in epsilon, w/ confidence
• Simple aggregate queries
• AVG(sensor value) ? n, w/in epsilon, w/confidence
• Uses a greedy algorithm to choose the observation
  plan

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In-Net Regression

• Linear regression simple way to predict future
  values, identify outliers

• Regression can be across local or remote values,
  multiple dimensions, or with high degree
  polynomials
• E.g., node A readings vs. node Bs
• Or, location (X,Y), versus temperature
• E.g., over many nodes

Guestrin, Thibaux, Bodik, Paskin, Madden.
Distributed Regression an Efficient Framework
for Modeling Sensor Network Data . Under
submission.
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In-Net Regression (Continued)

• Problem may require data from all sensors to
  build model
• Solution partition sensors into overlapping
  kernels that influence each other
• Run regression in each kernel
• Requiring just local communication
• Blend data between kernels
• Requires some clever matrix manipulation
• End result regressed model at every node
• Useful in failure detection, missing value
  estimation

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Exploiting Correlations in Query Processing

• Simple idea
• Given predicate P(A) over expensive attribute A
• Replace it with P over cheap attribute A such
  that P evaluates to P
• Problem unless A and A are perfectly
  correlated, P ? P for all time
• So we could incorrectly accept or reject some
  readings
• Alternative use correlations to improve
  selectivity estimates in query optimization
• Construct conditional plans that vary predicate
  order based on prior observations

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Exploiting Correlations (Cont.)

• Insight by observing a (cheap and correlated)
  variable not involved in the query, it may be
  possible to improve query performance
• Improves estimates of selectivities
• Use conditional plans
• Example

57
In-Network Join Strategies

• Types of joins
• non-sensor -gt sensor
• sensor -gt sensor
• Optimization questions
• Should the join be pushed down?
• If so, where should it be placed?
• What if a join table exceeds the memory available
  on one node?

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Choosing Where to Place Operators

• Idea choose a join node to run the operator
• Over time, explore other candidate placements
• Nodes advertise data rates to their neighbors
• Neighbors compute expected cost of running the
  join based on these rates
• Neighbors advertise costs
• Current join node selects a new, lower cost node

Bonfils Bonnet, Adaptive and Decentralized
Operator Placement for In-Network QueryProcessing
IPSN 2003.
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Topics

• In-network aggregation
• Acquisitional Query Processing
• Heterogeneity
• Intermittent Connectivity
• In-network Storage
• Statistics-based summarization and sampling
• In-network Joins
• Adaptivity and Sensor Networks
• Multiple Queries

60
Adaptivity In Sensor Networks

• Queries are long running
• Selectivities change
• E.g. night vs day
• Network load and available energy vary
• All suggest that some adaptivity is needed
• Of data rates or granularity of aggregation when
  optimizing for lifetimes
• Of operator orderings or placements when
  selectivities change (c.f., conditional plans for
  correlations)
• As far as we know, this is an open problem!

61
Multiple Queries and Work Sharing

• As sensornets evolve, users will run many queries
  simultaneously
• E.g., traffic monitoring
• Likely that queries will be similar
• But have different end points, parameters, etc
• Would like to share processing, routing as much
  as possible
• But how? Again, an open problem.

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Concluding Remarks

• Sensor networks are an exciting emerging
  technology, with a wide variety of applications
• Many research challenges in all areas of computer
  science
• Database community included
• Some agreement that a declarative interface is
  right
• TinyDB and other early work are an important
  first step
• But theres lots more to be done!