1 eecs 598 wireless sensor networks technologies, systems, and applications lecture 2: computer...
Post on 15-Jan-2016
219 views
TRANSCRIPT
1
EECS 598Wireless Sensor NetworksTechnologies, Systems, and Applications
Lecture 2: Computer Science Issues
Prabal DuttaUniversity of Michigan
January 12, 2010
2
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!
3
Outline
What makes good application-led research?
Picking research problems
Computer Science issues in Ubiquitous Computing
4
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]
5
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
6
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
7
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)
8
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?
9
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
10
Outline
What makes good application-led research?
Picking research problems
Computer Science issues in Ubiquitous Computing
11
Allen Newell’s Research Style
• Good science responds to real problems
• Good science is in the details
• Good science makes a difference
12
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
13
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
14
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]
15
Outline
What makes good application-led research?
Picking research problems
Computer Science issues in Ubiquitous Computing
16
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.
17
Are We There Yet?
• Hundreds of Tabs?
• Tens of Pads?
• One or two Boards?
18
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
19
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
20
Connecting the Physical World with Pervasive Networks
Deborah Estrin, David Culler, Kris Pister, Gaurav Sukhatme
21
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.”
22
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
23
Challenge: Immense Scale
NEST FE: 557 Trio Nodes, Self-powered, self-maintaining, GPS ground truth, multiple subsets
24
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
25
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