Transcript Sensor Network Querying - Computer Science and Engineering

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.
• Does that make sense?
Outline
• Computing in the 21st century
• Sensors and sensor networks
• Sensor network querying
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
- 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
• 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
• 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
• 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
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
• 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 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
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.
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
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Many sensors distributed in a region
Performing a common task
Local communication (between neighbors)
Frequent failures
Fault-tolerant distributed computing
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
• 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
• 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 customengineered
• New approach is needed
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
• 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 customengineered
- 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 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
• 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
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
Traditional DBMS
vs. Sensor Network Querying
dynamic data
queries
output
static
data
output
continuous
query
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
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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
• Traditionally:
- minimize computation time / disk accesses
• In sensor networks:
- minimize power consumption
• Sensor power consumption
– Computation
– Sensing (various modalities)
– Communication (receiving, transmitting)
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]
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
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 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
• 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.
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
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