ELEG 454/654
Sensor and Data Wireless Networks
Spring 2010
Before anything happens
• Add / drop
– Does anyone know of anyone who is dropping?
– A few people can add! (Don’t tell anyone)
• Meeting time
– One day a week?
– Earlier in the day?
Basics
• Instructor: Stephan Bohacek
• TA: none, but Vinay Sridhara will help with
Lab stuff.
• Web page: http://www.eecis.udel/~bohacek
– But nothing is there yet.
• Textbook: none. A reader will be available
soon.
Course Focus
• Introduction to sensor networks and wireless
networks
• Network protocols at various layers.
– MAC, routing, transport. (there is cross-over here with wired
and other wireless)
• Application issues.
– Data fusion, localization
• Analytical methods in networks
– Optimization (routing, and power control)
– Modeling (analysis of MAC)
• Unique issues: energy efficiency, self managing, data
driven, arbitrarily large scale, etc.
• And everything
Prerequisites
• Basic networking
– For example, you know
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•
•
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what the transport layer does
what exponential back-off is
at least one MAC protocol
what flooding is and how it works
• Programming
– For example, you can implement flooding.
Format of class
• Seminar / project oriented
– Lectures
• The first couple of months and some others.
– Reading
• Assigned reading with in class discussion
– Homework
– Projects
• Many small/moderate projects and a large one
• One Android mobile phones
– Presentations
• Grad students must give half-hour talks on a topic.
– The talk should be on a paper related to the lecture the week before.
– The talk should be fairly short and should add to the prior week’s discussion.
• Project discussions
– Group talk about project. The talk covers:
» The problem to be solved
» The related papers (if the project is base on a paper, then the talk
MUST include discussion from both the paper that the project is based
on, and some related papers)
» Key challenges
» Schedule
– Final project presentation
– Final project write-up
Projects
• Project selection and presentation (by 3/30)
– Projects may be related to grad student lecture topic
• Project progress report (4/20)
• Project presentation (5/20 and 5/25)
Grading
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Class discussion/reading (10%)
Small/moderate projects (30%)
Homework (10%)
Presentations (20%)
Final project (30%)
Reading material
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Reader available from the web site and papers listed on web site.
Also:
– Wireless sensor networks. Editors: Ragavendra, Sivalingam, and Znati
– Wireless sensor networks. Feng Zhao and Leonidas Guibas
– Protocols and Architecture for Wireless Sensor Networks. Karl and Willig\
– Wireless Sensor Networks. Bulusu and Jha
– Topology control in wireless ad hoc and sensor networks. Santi
– Handbook of sensor networks: algorithms and architectures. Ed.
Stojmenovic
– Ad Hoc wireless networks. Murthy and Manoj.
– Wireless Sensor Networks. Edgar and Callaway
– Handbook of sensor networks. Editors: Ilyas and Mahgoub
Topics (approximate)
• Overview of networks –
this week
• Propagation of wireless
signals
• Energy
• MAC protocols
• Routing
• Transport
• Applications
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Localization
Tracking
Time synchronization
Data
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Gathering
Processing
Compression
Fusion
• Architecture
Today: Introduction to Sensor Networks
• Sensor networks.
– Definition, motivation, examples
• Challenges.
What are sensor networks?
• Networks of devices that are able to sense the
environment, perform on-board computation, (and
communicate)
• Why? Because we can: Technology
– Circuit integration
• Ability to integrate more functions into chips with lower energy
– Wireless communication
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Better communication theory
Better devices
Bit-rates are slowly increasing
Transmission power is decreasing
– Sensor technology
Result: sensing nodes
PC-104+
UCLA TAG
UCB Mote
Embedded Networked Sensing
• Micro-sensors,
– on-board processing,
– wireless interfaces
– small scale and low cost => large numbers of sensor fields can be
deployed
– monitor phenomena “up close”.
• Enables spatially and temporally dense monitoring
– Nyquist Sampling – you must sample often enough (in time or
space)
– Inverse problems are very difficult, e.g., by sensing the temperature
at a few places, determine the temperature everywhere
(numerically unstable, i.e., errors are amplified). Instead, directly
sense the temperature everywhere.
– Wireless interface allow little infrastructure – easy deployment
– Wireless interface allow cooperation and distributed computing
Instability of an inverse problem
Contaminate sources
Wind direction
Air sensors
Objective: use the sensors to
determine the source of the
contaminate.
Assume that the density at (x,y) is



x, y g
x s, y

0, y
ds
g
x, y
1
2
x2
y
x
2
exp 12 x 


T, :G
0, :
where U is the flow into the environment at the source
Solution: put the sensor near to the source
y
for T=1:.25:10
G=zeros(21,21);
for i=-10:10
G(i+11,:) = normpdf([-10:10]-i,0,T);
end
u0=sin([-10:10]/10*pi)'; %rand(21,1);
u2=G*u0;
u0hat = inv(G)*u2;
u0NoiseEst = inv(G)*(u2+rand(size(u2))*.01);
figure(1)
clf
plot(u0)
hold on
plot(u2,'g')
plot(u0hat,'r')
legend('original','observed','inverse')
axis([0,21,-1,1]);
title(sprintf('Inverse problem at %.2f ',T))
T
pause
end
Vision
Embed numerous
sensing nodes to
monitor and interact
with physical world
Network these devices
so that they can
execute more complex tasks.
Note that these two problems are very different. Different scales and different
requirements. It is difficult to lump them into a single “sensors” group.
Sensor networks applications…
Vision: “Embed the World”
• Buildings self-detect and self-correct from structural faults (e.g.,
weld cracks).
• Schools detect airborne toxins at low concentrations, trace
contaminant transport to source.
• Buoys alert swimmers to dangerous bacterial levels (or an
excellent set of waves coming, brah).
• Earthquake-rubbled building infiltrated with robots and sensors:
locate survivors, evaluate structural damage.
• Ecosystems infused with chemical, physical, acoustic, image
sensors to track global change parameters.
• More??
Buoys along the east coast
Sensor to assist surfers!
Sensors are not always small…
• For example, Umass’s
CASA (Collaborative
Adaptive Sensing of the
Atmosphere). Network of
meteorological radars to
observe, detect, and predict
atmospheric phenomena.
Deployments
• Ecological Habitat Monitoring
– UCB/Intel Berkeley: Great Duck Island
– UCLA-CENS: James Reserve
– Princeton: ZebraNet in Kenya
• Structural Monitoring
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UCLA-CENS: Factor Building
USC: Networked SHM
UCB/Intel Berkeley: SF Golden Gate Bridge
UD
• Biomedical Applications
– Artificial retina
– “Bio-monitors”
• Industrial and Commercial Apps
– Ember Corp: Thermal Process Control, Shipment Tracking
– CCM
Environmental monitoring
• Petrel habitat on Great Duck Island in Maine.
• Questions to answer:
– Usage pattern of nesting burrows over the 24-72 hour cycle.
– Changes in the burrow and surface environmental
parameters.
– Differences in the micro-environments with and without large
numbers of nesting petrels.
Hierarchical deployment
Sensors
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Mica platform
– Atmel AVR w/ 512kB Flash
– 916MHz 40kbps RFM Radio
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Range: max 100 ft
Affected by obstacles, RF propogation
– 2 AA Batteries, boost converter
Mica weather board – “one size fits all”
– Digital Sensor Interface to Mica
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Onboard ADC sampling analog photo, humidity and passive IR sensors
Digital temperature and pressure sensors
– Designed for Low Power Operation
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Individual digital switch for each sensor
– Designed to Coexist with Other Sensor Boards
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Hardware “enable” protocol to obtain exclusive access to connector
resources
Packaging
– Conformal sealant + acrylic tube
– Placement
– Place above ground and in burrows (propagation?)
Gateway
• Communicate with sensor and base station.
• Solar powered (sensors are just battery
powered).
• Directional antenna pointed toward base
station.
Base station
• Laptops
• In lighthouse keepers house.
• Log all data and transmit via
satellite to D.C. and then on to
the Internet.
Smart Dust
• Design goals
– Cubic millimeter.
– Very low energy.
• Result: sensor package containing:
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Sensors
Optical transmitter (passive and active) and receiver
Signal processing
Solar power source
Smart dust applications
• Environmental monitoring.
– Insects.
– Meteorological phenomena.
• Special operations.
Smart dust components
Passive Transmitter with
Corner-Cube Retroreflector
Active Transmitter with Laser
Diode and Beam Steering
Receiver with Photodetector
Sensors
Analog I/O, DSP, Control
Power Capacitor
Solar Cell
Thick-Film Battery
1-2 mm
Smart dust: passive transmission
ModulatedDownlinkDataor
UnmodulatedInterrogation
BeamforUplink
Lens
Downlink
DataIn
Photodetector
Laser
Downlink
DataOut
Uplink
DataIn
Signal Selection
and Processing
CCD
Image Lens
Sensor
Array
Uplink
Uplink
Data ... Data
Out1
OutN
Base-StationTransceiver
ModulatedReflected
BeamforUplink
Corner-Cube
Retroreflector
DustMote
High power laser emitted from BS for
downlink and uplink communication.
Passive transmissions
• Reflect illuminating beam (from BS) back encoding
data.
• BS decodes data by reading the “on” and “off”
reflections.
• Rates of up to 1 kbps over 150m.
• Low power sensor power
• But, uninterrupted LoS with BS.
• A single CCD can decode multiple communications at
the same time.
– Each CCD element can “see” a small region of space. Each
element can decode one communication.
– Spatial multiplexing.
• While smart dust was introduced several years ago,
they are not yet feasible.
RFID
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Very low or no power identification
Very widely used
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Tolls (ez-pass)
Walmart
DoD
..
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Since DoD and Wal-Mart require RFID, many supplies use RFID. However, WalMart’s deployment has only been moderately successful.
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Energy method
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Active RF ID (uses a battery)
Passive
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Electromagnetic Inductive Coupling
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the current from the antenna is used to run a processor and send the signal
Driving the processor is not so hard, but transmitting the signal is difficult with such small power.
This approach only works to a meter (or less)
Backscatter
Because of energy issues, RFID misses cause problems
Backscatter Radio Links
•
When an antenna receives the signal,
current moves along the antenna.
On the other hand, if current is moving
along an antenna, the antenna is
transmitting a signal.
However, if the antenna is an open
circuit, then no current can flow, and
hence no transmission occurs.
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By controlling whether the antenna is
an open or closed circuit, we can
generate and not generate a
transmitted signal.
Backscatter Radio Links
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The reader must use specialized components such
as circulators or couplers to ensure that only
reflected signals are received.
•
But there are many objects that will reflect the
signal.
These reflections might be stronger than the tag.
Each reflection is a sine wave at the same
frequency, but with different phase and amplitude,
Recall that the sum of different sine waves at the
same frequency, but different phase and amplitude
is again a sine wave with some phase and
amplitude, where the resulting phase and amplitude
depends on the phases and amplitudes of the
different sine waves.
Thus, when the antenna is made a short circuit or
not, the phase (and amplitude) will change.
By detecting changes in the phase, we can detect
that the antenna was changed.
E.g., if the phase changes quickly (say 50 times a
sec), then a zero is received. If the Phase changes
slowly (25 times a sec), then a one is received.
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This works at ranges 3-5 meters or more.
Biomedical applications
• Health monitors.
– Glucose level.
– Digestive system.
– Vascular system, etc.
• Artificial retina.
Sensors for vision
Today: Introduction
• Sensor networks.
– Definition, motivation, examples
• Challenges.
Challenges
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•
•
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Energy
Self-configuring/adapting
Data processing
Scalability
Energy
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Sensors may require long life times
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Great Duck Island required 9 months
If embedded in highways, 10 years is required
Pacemaker (not just a sensor!) last 5-10 years
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(supervisory control and data acquisition (SCADA))
Energy
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Sensors may require long life times
– Great Duck Island required 9 months
– If embedded in highways, 10 years is required
– Pacemaker (not just a sensor!) last 5-10 years
•
•
(supervisory control and data acquisition (SCADA))
Approaches
– Low duty cycle systems.
• Sleep: deep sleep (everything off), listening but not transmitting, periodic listening
• Increases delay => impacts QoS
• Nodes must be synchronized
– Requires good clocks (which require more power)
– As battery power drops, clocks may experience severe drift, reducing effective lifetime
Energy
•
Sensors may require long life times
–
–
–
Great Duck Island required 9 months
If embedded in highways, 10 years is required
Pacemaker (not just a sensor!) last 5-10 years
•
•
(supervisory control and data acquisition (SCADA))
Approaches
–
Low duty cycle systems.
•
•
•
Sleep: deep sleep (everything off), listening but not transmitting, periodic listening
Increases delay => impacts QoS
Nodes must by synchronized
–
–
–
Requires good clocks (which require more power)
As battery power drops, clocks may experience severe drift, reducing effective lifetime
Low bit-rate
•
Low power transmissions require low bit-rate
Energy
•
Sensors may require long life times
–
–
–
Great Duck Island required 9 months
If embedded in highways, 10 years is required
Pacemaker (not just a sensor!) last 5-10 years
•
•
(supervisory control and data acquisition (SCADA))
Approaches
–
Low duty cycle systems.
•
•
•
Sleep: deep sleep (everything off), listening but not transmitting, periodic listening
Increases delay => impacts QoS
Nodes must by synchronized
–
–
–
Low bit-rate
•
–
Requires good clocks (which require more power)
As battery power drops, clocks may experience severe drift, reducing effective lifetime
Low power transmissions require low bit-rate
Complicated communication schemes
•
•
•
Nodes can cooperate to transmit far with low power.
Advanced data compression
However, CPU uses power as well. But CPU power usage is decreasing as technology
advances.
Energy
•
Sensors may require long life times
–
–
–
Great Duck Island required 9 months
If embedded in highways, 10 years is required
Pacemaker (not just a sensor!) last 5-10 years
•
•
(supervisory control and data acquisition (SCADA))
Approaches
–
Low duty cycle systems.
•
•
•
Sleep: deep sleep (everything off), listening but not transmitting, periodic listening
Increases delay => impacts QoS
Nodes must by synchronized
–
–
–
Low bit-rate
•
–
Low power transmissions require low bit-rate
Complicated communication schemes
•
•
•
–
Requires good clocks (which require more power)
As battery power drops, clocks may experience severe drift, reducing effective lifetime
Nodes can cooperate to transmit far with low power.
Advanced data compression
However, CPU uses power as well. But CPU power usage is decreasing as technology
advances.
Efficient protocols
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Avoid retransmissions
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Avoid end-to-end retransmission with link layer retransmissions and data caching
Avoid contention
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FDM
TDM
Energy
• Approaches
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Renewable power/scavenging.
Solar energy.
Mechanical vibrations (sneakers)
Radio-Frequency inductance (RFID)
Infrared inductance (passive optical)
Self-configuring/adapting
• Ad hoc deployment; inaccessible areas;
– E.g, dropped from airplane
• Adapt to unpredictable environment.
– E.g., nodes break, crash, run out of power (consider a
deployment where each node will last only a week, but
nodes come out of hibernation at random times so the
network has a lifetime of several months (see HW on next
page))
• Unattended, untethered
– There is no one to reboot …
• Fault tolerant and robust
– approaches
• Each sensor operate autonomously from neighbors.
• Overlapped services area.
• No single point of failure.
Homework
• Suppose that there are N sensor.
• Each sensor turns on at time t where t is a random
variable that is uniformly distributed between 0 and T.
• Once on, the sensor will remain on for  seconds.
• Find N (as a function of T and ) such that at time T/2,
the average number of on sensors is M.
• Hint:
– What is the probability that a particular sensor will be on at
time T/2?
– Use binomial distribution
Challenges for embedded biomedical sensors:
• Limited capabilities: power, processing,
storage, and communication.
• Continuous operation.
• Robustness and fault tolerance.
• Scalabilty.
• Self-configuring, self-managing, self-healing.
• Data-related issues.
Application-specific issues
• Material Constraints.
– Bio-Compatibility.
– Inconspicuous.
• Imitative of the environment.
• Detect-proof: e.g. stealth flight.
• Security.
– Privacy.
– Interference.
• Regulatory issues
– Such as FDA requirements.
Data processing
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Cooperation
– Exploit computation near data to reduce communication.
– Collaborative signal processing
• E.g., nearby images are combined to determine the exact location of an object.
The position of the object is the data sent to the base station, not the images.
– Data aggregation/compression
• If data is spatially correlated, then data can be aggregated and compressed,
taking advantage of correlation
Data processing
•
Cooperation
– Exploit computation near data to reduce communication.
– Collaborative signal processing
• E.g., nearby images are combined to determine the exact location of an object.
The position of the object is the data sent to the base station, not the images.
– Data aggregation/compression
• If data is spatially correlated, then data can be aggregated and compressed,
taking advantage of correlation
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Limited computation and storage capabilities
Error propagation – decrease fault tolerance
– The more sensors a process uses, the lower the robustness but more
efficient.
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Complicated cooperation may require intensive communication.
Heterogeneous power dissipation and lifetime
– Nodes closer to base station must carry more data and are also in a better
position to aggregate data. But these will expend energy.
•
Trade-off between latency and energy
Data processing
• Distributed representation/storage
• Data Centric Protocols, in-network processing:
– Interpretation of spatially distributed data (Per-node processing
alone is not enough).
– Network performs in-network processing based on distribution of
data.
– Queries automatically directed towards nodes that maintain
relevant/matching data.
– Pattern-triggered data collection
• Multi-resolution data storage and retrieval.
– E.g., many nodes hold general information, thus it is easy to get.
– More detailed information requires contacting more nodes
– Highly detailed information requires commutation with a large number of
nodes.
– If mostly low resolution data is required, then such a scheme is very useful
» low resolution = the enemy is crossing the border
» Medium resolution = the enemy is in the north
» High resolution = there is a enemy truck at the corner of Delaware and
S. Chapel st.
• Distributed edge/feature detection.
• Index data for easy temporal and spatial searching.
• Finding global statistics (e.g., distribution).
Traditional approach: warehousing
• Data extracted from sensors, stored on
server.
• Query processing takes place on server.
Sensor Database System
• Sensor Database System supports distributed
query processing over sensor network
Sensor database system
• Characteristics of a
sensor network:
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Streams of data.
Large number of nodes
Multi-hop network.
No global knowledge
about the network.
Node failure and
interference is common.
Energy scarce.
Limited memory
No administration,
• Can existing database
techniques be reused?
• What are the new problems
and solutions?
– Representing sensor data.
– Representing sensor
queries.
– Processing query fragments
on sensor nodes.
– Distributing query
fragments.
– Adapting to changing
network conditions.
– Dealing with site and
communication failures.
– Deploying and managing a
sensor database system.
Time and location
• Unlike the Internet, time and spatial location essential
for some applications.
– E.g., localization and time synchronization needed to detect
events, compare detections across nodes, perform
collaborative processing, geo forwarding/routing, etc.
– GPS provides solution (with differential GPS providing finer
granularity).
– GPS not always available.
– Resolution is not very good (10’s of meters)
– Other approaches?
• To correlate events, the time of the event must be
known.
• For coordinated sleeping, the sensors must be
synchronized.
Coverage
• Area coverage:fraction of
area covered by sensors
– Detectability: probability that
sensors detect moving
objects
– Overlap: fraction of sensors
covered by other sensors
– Control:
• Where to add new nodes
for max coverage.
• How to move existing
nodes for max coverage.
Why not Internet protocols?
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Traditional networks have hosts and routers.
Sensor nodes are both hosts and routers
Internet protocols were designed following an e2e approach.
For efficiency, need to use information from lower-layers.
Sensor networks are data-driven; end points don’t matter.
IP are bidirectional
Sensor networks are directional - Data flow and control flow
TCP/IP is wasteful, lazy convergence – it will work eventually
Sensor networks must be very thrifty - energy constraint issues
IP – the system admin is never very far away
Sensor network - Self-organization, self-management.
IP – local networks are fairly small
Sensor networks could contain of an arbitrarily large number of
small sensors generating data.
Today: Introduction
• Sensor networks.
– Definition, motivation
• Challenges.
Sample layered architecture
Resource constraints call
for more tightly integrated
layers
Open Question:
What are defining
architectural
principles?