CMSC828K: Sensor Data
Management; Data Streams
Amol Deshpande
Today..
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Introductions
Overview of the syllabus
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What ? Why ?
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Grading/Class requirements etc...
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No laptops in the class
Why ?
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Emergence of sensing devices that can be
networked together on a large scale
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New data management challenges
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Very high-rate “data streams”
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Uncertain/imprecise data
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New types of queries
We need to develop techniques to handle
such data
A Sensor
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A device that can “sense” things
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sense = measure = instrument
Examples:
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Traffic Sensors
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Location Sensors
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e.g. traffic cameras
e.g. cell phones, GPS units
Sensors sensing environmental properties
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e.g. temperature, humidity, light etc
Sensor Network
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A collection of sensing devices that can communicate with
each other
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Can collectively measure or instrument a large scale
phenomenon or property
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Increasing number of deployments everywhere
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Fueled mainly by developments in MEMS
MICA2 Mote (Berkeley mote)
Types of Sensors
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Temperature
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Variable resistors that change resistance with temp
Types of Sensors
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Photocells, fog detectors
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MEMS – Micro-electronic Mechanical Systems
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• Sensors attach via daughtercard
Acceloremeters, gyroscopes, tilt sensors
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Soil moisture (electrical resistance)
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GPS
•Weather
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Acoustic
(microphones)
–Temperature
–2 or 3 axis
Cameras
accelerometers
–Light ?x 2 (high
intensity PAR, low
•Tracking
intensity, full
–Microphone (for ranging
spectrum)
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•Vibration
Types of Sensors
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Wireless camera sensors ?
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Several projects underway
CMOS based imaging sensors
Very low power, but very low resolution
Cyclops (@UCLA)
CMUCam
WSNs: Vital Signs Monitoring
Project Codeblue: Harvard
Accelerometer, gyroscope, and
electromyogram (EMG) sensor
for stroke patient monitoring.
Pluto mote with a 3-axis accelerometer
Can monitor the vital signs (for months on 2 batteries), and transmit it wirelessly.
Can be used to detect anomalous behavior, to raise alarms etc…
RFIDs (“Smart Labels”)
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Identify objects from distance
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Wireless energy supply
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~100 bytes
Cost ~ $0.1 ... $1
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~1m, magnetic field (induction)
ROM or EEPROM (writeable)
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small IC with RF-transponder
Consumable and disposable
Flexible tags
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laminated with paper
RFIDs (“Smart Labels”)
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RFID: Increasingly number of deployments
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Supply chain management
Tracking Luggage, medical equipment, clothes ...
Activity recognition
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RFID Tags + iGlove
“Wired” Sensor Networks/Macro-scopes
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Traffic/GPS sensors, video cameras, microphones…
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Somewhat different challenges
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Disaster management, Surveillance, Tracking, Activity detection
Especially for audio/video sensors
But a lot of commonalities...
Thanks to Gutemberg Bezerra
Applications..
Habitat monitoring
Elder care
Seismic structure monitoring
Home automation
Contamination tracking
Measuring pollutants
Location-based services
Traffic monitoring
Supply-chain management
(RFID)
Precision Agriculture/Nursery
Object tracking
Smart environments
Surveillance
Industrial Monitoring
Interactive Museums
Battlefield applications
Swimming Pool Monitoring
etc... etc...
Challenges
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Hardware platforms
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Started with the vision of “smart dust”
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Power consumption still an issue
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Battery power doesn’t obey Moore’s law
Reliability
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Deployments in extreme conditions
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Need to autonomously deal with failures
Challenges
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Programming interfaces/abstractions
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Still too much variety in the platforms...
Networking for wireless sensors
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Reliable multi-hop is tricky
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Inherently lossy channel
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Routing protocols, connectivity
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Must deal with mobility, failures
Localization, synchronization
Challenges
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Security/Privacy
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A very important issue...
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Monitoring has been happening, and will continue,
no matter what
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WSNs just make it significantly easier
Who controls the data ? Who sees it ?
Challenges
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Finally...
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the focus of the class...
Data management challenges
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Data generated in real-time and continuously
(distributed data streams)
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Traditional database systems have significantly
more static data
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Tremendous amounts of data generated
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Much of it useless, but need to process all
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Must be processed immediately
Data management challenges
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Typically acquisitional environments
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Data is not gathered/sensed until asked for
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Should carefully decide what to “acquire”
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Required to conserve power as well as for sanity
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Changes query processing quite fundamentally
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“Acquisitional” QP
Data management challenges
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Raw data is inherently uncertain
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Lossy: sensor, communication link failures
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Imprecise: errors in the sensing
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Uncertain/probabilistic: fundamental limitations in
the way sensing is done
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Use of statistical models fundamental
“Probabilistic Databases” ?
Data management challenges
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Need for real-time statistical modeling
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Event/anomaly/pattern detection
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Removing noise from the data
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Spatial/temporal biases in the data
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…
Data management challenges
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Data provenance
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Being able to trace something back to its origins
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Data exploration and visualization
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Managing large-scale spatio-temporal datasets
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Data interoperability
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Data security and privacy
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…
Data management challenges
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Combination of all these factors has made
this a very challenging and exciting research
area....
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Multi-disciplinary solutions required
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Databases + Machine Learning + Networking
Class
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Class based on reading papers...
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Sorta-classic papers exist for two topics
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Wireless sensor networks
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Data streams
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Not so much about probabilistic modeling, uncertain data
etc…
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Schedule on the web....
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Class Forum
Outline
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Overview of sensing technologies, hardware trends,
applications: 2-3 classes
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Declarative query processing in sensornets: 2-3 classes
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Data streams: system design, query processing and
optimization, adaptive query processing: 7-8 classes
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Probabilistic graphical models and their role in sensor data
management: 4-5 classes
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Uncertain, probabilistic databases: 5-6 classes
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Tentative schedule
Class Structure
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Before each class, email reading summaries to me
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Include “828 Summary” in the subject.
Each class, presentation followed by discussion about the
papers
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Most by me
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Some by you
If you plan to attend regularly but are not enrolled, you
should consider doing a presentation as well
Grading
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Summaries: 10
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Participation + Presentation: 10
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Project: 40
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Literature survey
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Intermediate progress report
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Final report + presentation (or maybe poster)
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Homework/Exam: 40
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Remember, this is a graduate class
Thats all...
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Questions ?
Wireless Sensing Devices
LWIM III
AWAIRS I
UCLA, 1996
UCLA/RSC 1998
Geophone, RFM
Geophone, DS/SS
radio, PIC, star
Radio, strongARM,
network
Multi-hop networks
Sensor Mote
Medusa, MK-2
UCB, 2000
UCLA NESL
RFM radio,
2002
Atmel
Predecessors in
DARPA Packet Radio program
USC-ISI Distributed Sensor Network Project (DSN)
Examples of Wireless SNs
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Ecosystems, Biocomplexity
Marine Microorganisms
•
Micro-sensors, onboard processing,
wireless interfaces
feasible at very small
scale--can monitor
phenomena Ņup closeÓ
Contaminant Transport
Enables spatially and
temporally dense
environmental
monitoring
Seismic Structure Response
Embedded Networked
Sensing will reveal
previously
unobservable
phenomena