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Sensor Network Querying

Sensor Network Querying. Dina Q Goldin University of Connecticut, USA March 17, 2003. The Invisible Computer. The most user-friendly computer is one we don’t see Advocated in mid-1990’s by Michael Dertouzos, director of MIT's Laboratory for Computer Science for 25 years.

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Sensor Network Querying

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  1. Sensor Network Querying Dina Q Goldin University of Connecticut, USA March 17, 2003

  2. The Invisible Computer • The most user-friendly computer is one we don’t see • Advocated in mid-1990’s by Michael Dertouzos, director of MIT's Laboratory for Computer Science for 25 years. • Does that make sense?

  3. Outline • Computing in the 21st century • Sensors and sensor networks • Sensor network querying

  4. The Disappearing Computer • More and more processors are not on desktops • Processors in cars, in cellular telephones, in toys • Even the computer itself can “dissolve” into an entertainment system - digital TV screen and speakers - CPU on shelf - wireless keyboard on lap

  5. Home Computer of Tomorrow • Flat wall screens for TV/computer in many rooms • Connected to an out-of-sight CPU by LAN • Multiple speakers embedded in/around screen for 3D sound effects • Screen can act as an (open) window when not in use • Natural input interface -- voice/pointing (no keyboard needed)

  6. House as a Web Site • Processors in various appliances • All networked (locally, and to wireless hub) • Appliances can communicate with outside world - Security system calls you or police - “Smart recycling bin” orders more food • Can log onto your house site to control them - Turn heat up - Turn coffeemaker on (already a reality)

  7. Cars of Tomorrow • GPS to know position • Wireless connection to obtain traffic conditions • Sensors: - distance to cars / people / obstacles - indoor/outdoor temperatures - road traction • Screen to show sensor readings / maps • Radio used for warnings / directions • Automatic controls based on sensor readings

  8. Sensors for/in the Body • Digital jewelry: • DCPU in watch, speaker in an earring, camera in glasses • Scenarios: • (salesmen) Identifies person approaching, whispers their name, position to you • (repair trainee) Identifies machine parts, projects visual instructions on glasses • Assumes powerful vision/voice recognition • Embedded microsensors • Track vital signs, blood levels • For at-risk people: sick, old, mountain climbers

  9. Ambient Intelligence Intelligent environments of all kinds: • Highways - Where are the traffic jams? • Airports • Who is entering/leaving high-risk areas? • Large high-rise office complexes - Are there problems with heat/AC anywhere? • Oceans • Is a Tzunami on its way? • People

  10. Pervasive Computing • Computation in service of our needs: • Personal: Entertainment, daily activities, travel, house monitoring • Companies: Work efficiency, building monitoring • Scientific/medical: remote training / diagnosis, monitoring oceans • Governments: security, automatic gathering of statistics

  11. Pervasive Computing • Computing made easy - Interaction through natural modalities - Interaction during natural activities • Computing made invisible - Hidden in objects of everyday use - Distributed - Embedded in environments The computing paradigm for 21st century

  12. Sensors • Essential part of pervasive computing • Computation • A small embedded computer with limited processing power and memory • Communication: • LAN, Wireless, Infrared / sound • Sensing • Temperature, pressure, magnetic field, noise levels, chemicals, etc.

  13. Sensor Constraints • A race to decrease: • Size • Price • Energy consumption • A race to increase: • Sensoring / transmitting abilities • Computation power • Applications constrained by this tradeoff

  14. Sensor Networks • Many sensors distributed in a region • Performing a common task • Local communication (between neighbors) • Frequent failures • Fault-tolerant distributed computing

  15. Monitoring Tasks • “Killer application” for sensor networks • Highways - Where are the traffic jams? • Airports • Who is entering/leaving high-risk areas? • Large high-rise office complexes - Are there problems with heat/AC anywhere? • Networks custom-engineered for each task

  16. Sensor Network Wish List • Robust performance • Failed sensors do not bring down the network • Ad-hoc routing • New sensors join the network on their own • Concerns also shared by mobile computing networks • Cell phones / PDAs / laptops / GPS devices • Established research area

  17. Monitoring Task Wish List • Ad-hoc computing • New sensor join the task on their own • Ad-hoc querying • Monitoring tasks can be initiated by user • Impossible while each task is custom-engineered • New approach is needed

  18. Sensor Network Querying • A single general-purpose platform to enable sensor network users to perform all the monitoring activities mentioned above • A single (extensible) query language • A single (extensible) OS/DB engine • No more custom engineering • New & exciting research area

  19. Axioms of SN Querying • User sees network as a single intelligent information system • Sensors as sources of data • Monitoring tasks as data processing • Ad-hoc querying of sensor networks • Each task specified by user, not custom-engineered • Multiple tasks can be present at once • Separation of engineering concerns • physical level (routing, communication) • logical level (data processing) – our focus

  20. Sensors As Data • Sensors form a database relation • Sensors(NodeID, locn, temp, pressure, ….) • Syntax as for regular relations • Employees(EmpID, birthdate, salary, …) • Data semantics is dynamic • Temperature and pressure are streams of continuously changing values

  21. Monitoring Tasks as Queries • User asks queries in a query language • Return average temperature of each room in building • Syntax similar to regular database query languages • Such as SQL • Query semantics is continuous • Query “lives” in the network • Continuously reevaluated as sensor data dynamically changes

  22. Examples Find the average temperature in all the rooms that are dark SELECT roomNumber, AVG(temp) FROM sensors WHERE light = OFF GROUPBY roomNumber EPOCH DURATION 30 s

  23. Traditional DBMS vs. Sensor Network Querying dynamic data queries output output staticdata continuous query

  24. Distributed DB Engine… • Each sensor has an OS • for managing routing, communication, etc • for controlling sensors • such as TinyOS (UC Berkeley) • Each sensor has a DB processor • remembers all queries “alive” in the network • evaluates each of them continuously • such as TinyDB (UC Berkeley) • New sensors join the network seamlessly

  25. Coupled to Central Processor • Entry point into sensor network • User interacts with network via a CP • Additional (static) data stored at CP • Sensors are routed in a single tree whose root is connected to CP • Some data processing is centralized (at the CP), other localized (at the sensors)

  26. Query Optimization • Traditionally: - minimize computation time / disk accesses • In sensor networks: - minimize power consumption • Sensor power consumption • Computation • Sensing (various modalities) • Communication (receiving, transmitting)

  27. Events • Will play important role in SN querying • As part of query specification ON EVENT door-open(loc) [QUERY DESCRIPTION] • As optimization technique [monitor for sounds every 30 sec] BETWEEN EVENTS door-open, door-closed [monitor for sounds every 1 sec]

  28. Aggregation • Impossible to continuously collect raw sensor data (information overload) • Aggregation – family of operators to summarize data • Min, max, average • In-network aggregation for optimal query evaluation

  29. In-network Aggregation • Aggregate computed gradually • as values routed back to CP • Additional information carried along • to allow “partial” aggregation • Example: computing average • Carry <cnt, avg> • cnt0 = cnt1 + cnt2 • avg0 = (avg1 * cnt1 + avg2 * cnt2) / cnt0 • Same framework for all aggregate operations • Initializer at routing tree leaves • Evaluator for combining information

  30. Spatial Data • Spatial Databases store spatial data • Locations (of fire stations) • Regions (towns, lakes) • Lines (roads, rivers) • Spatial data will play larger role in SN querying • Dynamic spatial data • Contour maps • Tracking paths • Sensor locations (for mobile sensors) • Challenge: querying over dynamic spatial data

  31. Example Queries over Dynamic Spatial Data • When there is an unusually loud sound, return the path that is followed by the source of this sound • Identify when we have a growing area of decreased pressure that exceeds some specified tolerances • Track the area where the average daily temperature has been exceeding its expected value by some specified tolerance for a specified period of time.

  32. Georouting • For reducing communication during broadcasts of spatial data • Maintain bounding box at each sensor, over locations of sensors in its routing subtree • Use it to filter out spatial data that falls outside the bounding box • Results in very significant savings

  33. The Future: Active Sensor Networks • Sensors become mobile robots • Multiple communication modalities • Sound, wireless, infrared, smell • Can act upon their environments • Move things, turn switches, deposit color or scent • Interacting with our environment • Rather than just monitoring

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