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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

Sensor Network Querying

Dina Q Goldin

University of Connecticut, USA

March 17, 2003

the invisible computer
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?
  • Computing in the 21st century
  • Sensors and sensor networks
  • Sensor network querying
the disappearing computer
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

home computer of tomorrow
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)
house as a web site
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)

cars of tomorrow
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
sensors for in the body
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
ambient intelligence
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
pervasive computing
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
pervasive computing1
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

  • 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.
sensor constraints
Sensor Constraints
  • A race to decrease:
    • Size
    • Price
    • Energy consumption
  • A race to increase:
    • Sensoring / transmitting abilities
    • Computation power
  • Applications constrained by this tradeoff
sensor networks
Sensor Networks
  • Many sensors distributed in a region
  • Performing a common task
  • Local communication (between neighbors)
  • Frequent failures
  • Fault-tolerant distributed computing
monitoring tasks
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
sensor network wish list
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
monitoring task wish list
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
sensor network querying1
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
axioms of sn querying
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
sensors as data
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
monitoring tasks as queries
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

Find the average temperature in all the rooms that are dark

SELECT roomNumber, AVG(temp)

FROM sensors

WHERE light = OFF

GROUPBY roomNumber


traditional dbms vs sensor network querying
Traditional DBMS vs. Sensor Network Querying

dynamic data





continuous query

distributed db engine
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
coupled to central processor
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)
query optimization
Query Optimization
  • Traditionally:

- minimize computation time / disk accesses

  • In sensor networks:

- minimize power consumption

  • Sensor power consumption
    • Computation
    • Sensing (various modalities)
    • Communication (receiving, transmitting)
  • Will play important role in SN querying
  • As part of query specification

ON EVENT door-open(loc)


  • As optimization technique

[monitor for sounds every 30 sec]

BETWEEN EVENTS door-open, door-closed

[monitor for sounds every 1 sec]

  • 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
in network aggregation
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
spatial data
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
example queries over dynamic spatial data
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.
  • 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
the future active sensor networks
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