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EECS 598Wireless Sensor NetworksTechnologies, Systems, and Applications

Lecture 2: Computer Science Issues

Prabal DuttaUniversity 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 application-driven”– 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• 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– 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– 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, self-maintaining, GPS ground truth, multiple subsets

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Challenge: Limited Physical Access

Top endcap

O-rings

Cylindrical enclosure

Protective skirt

Top sensing surface:incident PAR and TSR

Battery

Mica2Dot

Bottom sensing surface:temperature, humidity,barometric pressure, reflected PAR & TSRBottom endcap

to appear Sensys 05

Redwoods

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Challenge: Extreme Dynamics

• 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

ExScal