Transcript Motivating Sensor Network Research: The Applications and Computer Science Issues Prabal Dutta and David Chu September 13, 2005

Motivating Sensor Network Research:
The Applications and Computer Science Issues
Prabal Dutta and David Chu
September 13, 2005
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What Makes Good Application-Led Research?
Richard Sharp and Kasim Rehman
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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-Fa04]
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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
• NEST FE Provides some good examples
• 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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Allen Newell’s Research Style
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Allen Newell’s Research Style
• Good science responds to real problems
– Don’t pick fantasy problems; there are too many real ones
• Good science is in the details
– Takes the form of a working model
– Includes detailed analysis or implemented models
• Good science makes a difference
– Measure of contribution is in
• How it solves real problems
• Shapes the work of others
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Some Computer Science Issues in Ubiquitous Computing
Mark Weiser
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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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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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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 sensing surface:
incident PAR and TSR
Top endcap
O-rings
Battery
Cylindrical enclosure
Mica2Dot
Bottom sensing surface:
temperature, humidity,
barometric pressure,
reflected PAR & TSR
Protective skirt
Bottom endcap
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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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Discussion
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