Transcript WSN Introduction

Introduction to Wireless Sensor
Networks
Biljana Stojkoska
[email protected]
University “Ss. Cyril and Methodius”,
Skopje
A wireless
sensor network
(WSN)Networks
is a wireless network
Wireless
Sensor
consisting of spatially distributed autonomous devices
using sensors to cooperatively monitor physical or
environmental conditions, such as temperature, sound,
vibration, pressure, motion or pollutants, at different
locations.
Network Model for WSN
continue …
A base station links the sensor network to another
network (like a gateway) to disseminate the data
sensed for further processing. Base stations have
enhanced capabilities over simple sensor nodes since
they must do complex data processing.
How the story started
The development of wireless sensor networks was
originally motivated by military applications such as
battlefield surveillance. However, wireless sensor
networks are now used in many civilian application
areas, including environment and habitat monitoring,
healthcare applications, home automation, and traffic
control.
Military
Remote deployment of sensors
for tactical monitoring of enemy
troop movements.
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Sensors
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The overall architecture of a sensor
node consists of:
– The sensor node processing
subsystem running on sensor
node main CPU
– The sensor subsystem and
– The communication subsystem
The processor and radio board
includes:
– TI MSP430 microcontroller with
10kB RAM
– 16-bit RISC with 48K Program
Flash
– IEEE 802.15.4 compliant radio at
250 Mbps
– 1MB external data flash
– Runs TinyOS 1.1.10 or higher
– Two AA batteries or USB
– 1.8 mA (active); 5.1uA (sleep)
Crossbow Mote
TPR2400CA-TelosB
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What is a mote?
Imote2 06 with
enalab camera
• mote noun [C] LITERARY
something, especially a bit of dust, that is so small it is almost
impossible to see
---Cambridge Advanced Learner’s Dictionary
http://dictionary.cambridge.org/define.asp?key=52014&dict=C
ALD
Evolution of Sensor Hardware Platform (Berkeley), [Alec Woo
2004]
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Mica2 Wireless Sensors
MTS310 Sensor Boards
• Acceleration,
• Magnetic,
• Light,
• Temperature,
• Acoustic,
• Sounder
New MicaZ follows IEEE 802.15.4 Zigbee
standard with direct sequence sprad
spectrum radio and 256kbps data rate
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Motes and TinyOS
• Motes (Mica2, Mica2dot, MicaZ)
– Worked well with existing curriculum
• ATMega128L microcontroller
• 128KB program flash; 512KB measurement Flash; 4KB EEPROM
– Standard platform with built-in radio chicon1000 (433MHz, 916MHz, 2.4GHz)
38.4kb; 256kbps for MicaZ IEEE 802.15.4. (1000ft, 500ft; 90/300ft) range
– AA battery
– Existing TinyOS code base
– Convenient form factor for adding sensors
• TinyOS
– Event-based style helped students understand:
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Time constraints
Code structure (need to write short non-blocking routines)
– Existing modular code base saved time
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Made a more complex project possible
Provided a degree of abstraction
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Networked vs. individual sensors
• Extended range of sensing:
– Cover a wider area of operation
• Redundancy:
– Multiple nodes close to each other increase fault
tolerance
• Improved accuracy:
– Sensor nodes collaborate and combine their data to
increase the accuracy of sensed data
• Extended functionality:
– Sensor nodes can not only perform sensing
functionality, but also provide forwarding service.
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Sensor Network
Antenna
Server
Interface
electronics, radio
and microcontroller
Soil moisture
probe
Communications
barrier
Sensor field
Mote
Gateway
Internet
Sensor Network
Server
Watershed
Sensor
field
Gateway
Internet
Network Architectures
Layered
Architecture
Base
Statio
n
Clustered
Architecture
Base
Statio
n
Layer 1
Layer 2
Layer 3
Larger Nodes denote Cluster Heads
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Wireless Sensor Network
Stargate
• 802.11a/b
• Ethernet
• Mica2
• PCMCIA
• Compactflash
• USB
• JTAG
• RS232
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Why Wireless Sensors Now?
• Moore’s Law is making sufficient CPU performance available
with low power requirements in a small size.
• Research in Materials Science has resulted in novel sensing
materials for many Chemical, Biological, and Physical sensing
tasks.
• Transceivers for wireless devices are becoming smaller, less
expensive, and less power hungry.
• Power source improvements in batteries, as well as passive
power sources such as solar or vibration energy, are
expanding application options.
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Computer Revolution
Original IBM PC (1981)
MICAZ Mote (2005)
4.77 MHz
4 MHz
16-256 KB RAM
128 KB RAM
160 KB Floppies
512 KB Flash
~ $6K (today)
~ $35
~ 64 W
~14 mW
25 lb, 19.5 x 5.5 x 16 inch
0.5 oz, 2.25 x 1.25 x 0.25 inch
Advances in Wireless Sensor Nodes
Consider Multiple Generations of Berkeley Motes
Model
Rene
Mica
Mica-2
Mica-Z
Date
1999
2002
2003
2004
CPU
4 MHz
4 MHz
4 MHz
4 MHz
Flash
Memory
8 KB
128 KB 128 KB
128 KB
RAM
512 B
Radio
4 KB
4 KB
4 KB
10 Kbps 40 Kbps 76 Kbps 250 Kbps
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Characteristics and challenges
• Deeply distributed architecture: localized coordination to reach
entire system goals, no infrastructure with no central control
support
• Autonomous operation: self-organization, self-configuration,
adaptation, exception-free
– TCP/IP is open, widely implemented, supports multiple physical
network, relatively efficient and light weight, but requires
manual intervention to configure and to use.
• Energy conservation: physical, MAC, link, route, application
• Scalability: scale with node density, number and kinds of networks
• Data centric network: address free route, named data,
reinforcement-based adaptation, in-network data aggregation
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Common system services
Localization &
Time Synchronization
Calibration
In Network Processing
Programming Model
Routing and Transport
Event Detection
Needed: Reusable, Modular, Flexible, Well-characterized Services/Tools
• Routing and Reliable transport
• Time synchronization, Localization, Calibration, Energy Harvesting
• In Network Storage, Processing (compression, triggering), Tasking
• Programming abstractions, tools
• Development, simulation, testing, debugging
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Key Properties
• Networks meaningfully
distributed over physical space
• Large numbers of nodes
• Long duration
• Irregular, varying connectivity
• Variations in density
• Loss & interference
• Constrained resources & Energy
• Connected to deeper
infrastructure
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Reguirements
• Wireless sensors need to operate in conditions
that are not encountered by typical computing
devices:
– Rain, sleet, snow, hail, etc.
– Wide temperature variations
• May require separating sensor from electronics
– High humidity
– Saline or other corrosive substances
– High wind speeds
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Sensor deployment
• Sensor deployment is a critical issue because
it affects the cost and detection capability of a
wireless sensor network
• A good sensor deployment should consider
both coverage and connectivity
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Coverage
Connectivity
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Coverage Problems
• Coverage: is a measure of the Quality of service
of a sensor network
• How well can the network observe (or cover) a
given event?
– For example, intruder detection; animal or fire
detection
• Coverage depends upon:
– Range and sensitivity of sensing nodes
– Location and density of sensing nodes in given region
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Routing
Phenomenon
being sensed
Data aggregation
takes place here
Sink
Multihop routing is common due to limited transmission range
Some routing issues in WSNs
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Limited node mobility
Power aware
Irregular topology
MAC aware
Limited buffer space
Why not port Ad Hoc Protocols?
• Ad Hoc networks require significant amount of
routing data storage and computation
– Sensor nodes are limited in memory and CPU
• Topology changes due to node mobility are
infrequent as in most applications sensor nodes
are stationary
– Topology changes when nodes die in the network due
to energy dissipation
• Scalability with several hundred to a few
thousand nodes not well established
• GOAL: Simple, scalable, energy-efficient protocols
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WSN vs. MANET
• Wireless sensor networks may be considered a subset
of Mobile Ad-hoc NETworks (MANET).
• WSN nodes have less power, computation and
communication compared to MANET nodes.
• MANETs have high degree of mobility, while sensor
networks are mostly stationary.
– Freq. node failures in WSN -> topology changes
• Routing protocols tend to be complex in MANET, but
need to be simple in sensor networks.
• Low-power operation is even more critical in WSN.
• MANET is address centric, WSN is data centric.
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Wireless Sensor Network(WSN) vs. Mobile
Ad Hoc Network (MANET)
WSN
MANET
Similarity
Wireless
Multi-hop networking
Security
Symmetric Key Cryptography
Public Key Cryptography
Routing
Support specialized traffic
pattern. Cannot afford to have
too many node states and
packet overhead
Support any node pairs
Some source routing and
distance vector protocol incur
heavy control traffic
Resource
Tighter resources (power,
processor speed, bandwidth)
Not as tight.
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A Rosy Future for Wireless Sensors?
• Is the effort on wireless sensor protocols a
waste of time??
• Can we just wait 10-15 years until we have
sensors that are very powerful??
 NO!! Will still face:
– Very limited storage
– Modest power supplies
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Data processing in WSN
• Energy Management Issues
Taxonomy of data driven energy saving
Data processing in WSN
Data Aggregation
Phenomenon being sensed
• In-network fusion of data from different
sensors to eliminate redundant transmissions
to the base station.
• Aggregation takes place if the data arriving
the common node have same attributes of
the phenomenon being sensed
• This significantly reduces the amount of data traffic as well as the distances over
which the data needs to be transmitted
• In the process of in-network aggregation, the value generated at each
sensor in each round must influence the value that reaches the basestation in that round !
• Problem: generating aggregation tree
Data processing in WSN
Dual Prediction Scheme
• each node runs a filter (or a model) that
estimates next sensor reading
• sink node runs exactly the same models for
each sensor in the network and makes the
same predictions
• sensor makes measurements of the sensed
quantity
• Used filters (models)
– Kalman filter
– Least Mean Square adaptive filter
– AR, MA, ARMA, ARIMA
Dual Prediction Scheme
• Simulation results for two variants of LMS algorithm for
temperature readings from Intel network laboratory
Dual Prediction Scheme
• Simulation results for two variants of LMS algorithm for
humidity readings from Intel network laboratory
Dual Prediction Scheme
• Simulation results for two variants of LMS algorithm for
temperature readings from Intel network laboratory
considering cluster-based approach
Localization
Location necessary in order for sensed data to be meaningful e.g. forest fire detection
Location information is taken for granted in many network designs, e.g. geographic
routing
Equipping each node with GPS is not always feasible due to power constraints and other
limitations inherent to sensor networks
Nodes can often measure their distances to nearby nodes, e.g. ultra-wideband ranging
Localize using inter-node distances
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Localization ?
A network in the plane
?
?
Anchors are nodes whose positions are known
Anchor positions from GPS or manual configuration.
The distances between some nodes are known
?
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The network localization problem is to determine the positions of all the nodes
The network is localizable if there exists exactly one position in the plane corresponding to
each non-anchor node so that all known inter-node distances are satisfied
A node is localizable if its position is uniquely determined by the known inter-node
distances and anchor positions
WSN Applications
• Wide area monitoring tools supporting Scientific Research
– Wild life Habitat monitoring projects Great Duck Island (UCB), James
Reserve (UCLA), ZebraNet (Princeton.
– Building/Infrastructure structure study (Earthquake impact)
• Military Applications
– Shooter Localization
– Perimeter Defense (Oil pipeline protection)
– Insurgent Activity Monitoring (MicroRadar)
• Commercial Applications
– Light/temperature control
– Precision agriculture (optimize watering schedule)
– Asset management (tracking freight movement/storage)
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Traffic Management & Monitoring
 Future cars could use
wireless sensors to:
– Handle Accidents
– Handle Thefts
Sensors embedded in
the roads to:
–Monitor traffic flows
–Provide real-time route
updates
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Smart Home / Smart Office
• Sensors controlling
appliances and
electrical devices in
the house.
• Better lighting and
heating in office
buildings.
• The Pentagon building
has used sensors
extensively.
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Industrial & Commercial
• Numerous industrial and commercial
applications:
– Agricultural Crop Conditions
– Inventory Tracking
– In-Process Parts Tracking
– Automated Problem Reporting
– RFID – Theft Deterrent and Customer Tracing
– Plant Equipment Maintenance Monitoring
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Usage of Sensor Networks
Healthcare:
Sensors can be used in biomedical applications to
improve the quality of the provided care. Sensors are
implanted in the human body to monitor medical
problems like cancer and help patients maintain their
health.
Mercury: A Wearable Sensor Network Platform
for High-Fidelity Motion Analysis
SHIMMER wearable mote
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developed by the Digital Health Group at Intel
TI MSP430 processor,
CC2420 IEEE 802.15.4 radio,
triaxial accelerometer,
rechargeable Li-polymer battery
MicroSD slot supporting up to 2 GBytes of
Flash memory
Mercury network
• Harvard University and Spaulding Hospital
• long-term motion analysis studies in a home
setting
• Each sensor samples multiple channels of
accelerometer, gyroscope, and/or physiological
data and stores raw signals to local flash
• Sensors also perform feature extraction on the
raw signals, which may involve expensive onboard computation
• Nodes also save energy by dropping down to a
low-power state when the sensor is not moving.
Mercury network
• Parkinson's disease
– disease affects about 3% of the population over the age of 65 years
– number of hours of ON (i.e., when medications effectively attenuate tremor)
and OFF time
– 9 nodes on the body (2 on each arm and leg, one on the back)
– recording triaxial accelerometer and gyroscope data device for measuring or
maintaining orientation at 100 Hz
– SVM for data classification (transmitting features estimated from the raw data,
instead of transmitting the raw data itself)
• Epilepsy
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starting up project
4 sensors on arms and only 2 on legs
Accel, gyro, and EMG data is collected
Electromyography (EMG) is a technique for evaluating and recording the
electrical activity produced by skeletal muscles
– EMG is be localized (say, one arm) and sampled at 500 Hz
Wireless Body Area Network
E. Jovanov, et al., “A wireless body area network of intelligent motion sensors for computer assisted
physical rehabilitation,” Journal of NeuroEngineering and Rehabilitation, 2005, 2:6
Wireless Body Area Network
The personal server can be implemented on an Internet-enabled PDA or a 3G mobile
phone, or a regular laptop of desktop computer. It can communicate with remote
upper-level services in a hierarchical type architecture. Its tasks include:
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Initialization, configuration, and synchronization of WBAN nodes
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Control and monitor operation of WBAN nodes
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Collection of sensor readings from physiological sensors
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Processing and integration of data from the sensors
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Secure communication with remote healthcare provider
Framework for Medical Image Analysis
The remote medical image repositories communicate through different types of network
connections with the central computing site that coordinates the distributed analysis.
V, Megalooikonomou, et al., “Medical Data Fusion for Telemedicine,” IEEE Engineering in Medicine and
Biology Magazine, pp. 36-42, September/October 2007
Usage of Sensor Networks
Building Monitoring:
Sensors can also be used in large buildings or factories
monitoring
climate
changes.
Thermostats
and
temperature sensor nodes are deployed all over the
building’s area. In addition, sensors could be used to
monitor vibration that could damage the structure of a
building.
Illinois Structural Health Monitoring
Project (ISHMP)
• Manual inspection of bridges (measure acceleration),
– costs millions of dollars
– relatively unreliable
– can only be carried out sporadically
• But ….Why WSN, why not traditional sensors?
• Tsing Ma Bridge and Kap Shui Mun Bridge in Hong
Kong, which uses 326 channels of sensors in total and
produces about 65 MBytes of data every hour, is an
attempt toward in-depth monitoring.
• Limited deployment
• Cost
– the total system cost of the monitoring system (including
installation) on the Bill Emerson Memorial Bridge in Cape
Girardeau, Missouri, USA is approximately $1.3M for 86
accelerometers, which makes the average installed cost
per sensor a little over $15,000.
Start from scratch
distributed implementation of vibration-based
damage detection algorithms
• Imote2 hardware (homogenous network)
• TinyOS middleware services
• hierarchical
network
topology
(base smart
station,
manager
• the most
powerful
and promising
wireless
sensor
node, platform
cluster head nodes, and leaf nodes), but their
roles
can be reassigned in the case when some nodes
• built with 32 bit XScale processor
stop working due to battery exhaustion, OS failure, etc
• RAM of 32 MB
• newly developed damage detection algorithm (most of
flash memory of 32 MB,
the •SHM
methods mentioned above employ modal
• Integrated
radio
with parameters
a built-in 2.4 such
GHz antenna
analysis
to obtain
modal
as natural
frequencies, damping ratios, and mode shapes)
cost as little as $200/node and prices are falling rapidly while functionality is
improving
Usage of Sensor Networks
Environmental Observation:
Sensor networks can be used to monitor environmental changes.
An example could be water pollution detection in a lake that is
located near a factory that uses chemical substances. Sensor
nodes could be randomly deployed in unknown and hostile areas
and relay the exact origin of a pollutant. Other examples include
forest fire detection, air pollution and rainfall observation in
agriculture.
Camalie Vineyards
Case Study in Crossbow Mote
Deployment.
Background
• Camalie Vineyards is a 4.4 acre hillside vineyard
on the western slopes of Napa Valley: Mt Veeder
• Highly varied soil, slope, sun exposure, and water
flow, At least 12 distinct areas.
• World class Cabernet Sauvignon grapes; $5K/ton
– French clones 337, 338, 191 on rootstocks selected to
compensate for varying vigor across vineyard areas.
• Water is scarce, most wells and reservoirs are dry
by the end of the growing season. 60 gal/vine/yr.
300K gal.
Water in the Vineyard
Initial Installation
2005 growing season
• Monitor water getting to the vines and the
irrigation system getting it there.
• 1 mote with 3 sensors in each of 4 irrigation
blocks
• 2 pressure sensors at irrigation manifold, pre
filter and post filter. Monitor tank level and
filter status. .
2003-2004 used weather station with 3 soil moisture
sensors at one location
Vineyard Installation
• At each Mote location:
• 2 soil moisture sensors
• 12” and 24” depth
• 1 soil temp sensor to calibrate
soil moisture sensors
Irrigation Manifold
Vineyard Mote Prototype
• 433MHz Mica2dot
• Solar power supply
• Up to 6 resistive sensor inputs
Power Supply
• 2 month max battery life now with 10 minute
sampling interval
• Decided to use solar power, always there
when doing irrigation. Solar cell $10 in small
quantities though and need a $.50 regulator.
Network Maps
13 nodes late 05, Now 18 nodes
Irrigation Block Map
Soil Moisture Data
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•
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Red = 12” depth soil moisture
Green= 24” depth soil moisture
Note delay deeper
More frequent, shorter watering keeps water shallow
Irrigation Pressure Sensors
Temp Data
Software for WSN
• Multi-tier architecture
• Each layer must be carefully designed
• Programming sensor nodes
– TinyOS
– NesC
– TinyDB
event result_t Timer.fired()
{
event
result_t MyI2C.sendEndDone(){
call Leds.redToggle();
call Leds.yellowToggle();
call MyI2C.sendStart();
return SUCCESS;
return
SUCCESS;
}
}
• Xmesh
• XServe
• Tossim (Power Tossim)
Software for WSN
• Mote-VIEW is a full-featured data analyzing
application developed by Crossbow
– visualize the topology,
– produce graphs from selected motes,
– check status of the nodes
– export sensor readings to a database
Software for WSN
• jWebDust
• Java based application
• consists of software components that use
interfaces to communicate with each other
• new services can be developed and integrated
with the modification of existing services
• University of Patras, Patras, Greece
• A framework for service provisioning in virtual
sensor networks (april 2012)
Web Interface for Habitat Monitoring
using Wireless Sensor Network
FUNCTIONALITIES OF THE WEB INTERFACE
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Sensor readings
Topology control
Statistics
Dynamic graphs
FUNCTIONALITIES OF THE WEB INTERFACE
• Smart phone controlling the WSN
• Interacting with the WSN
What will Wireless Sensor Networks
Look Like in the Near Future?
Large-Scale Deployments
• Sensor networks
will grow in size
because of:
– Lower cost
– Better protocols
– Advantages of
dense networks
Sensing Zone with sensor-coordinator,
sensing-collaborators, and backbone nod
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Heterogeneous Sensors
• Homogeneous network of sensors has
been the typical assumption, but not the
future!!
– Combining sensors with different functions
– Hierarchy of sensors – a few expensive
powerful sensors with more cheap sensors
• Useful for special communication nodes
– A few sensor nodes with expensive sensors,
such as GPS-equipped sensors
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Mobile Sensors
Sensors with Micromachines
Low-Power Motors that Support Mobility
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General Purpose Sensors
 Single-purpose network is the typical assumption, but not the future!!
– Sensors for evolving applications
– Sensors that can adapt to changing objectives
– More memory and CPU will allow more
complex applications
– Flexibility increases marketability
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Overlapping Coverage Areas
• Sensors will be deployed for specific
applications, but
– These deployments will overlap physically
– Sensors will have different properties
– Users will want to combine these different
sensors for new applications:
• Temperature sensors for HVAC control
• Location tracking of employees
• Combine these for fire rescue operations
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Mixture of Wired and Wireless
• Wireless sensors will become a seamless
part of larger networks!
– Combining wired sensors with wireless sensors
• Wired sensors can have more power
• Wired sensors can run TCP/IP
– Accessing wireless sensors through the Internet
• Need a gateway to translate requests
• Uploading/downloading information remotely
• Modifying wireless sensor tasks remotely
– Increased direct user interaction
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The Traditional WSN Myth
 The wireless sensor network
paradigm was a myth from the
late 1990s
 Usual “assumptions”:
– 1000s of homogeneous
“sensing only” nodes
– Mesh routing all nodes
 This market is marginal
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•
Sink
Luckily, the ideas and algorithms that were developed can be applied to ubiquitous
wireless applications
Huge research and market potential
Sensors Everywhere
Some current issues:
 There are already many deployed sensors
– Mobile phones
– Surveillance cameras
– GPS receivers
– Motion and light sensors
 How to organize them in networks
 How to retrieve, store, and index data from sensors
 Change the attention from “network” to “data”
 Combine data processing with data delivery
Convergent WSNs
How do we integrate the Internet and
Intranets with sensor networks?
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Where is the intelligence?
Heterogeneous protocol interfaces?
Scalability and security are important issues
Mobility support
Gateways play an important role, as they
communicate with TCP/IP and sensor
networks
– A proxy application often used to translate and
shield one level from another
Convergent WSNs
Looking at the sales of uCs, there is a
potential for billions of networked devices –
much larger than the Internet itself
Huge impact also on the core Internet
– IPv6 will be key to supporting convergent sensor
networks
– Intelligent data processing to reduce the network
traffic
Economic Factors
• New technologies replace existing
technologies or fill new niches when there
are economic advantages.
– Wireless sensors will replace wired sensors
• No wiring – lower costs
• More flexible deployments
– Wireless sensors will provide new services
• Provide cost advantages or lower overhead
• Improve product quality or product features
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Summary and Conclusions
• Wireless sensor networks have a bright future
– Many applications have been proposed
– Potential to revolutionize human-computer
interactions
– Availability of sensors will lead to new and exciting
applications
• A lot of research remains to be done
– Wireless sensors will not evolve into traditional
computers
– Many obstacles to overcome
– Allow realism to guide research efforts
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