vision for sensor networks cse 291 chien spring 2003 april 8, 2003

Vision for Sensor Networks

CSE 291 Chien

Spring 2003

April 8, 2003

CSE 291 Sensor Networks – 4/8/2003

Course Logistics

• Friday meeting time» 1230-150, SSB 106 (only irregularly, including this Friday)

• Web site emerging» Information being added regularly» Initial paper presentation signups and paper summaries» Lectures available» Projects (still coming)


Paper Summaries

• Evaluations of the papers are due after we cover them in class. Each should be less than a page long (don't write a novel) and should follow this rough format:

» Title and Authors of paper » One sentence summary of the paper (the problem(s) it addresses and how) » Summary of key or important ideas in the paper (why are we reading this

anyway?) » How does the paper substantiate the importance of those ideas (brief

summary of logical argument and evidence) » One or two important flaws or limitations in the paper (be explicit) » How relevant is this work today and/or what future research does it

suggest?

• The evaluations should be turned in to Prof Chien’s office at the end of each week, on Friday before 5pm. If the door is closed, these can be put under the door.


Today’s Papers

• D. Estrin, D. Culler, K. Pister, and G. Sukhatme, Connecting the Physical World with Pervasive Networks , IEEE Pervasive Computing, pp. 59-69, January-March 2002.

• Which largely subsumes» D. Estrin, R. Govindan, J. Heidemann and S. Kumar,

Next Century Challenges: Scalable Coordination in Sensor Networks, International Conference on Mobile Computing and Networks (MobiCOM '99), August 1999, Seattle, Washington.

• J. M. Kahn, R. H. Katz, and K. S. J. Pister, Next Century Challenges: Mobile Networking for "Smart Dust" , In International Conference on Mobile Computing and Networks (MobiCOM '99), August 1999, Seattle, Washington.


First…

• D. Estrin, D. Culler, K. Pister, and G. Sukhatme, Connecting the Physical World with Pervasive Networks , IEEE Pervasive Computing, pp. 59-69, January-March 2002.


Vision of Ubiquity

• Capability to go anywhere and be anyplace• Out of the machine room, backpack…• Away from the power supplies… (or near)• Away from the infrastructure… (or near)• … can’t require a crushing effort…


Vision of Invisibility

• Not seen…» Systems that are small…» Systems that are unobtrusive…» Systems that are intuitive to use… interfaces that don’t

require training

• Invisibility is coupled with pervasive for utility


Deep Integration with the Physical World

• Today’s computers are “blind, deaf, dumb”» Limited sensory input» Limited interaction» Either disconnected, offline or primarily input processing, etc.

• Embedded systems are the exception to this, but historically have limited networking

• Sensor networks are the superexception to this» Focused on the physical world» Data acquisition, computation, and action» Closely coupled sensing and actuation» The real “autonomic computing systems” ??


Major System Challenges

• Large numbers of elements• Limited physical access• Extreme environmental conditions

• => demand a fundamental reexamination of familiar layers of abstraction, hardware, algorithms

• Suggestion: these are a radically different kind of computing system


Scale

• # of sensors: 10’s to millions• Size of sensors (cubic feet to millimeters)• Spatial and temporal sampling rates (miles to mm’s

and days to ms)• Capability (sensors, power, compute, communicate)• Compose and evolve as a system at scale

• Question: what computing systems do we have that span these ranges? Are they one kind?


Limited Access

• Unwired, unpowered, limited networking (cost)• Physically remote or harsh enviroments• Limited human intervention support/administration• Resource limited

• What are characteristics of our longest running, reliable systems, and techniques?


Extreme Dynamics

• Activity in the physical world happens in bursts» Animals are built for this (adrenaline, fear-flight, hunt, taste,

smell, etc.)» Static network -> things passing, evolving» Mobile network -> things encountered» Dynamic range of sensory input is orders of magnitude

• Systems must have passive vigilance, efficient triggering, and rapid transformation to high levels of concurrency and effectiveness.

• Not quite “lazy computing”, or something analogous?


Breadth

• Variable design structure – static, dynamic, regular and irregular

• Single sensor type/mode, multiple, single application and multiple-application

• Static or mobile – fast and slow change• Autonomy and limited access

» Degree of human involvement in both decision-making/control as well as maintenance of system


State of the Art

• … rambling discussion of some current research activities…» Small devices increasing in environmental awareness and

networking capability» Evolving radio, silicon technology enabling sensor networks» Maturing software environments


State of the Art (cont.)

• Outline of several specific challenges (not comprehensive)» Sensing and actuation – control loops and variable delays» Localization as a key challenge and foundation for coupling

with the physical world» Self configuration – and reconfiguration


Some “throw ins”

• Data centric architecture and “directed diffusion” (we’ll discuss later)

• Tiered (hierarchical) architectures» Different capabilities, heterogeneity

• Frontier for almost any CS subdiscipline is in this area


Discussion

• What kind of systems are really being discussed?• How do they differ fundamentally from those more

familiar?• How do they differ qualitatively/parametrically?• Are the systems being discussed a single class?

Multiple classes?• What areas are likely to be extensions of current

areas? Which are likely to be revolutionarily new?• Is the frontier for your CS subdiscipline in this area?


Second…

• J. M. Kahn, R. H. Katz, and K. S. J. Pister, Next Century Challenges: Mobile Networking for "Smart Dust" , In International Conference on Mobile Computing and Networks (MobiCOM '99), August 1999, Seattle, Washington.


Mobile Networking for “Smart Dust”

• Smart dust can be small enough to» Remain suspended in air» Buoyed by air currents» Sensing and communicating for hours and days on end

• “Exploring the limits of size and power consumption in autonomous sensor nodes”

• Interestingly, macro-scale systems have achieved this capability today…


Smart Dust

• “Mote”, as in dust Motes• Integrated

» MEMS sensors» Signal processing and

control circuitry» Power source and solar

cells» Laser diode and MEMS

mirror for active optical» Retroreflector and optical

receiver for passive

• 1-2 mm in dimension, autonomous system


Some Technology Limits

• Objective: 1mm3 sensors

• How much energy can we consume?

• Batteries -> ~1 Joule / 1mm3

• For a lifetime of 1 day» -> 10 microwatts average (NOT milliwatts!)» Augmenting with solar power limited to 2x» Artificial lighting 1.001x» Energy scavenging all well below solar


What does this mean?

• There are fundamental limits to what can be consumed (resources) by these systems in particular deployment/connection modes» Energy consumed per unit time (average)» Energy consumed in a burst, and cluster of bursts» Data communicated (probably)» Operations computed (?)

• Do these limits naturally partition sensor network systems? Is this a continuum or a discontinuity?


Low Power Techniques

• Clearly at a premium in these systems» E.g. 2x efficiency => 2x more STUFF can get done or 2x

LIFETIME or 2x reduction in COST» Intelligent control of hardware subsystems» Intelligent control of software and application activities» Intelligent design and implementation of systems for power

efficiency

• In particular, power-efficient techniques for communication which due to radios is dramatically more expensive than computation


Free space Networking

• Base station architecture, based on cheap video and imaging circuits• Many sensors transmit simultaneously• Power efficiency per bit is dramatically higher

» Active (LEDs), passive cornercube retroreflector (MEMS) for interrogation, demonstrated kb/s

• Reading an electronic panel


Specific Functions

• Parallel Read out » Synchronized sample of the space and smart dust

• Demand Access» Passive monitoring, triggered activation and sensing

• Controlled probing rates

• => the benefits of centralized control


Limitations

• Line of sight» Direct optical communication to BTS ideal» Multihop possible, but limited» Increases bandwidth densities, but decreases connectivity

• Link Directionality» Can focus interrogation subset of “viewable” sensors» Limits mote visibility and connectivity to a hemisphere» Interesting connectivity, routing, and interlaced network

challenges


Discussion

• How is this vision different/similar to that of Culler/Estrin?

• What are the advantages of this type of networking approach? Disadvantages?

• Power, line of sight, orientation, how do applications differ in their needs for these things?

• How does a technological underpinning like this affect the “architecture” of a sensor network system?


Next Time

• Friday, April 11 in SSB 106• Detailed overview of current/emerging sensor

network hardware» Ember Networks Platform (Ryan Wu) » Crossbow Platform (Berkeley Motes) (Johanz Ammerlahn)

• We will update the web links to specific documents, but not until late today

• Don’t forget your first set of paper summaries are due Friday at 5pm.

vision for sensor networks cse 291 chien spring 2003 april 8, 2003

Documents

pervasive networks

mobile computing

ieee pervasive computing

authors of paper

networks mobicom

physical worldtodays

physical worlddata acquisition

international conference