EECS 598
Wireless Sensor Networks
Technologies, Systems, and Applications
Lecture 2: Computer Science Issues
Prabal Dutta
University of Michigan
January 12, 2010
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Course Updates
• Twitter feed for late-breaking updates:
– http://twitter.com/eecs598w10
• Writeups
– Content looks good so far
– If you decide to take a “pass,” send an e-mail saying so
– Please send as e-mail plain text (no attachments)
• Today’s office hours immediately after class
• No class on Thursday, but writeups are still due!
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Outline
What makes good application-led research?
Picking research problems
Computer Science issues in Ubiquitous Computing
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Perspectives
• “Applications are of course the whole point of
ubiquitous computing”
– Mark Weiser [Wei93]
• “We need to increase the applications deployed
to books written ratio in sensor networks”
– Deborah Estrin [Personal Communications]
• “In the future, increasing proportion of
computer science research will be applicationdriven”
– Eric Brewer and Mike Franklin [CS262A]
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Defining Application-Led Research
• Application-Led Research
– Driven by domain problem
– Evaluated by quantifying benefits brought to domain
• Technology-Led Research
– Not necessarily motivated by potential domain benefits
– Interesting or challenging from a technical perspective
• Research Goals Should (do you agree?)
– Identify users’ problems and application requirements
– Provide infrastructure developers with application
requirements
– Validate technology and provides insights into its use
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Selecting Applications
• Will this change the way people think?
– If nothing changes after your research, what’s the point?
• Must make an impact on computer science
– Just impacting biology or civil engineering is not enough
– Starting from scratch can make this more difficult or easier
• If system building, what will you learn from it?
– There must be an important question in there!
• Identify and attack “severe and persistent problems”
• Avoid trivial “proof-of-concept” research projects
– Team up with domain experts when selecting problems
– Make sure there’s a concept and it’s worth proving
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Implementing Applications
• To start from scratch or not?
– Benefits?
– Drawbacks?
• Is building reusable infrastructure worth it?
– Research community values novelty over good engineering
– Research community doesn’t value implementation as research
– Do you agree?
• Reframe the question: What are your options? (Aside)
– Your efforts can be directed structurally or strategically
• Structural: change the community so that it values infrastructure
• Strategic: pick the right topic, and your work will be broadly used
(and well referenced)
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Evaluating Applications
• Small, lab-scale evaluations
– Useful: in the early stages of design
– Insufficient: impossible to understand the impact of
• Environment on technology
• Technology on environment
• Often hard to teach these apart – hence “systems” research
• Applications are evaluated only against themselves
– Self-evaluation is insufficient
– Requires applications, infrastructure, and data to be
shared
• Is this a good idea?
• Is it done in other fields?
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Recommendations
• Choose applications carefully
– Address severe persistent problems; avoid trivial ones
• Share technical infrastructure
– Design reusable SW/HW; publicly release code
• Evaluate applications in realistic environments
– Only way to investigate interactions between
tech/env/users
– “The real world is it’s own best model” – Rodney Brooks
• Perform comparative evaluations
– Release data sets from field trials; allows other to analyze
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Outline
What makes good application-led research?
Picking research problems
Computer Science issues in Ubiquitous Computing
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Allen Newell’s Research Style
• Good science responds to real problems
• Good science is in the details
• Good science makes a difference
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Good science responds to real problems
• Don’t pick fantasy problems
• Don’t pick trivial “proof-of-concept” problems
• Too many real pressing real-world problems!
• Pick “severe and pressing” problems
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Good science is in the details
• Takes the form of a working model
– The artifact is about understanding, not building
– Must build when analysis is too complex
– Brooks’ quote: “The real world is its own best model”
• Includes detailed analysis or implemented models
– Allows others to benefit from work at an abstract level
– Enables comparisons between difference approaches
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Good science makes a difference
• Measures of contribution:
– How it solves a real problem
– How it shapes the work of other
• Solves a real problem
– The problem sets the crucial context for the work
– A million ideas to pursue, but which ones are worth doing?
• Shapes the work of others
– Highest goal: change other people’s thinking
– Paradigm changes are the most impactful [Kuhn]
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Outline
What makes good application-led research?
Picking research problems
Computer Science issues in Ubiquitous Computing
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Mark Weiser’s Vision
• Who is Mark Weiser?
– Michigan alumnus: MA(‘77), PhD (’79)
– Father of ubiquitous computing
– Work is incredibly influential
• What are the principles of ubiquitous computing?
– The purpose of a computer is to help you do something else.
– The best computer is a quiet, invisible servant.
– The more you can do by intuition the smarter you are; the
computer should extend your unconscious.
– Technology should create calm.
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Are We There Yet?
• Hundreds of Tabs?
• Tens of Pads?
• One or two Boards?
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Did Their Work Have Impact?
• Yes! Due to emphasis on computer science issues:
“The fruitfulness of ubiquitous computing for new computer
science problems justified our belief in the…framework”
• Issues like
– Hardware components
• Low power (P=C*V^2*f gives lots of degrees of freedom)
• Wireless (custom radios (SS/FSK/EM-NF bits/sec/meter^3 metric)
• Pens (how do you write on walls?)
– Network Protocols
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•
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Wireless media access (MACA: RTS/CTS)
Gigabit networks (lot’s of little devices create a lot of traffic)
Real-time protocols (IP telephony)
Mobile communications
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Next Time
• Today’s office hours immediately after class
• Readings for Thursday
– [ECPS02] “Connecting the Physical World with Pervasive
Networks”
– [AABB07] “Mobiscopes for Human Spaces”
• No class on Thursday, but summaries still due
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Connecting the Physical World with Pervasive Networks
Deborah Estrin, David Culler, Kris Pister, Gaurav Sukhatme
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Goals
• Goal: to measure the physical world
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Across large spaces
Over long periods of time
Using multiple sensing modalities
In remote, and largely inaccessible locations
“The physical world is a partially observable,
dynamic system, and the sensors and actuators are
physical devices with inherent accuracy and
precision limits.”
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Challenges
• Immense scale of distributed systems elements
– Vast numbers of devices
– Fidelity
• Limited physical access
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Embedded in the environment
Remote, expensive, or difficult to access
Wireless communications
Energy harvesting or very moderated energy consumption
• Extreme dynamics
– Temperature, humidity, pressure, grass height, …
– Passive vigilance to a flurry of activity in seconds
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Challenge: Immense Scale
NEST FE: 557 Trio Nodes, Self-powered, selfmaintaining, GPS ground truth, multiple subsets
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Challenge: Limited Physical Access
Redwoods
Top endcap
O-rings
Cylindrical enclosure
Protective skirt
Bottom endcap
Top sensing surface:
incident PAR and TSR
Battery
Mica2Dot
Bottom sensing surface:
temperature, humidity,
barometric pressure,
reflected PAR & TSR
to appear Sensys 05
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Challenge: Extreme Dynamics
ExScal
• Border Control
– Detect border crossing
– Classify target types and counts
• Convoy Protection
– Detect roadside movement
– Classify behavior as anomalous
– Track dismount movements off-road
• Pipeline Protection
– Detect trespassing
– Classify target types and counts
– Track movement in restricted area
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