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Database Middleware for Sensor Networks

Database Middleware for Sensor Networks. Sam Madden Assistant Professor, MIT madden@csail.mit.edu. Slides prepared with Wei Hong. Berkeley Mote. Motivation. Sensor networks (aka sensor webs, emnets) are here Several widely deployed HW/SW platforms

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Database Middleware for Sensor Networks

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  1. Database Middleware for Sensor Networks Sam Madden Assistant Professor, MIT madden@csail.mit.edu Slides prepared with Wei Hong

  2. Berkeley Mote Motivation • Sensor networks (aka sensor webs, emnets) are here • Several widely deployed HW/SW platforms • Low power radio, small processor, RAM/Flash • Variety of (novel) applications: scientific, industrial, commercial • Great platform for mobile + ubicomp experimentation • Real, hard research problems to be solved • Networking, systems, languages, databases • Central problem: ease of access, appropriate programming abstractions I will summarize: • Low-level sensornet issues • A particular middleware architecture: • TinyDB + TASK • Current and future research middleware ideas

  3. Some Sensornet Apps smart cooling in data centers redwood forest microclimate monitoring http://www.hpl.hp.com/research/dca/smart_cooling/ And More… condition-based maintenance • Homeland security • Container monitoring • Mobile environmental apps • Bird tracking • Zebranet • Home automation • Etc! structural integrity

  4. External Tools Client Tools GUIs,etc Stable Store(DBMS) Middleware Field Tools Local Servers Sensor Network TinyDB Architectural Overview Internet Directed Diffusion COUGAR Middleware Issues: APIs for current + historical access? Which data when? How to act on data? Network and node status?

  5. 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

  6. TinyDB: Declarative Query Interface to Sensornets • 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

  7. Agenda • Part 1 : Sensor Networks (40 mins) • TinyOS • NesC • Part 2: TinyDB + TASK (50 mins) • Data Model and Query Language • Software Architecture • 30 minute break • Part 3: Alternative Middleware (1:30 mins) Architectures + Research Directions • Finish around 12

  8. Part 1 • Sensornet Background • Motes + Mote Hardware • TinyOS • Programming Model + NesC • TinyOS Architecture • Major Software Subsystems • Networking Services

  9. Sensor Networks: a hot topic • New university courses • New conferences • ACM SenSys, IEEE IPSN, etc. • New industrial research lab projects • Intel, PARC, MSR, HP, Accenture, etc. • Startup companies • Crossbow, Dust, Ember, Sensicast, Moteiv, etc. • Media Buzz • Over 30 news articles since July 2002 covering Intel-Berkeley/UC Berkeley sensor network activities • One of 10 emerging technologies that will change the world – MIT Technology Review

  10. Why Now? • Commoditization of radio hardware • Cellular and cordless phones, wireless communication • Low cost -> many/tiny -> new applications! • Real application for ad-hoc network research from the late 90’s • Coming together of EE + CS communities

  11. uProc: 4Mhz, 8 bit Atmel RISCRadio:40 kbit 900/450/300 MHz or250 kbit 2.5GHz (MicaZ 802.15.4)Memory:4 K RAM / 128 K Program Flash / 512 K Data FlashPower: 2 x AA or coin cell Mica2Dot Mica Mote iMote Telos Mote uProc: 12Mhz, 16 bit ARMRadio: BluetoothMemory:64K SRAM / 512 K Data FlashPower: 2 x AA uProc: 8Mhz, 16 bit TI RISCRadio: 250 kbit 2.5GHz (802.15.4)Memory:2 K RAM / 60 K Program Flash / 512 K Data FlashPower: 2 x AA Motes

  12. History of Motes • Initial research goal wasn’t hardware • Has since become more of a priority with emerging hardware needs, e.g.: • Power consumption • (Ultrasonic) ranging + localization • MIT Cricket, NEST Project • Connectivity with diverse sensors • UCLA sensor board • Even so, now on the 5th generation of devices • Costs down to ~$50/node (Moteiv, Dust) • Greatly improved radio quality • Multitude of interfaces: USB, Ethernet, CF, etc. • Variety of form factors, packages

  13. Motes vs. Traditional Computing • Embedded OS • Lossy, Adhoc Radio Communication • Sensing Hardware • Severe Power Constraints

  14. NesC: a C dialect for embedded programming Components, “wired together” Quick commands and asynch events TinyOS: a set of NesC components hardware components ad-hoc network formation & maintenance time synchronization NesC/TinyOS Think of the pair as a programming environment

  15. From Ganesan, et al. “Complex Behavior at Scale.” UCLA/CSD-TR 02-0013 Radio Communication • Low Bandwidth Shared Radio Channel • ~40kBits on motes • Much less in practice • Encoding, Contention for Media Access (MAC) • Very lossy: 30% base loss rate • Argues against TCP-like end-to-end retransmission • And for link-layer retries • Generally, not well behaved

  16. Types of Sensors • Sensors attach via daughtercard • Weather • Temperature • Light x 2 (high intensity PAR, low intensity, full spectrum) • Air Pressure • Humidity • Vibration • 2 or 3 axis accelerometers • Tracking • Microphone (for ranging and acoustic signatures) • Magnetometer • GPS • RFID Reader

  17. Non-Volatile Storage • EEPROM • 512K off chip, 32K on chip • Writes at disk speeds, reads at RAM speeds • Interface : random access, read/write 256 byte pages • Maximum throughput ~10Kbytes / second • MatchBox Filing System • Provides a Unix-like file I/O interface • Single, flat directory • Only one file being read/written at a time

  18. Power Consumption and Lifetime • Power typically supplied by a small battery • 1000-2000 mAH • 1 mAH = 1 milliamp current for 1 hour • Typically at optimum voltage, current drain rates • Power = Watts (W) = Amps (A) * Volts (V) • Energy = Joules (J) = W * time • Lifetime, power consumption varies by application • Processor: 5mA active, 1 mA idle, 5 uA sleeping • Radio: 5 mA listen, 10 mA xmit/receive, ~20mS / packet • Sensors: 1 uA -> 100’s mA, 1 uS -> 1 S / sample

  19. Energy Usage in A Typical Data Collection Scenario • Each mote collects 1 sample of (light,humidity) data every 10 seconds, forwards it • Each mote can “hear” 10 other motes • Process: • Wake up, collect samples (~ 1 second) • Listen to radio for messages to forward (~1 second) • Forward data

  20. Sensors: Slow, Power Hungry, Noisy

  21. TinyOS: Getting Started • The TinyOS home page: • http://webs.cs.berkeley.edu/tinyos • Start with the tutorials! • The CVS repository • http://sf.net/projects/tinyos • The NesC Project Page • http://sf.net/projects/nescc • Crossbow motes (hardware): • http://www.xbow.com • Intel Imote • www.intel.com/research/exploratory/motes.htm.

  22. Part 2 The Design and Implementation of TinyDB

  23. Part 2 Outline • TinyDB Overview • Data Model and Query Language • TinyDB Java API and Scripting • Demo with TinyDB GUI • TinyDB Internals • Extending TinyDB • TinyDB Status and Roadmap

  24. Sensor Network TinyDB Revisited SELECT MAX(mag) FROM sensors WHERE mag > 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

  25. 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

  26. 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 7

  27. 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?

  28. Query Language (TinySQL) SELECT <aggregates>, <attributes> [FROM {sensors | <buffer>}] [WHERE <predicates>] [GROUP BY <exprs>] [SAMPLE PERIOD <const> | ONCE] [INTO <buffer>] [TRIGGER ACTION <command>]

  29. 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

  30. TinySQL Examples SELECT nodeid, nestNo, light FROM sensors WHERE light > 400 EPOCH DURATION 1s “Find the sensors in bright nests.” Sensors 1

  31. 2 SELECT AVG(sound) FROM sensors EPOCH DURATION 10s • SELECT region, CNT(occupied) AVG(sound) • FROM sensors • GROUP BY region • HAVINGAVG(sound) > 200 • EPOCH DURATION 10s 3 Regions w/ AVG(sound) > 200 TinySQL Examples (cont.) “Count the number occupied nests in each loud region of the island.”

  32. 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

  33. 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

  34. Using the Java API • SensorQueryer • translateQuery() converts TinySQL string into TinyDBQuery object • Static query optimization • TinyDBNetwork • sendQuery() injects query into network • abortQuery() stops a running query • addResultListener() adds a ResultListener that is invoked for every QueryResult received • removeResultListener() • QueryResult • A complete result tuple, or • A partial aggregate result, call mergeQueryResult() to combine partial results • Key difference from JDBC: push vs. pull

  35. Writing Scripts with TinyDB • TinyDB’s text interface • java net.tinyos.tinydb.TinyDBMain –run “select …” • Query results printed out to the console • All motes get reset each time new query is posed • Handy for writing scripts with shell, perl, etc.

  36. Using the GUI Tools • Demo time

  37. SELECT AVG(temp) WHERE light > 400 T:1, AVG: 225 T:2, AVG: 250 Queries Results Aggavg(temp) Name: temp Time to sample: 50 uS Cost to sample: 90 uJ Calibration Table: 3 Units: Deg. F Error: ± 5 Deg F Get f: getTempFunc()… got(‘temp’) get (‘temp’) Tables Samples getTempFunc(…) 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 > 400 Schema TinyOS TinyDB

  38. Q:SELECT … A Q Q R:{…} R:{…} Q B C Q Q Q Q R:{…} D R:{…} Q R:{…} Q Q Q F E Q Tree-based Routing • Tree-based routing • Used in: • Query delivery • Data collection • In-network aggregation • Relationship to indexing?

  39. Current Sensor A Sleeping Transmitting Time Radio On, Processing Sensor B Sensor B Power Consumption and Lifetime • Power typically supplied by a small battery • At full power, device will last 2-3 days -> Critical Constraint • Lifetime, power consumption varies by application • Scales with “duty cycle” : amount of time on • Low data rate (< 1 sample / 30 secs) : > 6 months possible from AA batteries Must Synchronize! Fundamental challenge: distributed coordination with low power!

  40. Time Synchronization • All messages include a 5 byte time stamp indicating system time in ms • Synchronize (e.g. set system time to timestamp) with • Any message from parent • Any new query message (even if not from parent) • Punt on multiple queries • Timestamps written just after preamble is xmitted • All nodes agree that the waking period begins when (system time % epoch dur = 0) • And lasts for WAKING_PERIOD ms • Adjustment of clock happens by changing duration of sleep cycle, not wake cycle.

  41. Extending TinyDB • Why extending TinyDB? • New sensors  attributes • New control/actuation  commands • New data processing logic  aggregates • New events • Analogous to concepts in object-relational databases

  42. Adding Attributes • Types of attributes • Sensor attributes: raw or cooked sensor readings • Introspective attributes: parent, voltage, ram usage, etc. • Constant attributes: constant values that can be statically or dynamically assigned to a mote, e.g., nodeid, location, etc.

  43. Adding Attributes (cont) • Interfaces provided by Attr component • StdControl: init, start, stop • AttrRegister • command registerAttr(name, type, len) • event getAttr(name, resultBuf, errorPtr) • event setAttr(name, val) • command getAttrDone(name, resultBuf, error) • AttrUse • command startAttr(attr) • event startAttrDone(attr) • command getAttrValue(name, resultBuf, errorPtr) • event getAttrDone(name, resultBuf, error) • command setAttrValue(name, val)

  44. Adding Attributes (cont) • Steps to adding attributes to TinyDB • Create attribute nesC components • Wire new attribute components to TinyDBAttr configuration • Reprogram TinyDB motes • Add new attribute entries to catalog.xml • Constant attributes can be added on the fly through TinyDB GUI

  45. Adding Aggregates • Step 1: wire new nesC components

  46. Adding Aggregates (cont) • Step 2: add entry to catalog.xml <aggregate> <name>AVG</name> <id>5</id> <temporal>false</temporal> <readerClass>net.tinyos.tinydb.AverageClass</readerClass> </aggregate> • Step 3 (optional): implement reader class in Java • a reader class interprets and finalizes aggregate state received from the mote network, returns final result as a string for display.

  47. TinyDB Status • Latest released with TinyOS 1.1 (9/03) • Install the task-tinydb package in TinyOS 1.1 distribution • First release in TinyOS 1.0 (9/02) • Widely used by research groups as well as industry pilot projects • Successful deployments in Intel Berkeley Lab and redwood trees at UC Botanical Garden • Largest deployment: ~80 weather station nodes • Network longevity: 4-5 months

  48. The Redwood Tree Deployment • Redwood Grove in UC Botanical Garden, Berkeley • Collect dense sensor readings to monitor climatic variations across • altitudes, • angles, • time, • forest locations, etc. • Versus sporadic monitoring points with 30lb loggers! • Current focus: study how dense sensor data affect predictions of conventional tree-growth models

  49. Data from Redwoods 36m 33m: 111 32m: 110 30m: 109,108,107 20m: 106,105,104 10m: 103, 102, 101

  50. TASK

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