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Introduction
Jun Yang
CPS 296.1, Spring 2007
Sensor Data ProcessingWith contents from
D. Estrin, D. Ganesan, M. Welsh, and F. Zhao
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Wireless sensors are here!
Low-power, wireless sensor nodes with tiny amount of
CPU/memory
Networked for high-resolution sensing of environment
Untethered micro sensors will go anywhere and measure
anything—traffic flow, water level, number of people walking by,
temperature. This is developing into something like a nervous
system for the earth.
— Horst Stormer in Business Week, 8/23-30, 1999
Moteiv Tmote Sky ’05Berkeley Mote ’00
UCLA/RSC AWAIRS I ’98
UCLA LWIM III ’96
Berkeley Speck ’03
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Wide range of applications
Environmental sensingTraffic, habitat, hazards, security,
anti-terrorism
Industrial sensingMachine monitoring and diagnostics
Power/telecomm. grid monitoring
Human-centered computingContext-aware environment
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Habitat monitoring on Great Duck Island
10 miles off the coast of Maine, wireless sensors are being used
to find more about birds in their natural habitat
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Environment monitoring in Duke Forest
Right here in Duke Forest, wireless sensors are being used to
study how environment affects forest growth
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A chemical plume tracking scenarioLarge-scale chemical leak is
detected at a plant3 UAVs are launched 15 miles from the leak site;
each is equipped with 1000 tiny wireless chemical sensor nodesUpon
flying over the vicinity of the leak site, UAVs release sensor
nodesWhile airborne, nodes self-organize into an ad-hoc network and
relay the tracking results back to command center
Where is the plume, how big, how fast, which direction?
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Sample hardware: Moteiv Tmote SkyCPU and storage:
8MHz 16-bit Texas Instruments MSP430
No floating point hardware
10kB RAM, 48kB flash for program
1MB external flash for storage
Radio:2.4 GHz, 250kbps, max range 125m
Sensors: humidity, light, temperature
Power: 2 AA batteries (1850mAh capacity)Active with radio on:
19mA
Sleeping: 5.1µA
User interface: push buttons & LEDs!
USB connector: easy programming and data collection
Implications?
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Power breakdown
Implications?
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Advantage of multi-hoppingRadio-frequency (RF) signal power
attenuation near ground: Preceive ∝ Psend / rα, where α is
typically 2-5Compare between
Covering a distance of N × r with one hopCovering the same
distance with N hops
Psend(Nr) : N × Psend(r)= (Nr)α Preceive : N × rα Preceive= Nα –
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So the more the merrier?Analysis ignores power usage by other
components of RF circuitryand cost of additional hardware
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Other advantages of a dense network
A denser sensor field improves the odds of target detection
within max sensing range
Within the range, further increasing the density by k improves
the signal-to-noise ratio (SNR) by 10 log k db (in the 2-d acoustic
sensing case)
Preceive ∝ Psource / r2
SNRr = 10 log (Preceive/Pnoise) = 10 log Psource – 10 log Pnoise
– 20 log r
SNRr/sqrt(k) – SNRr = 10 log k
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One typical sensor network setup
Internet
Users
Gateway or base station,typically more powerful than nodes, and
sometimes tethered
A (sensor) node, usually withmultiple attached sensors
Data service
Physical environment
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But…
[Gupta & Kumar, 2000] If every node has some data to
transmit, the per-node throughput scales as 1/sqrt(N)—can’t get out
more data than that!
I.e., in a large network, everybody spends almost all its
efforts forwarding messages from others!
Fortunately, a dense sensor field generates lots of redundant
data—not all needs to be transmitted!
[Scaglione & Servetto, 2002] Given a physical phenomenon to
monitor with prescribed accuracy, amount of non-redundant data
grows as O(log N)
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ChallengesUntethered operation in unpredictable environment
Energy, energy, energy…Unavoidable failure and uncertainty
How do nodes discover each other and sync time?How do we send
messages among nodes?How do we compress/suppress messages?How do we
balance load?How do we program a sensor network?How do we plan a
deployment?How do we acquire, process, model, and store data?
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Constraints and objectives
Hardware cost?
Total energy expenditure?
Lifetime?
Manpower required for continuous operation?
Survivability?
Data accuracy/completeness?
Utility of information?What can you learn from data?
How soon can you learn it?
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Sample issues in networking
Hidden terminal problemin MAC (Media Access Control)
CSMA (Carrier Sense) is not enough
A: currentlytransmitting
B: wishingto transmit
hole
Routing around holesGreedy forwarding does not always work
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Sample issues with uncertainty
A sensor samples periodically and reports all readings x1, x2,
…, xt, … to the base station
Value-based temporal suppression (a.k.a. delta modulation)
Remember the value x’ at the last transmission time
If |xt – x’| ≥ ε, send xt – x’
What if a message gets lost?
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Sample issues with uncertainty (cont’d)
Model-based suppression exampleUse x*t = xt’ + dt’ (t – t’) to
predict xt, where we expect
• dt’ changes very infrequently
• xt occasionally deviates from x*tNode transmits dt’ when it
changes
Node transmits xt if |xt – x*t| ≥ ε
What if a dt’ message gets lost?
xt
t
×
!!
!
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To sum up
Sensor research is exciting: lots of new apps and challenges
Senor research is active at Duke
CPS 296.1: a stepping stone to sensor researchMore
application/data-centric
Also check out ECE 299.02 offered by Prof. Romit Choudhury if
you are interested in the communication/networking aspect
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Course roadmap (tentative)
Sensor network applications (2 lectures)
Sensor network as a database (3 lectures)
Basics of model-based processing (2 lectures)
Networking and system issues (4 lectures)
Programming sensors (2 lectures)
Coping with data uncertainty (5 lectures)
TBD based on class interest and most recent literature (6
lectures)
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Misc. course information
Recommended (but not required) bookWireless Sensor Networks: An
Information Processing Approach, by Feng Zhao & Leonidas
Guibas
• Also on reserve in Engineering (Vesic) Library
Web sitehttp://www.cs.duke.edu/courses/spring07/cps296.1/
Course information; schedule and reading list; lecture slides;
project information; programming notes
Mailing list: [email protected] hours: Tuesday/Thursday
after class, or by appointment
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Course load
Reading and participation (30%)
Course project (70%)
No problem sets
No exams
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Reading and participation
Breakdown (subject to revision):10%: mini-reviews of papers on
reading list
• Submit via email to instructor
20%: presentation of two papers in class• 30 minutes each
• Need to prepare slides
• Meet me in advance to go over the paper
• I will suggest some papers, but you are welcome to propose
something else
Read with the mind of a researcher!
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Course project
Breakdown10%: proposal presentation in class (before spring
break)
60%: final talk and write-up due at the final exam time
Team work
Ask me for ideas or come up with your own
ResourcesData from our real deployment
10 Moteiv Tmote Sky nodes
Aim high: make it a publishable piece of work
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Example projects
Database/UI for our Duke forest deployment
Simulator/deployment planning tool
Database support for model-based suppression
Managing uncertainty in model-based suppression
Benchmarking/profiling sensor node hardware
Collecting some real data
Exploring application/communication interaction
