1 EECS 598 Wireless Sensor Networks Technologies, Systems, and Applications Lecture 2: Computer Science Issues Prabal Dutta University of Michigan January 12, 2010

2 Course Updates Twitter feed for late-breaking updates: – Writeups –Content looks good so far –If you decide to take a “pass,” send an saying so –Please send as 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 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