Wireless sensor network

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21 Νοε 2013 (πριν από 3 χρόνια και 11 μήνες)

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Wireless

sensor network


Introduction

A
wireless sensor network

(WSN) is a
wireless network
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
. 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 industrial and civilian application
areas, including industrial process

monitoring and control, machine health
monitoring, environment and habitat monitoring, healthcare applications,
home automation
, and traffic control.


In addition to one or
more sensors, each node in a sensor network is
typically equipped with a
radio

transceiver

or other wireless communicati
ons
device, a small
microcontroller
, and an energy source, usually a
battery
. The
envisaged size of a single
sensor node

can vary from shoebox
-
sized nodes
down to devices the size of grain of dust although functioning 'motes' of
genuine microscopic dimension
s have yet to be created. The cost of sensor

nodes is similarly variable, ranging from hundreds of dollars to a few cents,
depending on the size of the sensor network and the complexity required of
individual sensor nodes. Size and cost constraints on sens
or nodes result in
corresponding constraints on resources such as energy, memory,
computational speed and bandwidth.


A sensor network normally constitutes a
w
ireless ad
-
hoc network
, meaning
that each sensor supports a multi
-
hop

routing algorithm (several nodes may
forward data packets to the base station).

I
n
computer science

and
telecommunications
, wireless sensor networks are
an active re
search area with numerous workshops and conferences arranged
each year.


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Applications

The applications for WSNs are many and varied, but typically involve some
kind of monitoring, tracking, and controlling. Specific applications for
WSNs include habitat

monitoring, object tracking, nuclear reactor control,
fire detection, and traffic monitoring. In a typical application, a WSN is
scattered in a region where it is meant to collect data through its sensor
nodes.

Area monitoring

Area monitoring is a common
application of WSNs. In area monitoring, the
WSN is deployed over a region where some phenomenon is to be monitored.
For example, a large quantity of sensor nodes could be deployed over a
battlefield to detect enemy intrusion instead of using
landmines
. When the
sensors detect the event being monitored (heat, pressure, sound, light,
electro
-
magnetic field, vibration, etc), the event needs to be reported to one
of the base stations, which
can take appropriate action (e.g., send a message
on the internet or to a satellite). Depending on the exact application,
different objective functions will require different data
-
propagation
strategies, depending on things such as need for
real
-
time

respo
nse,
redundancy

of the data (which can be tackled via
data aggregation

techniques), need for
security
,

etc.



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Environmental monitoring

A number of WSN deployments have been done in the past in the context of
environmental monitoring. Many of these have been

short lived, often due to
the prototypical nature of the projects. A more long
-
lived deployment is
monitoring the state of permafrost in the swiss alps
.


Characteristics

Unique characteristics of a WSN include:



Limited power they can harvest or store



Abil
ity to withstand harsh environmental conditions



Ability to cope with node failures



Mobility of nodes



Dynamic network topology



Communication failures



Heterogeneity of nodes



Large scale of deployment



Unattended operation

Sensor nodes can be imagined as small

computers, extremely basic in terms
of their interfaces and their components. They usually consist of a
processing unit

with limited computational power and limited memory, The
base stations are one or more distinguished components of the WSN with
much mo
re computational, energy and communication resources. They act
as a gateway between sensor nodes and the end user.

Platforms

Standards

Several standards are currently either ratified or under development for
wireless sensor networks.
ZigBee

is a mesh
-
networking standard intended
for uses such as embedded sensing, medical data collection, consumer
devices like television remote controls, and home automation. Zigbee is
promoted by a large consort
ium of industry players.
WirelessHART

is an
extension of the
HART Protocol

and is specifically designe
d for Industrial
applications like Process Monitoring and Control
.



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Hardware

The main challenge is to produce
low cost

and
tiny

sensor nodes. With
respect to these objectives, current sensor nodes are mainly prototypes.
Miniaturization and low cost are und
erstood to follow from recent and future
progress in the fields of
MEMS

and
NEMS
. Some of the existing sensor
nodes are given below. Some of the nodes are still in research stage.

An overview of commonly used sensor network platforms, components,
technology and related topics is available
in the
SNM
-

Sensor Network
Museum
.

Software

Energy is the scarcest resource of WSN nodes, and it determines the lifetime
of
WSNs. WSNs are meant to be deployed in large numbers in various
environments, including remote and hostile regions, with ad
-
hoc
communications as key. For this reason, algorithms and protocols need to
address the following issues:



Lifetime maximization



Rob
ustness and fault tolerance



Self
-
configuration

Some of the "hot" topics in WSN software research are:



Security



Mobility (when sensor nodes or base stations are moving)



Middleware: the design of middle
-
level primitives between the
software and the hardware

Operating systems

Operating systems

for wireless sensor network nodes are typically less
complex than general
-
purpose operating systems both because of the special
requirements of sensor network applications and because of the resource
constraints in senso
r network hardware platforms. For example, sensor
network applications are usually not interactive in the same way as
applications for PCs. Because of this, the operating system does not need to
include support for user interfaces. Furthermore, the resourc
e constraints in

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terms of memory and memory mapping hardware support make mechanisms
such as virtual memory either unnecessary or impossible to implement.

Wireless sensor network hardware is not different from traditional embedded
systems and it is therefo
re possible to use embedded operating systems such
as
eCos

or
uC/OS

for sensor networks. However, such operating systems are
often des
igned with real
-
time properties. Unlike traditional embedded
operating systems, however, operating systems specifically targeting sensor
networks often do not have real
-
time support.

TinyOS

is

perhaps the first
]

operating system specifically designed for
wireless sensor networks. Unlike most other operating systems, TinyOS is
based on an
event
-
dri
ven programming

model instead of
multithreading
.
TinyOS programs are composed into
event handlers

and
tasks

with run to
completion
-
semantics. When an external event occurs, su
ch as an incoming
data packet or a sensor reading, TinyOS calls the appropriate event handler
to handle the event. Event handlers can post tasks that are scheduled by the
TinyOS kernel some time later. Both the TinyOS system and programs
written for TinyOS

are written in a special programming language called
nesC

which is an extension to the
C pro
gramming language
. NesC is
designed to detect
race conditions

between tasks and event handlers.

There are also operating systems that allow programming in C. Examples of
such
operating systems include
Contiki
, MANTIS
,

BTnut, SOS and
Nano
-
RK
.
Contiki

is designed to support loading modules over the network and
supports run
-
time loading of standard
ELF

files. The Contiki kernel

is event
-
driven, like TinyOS, but the system supports multithreading on a per
-
application basis. Furthermore, Contiki includes
protothreads

that provide a
thread
-
like programming
abstraction but with a very small memory overhead
Unlike the event
-
driven Contiki kernel, the MANTIS and Nano
-
RK kernels
are based on preemptive multithreading. With preemptive multithreading,
applications do not need to explicitly yield the microprocessor

to other
processes. Instead, the kernel divides the time between the active processes
and decides which process that currently can be run which makes application
programming easier.
Nano
-
RK

is a real
-
time resource kernel that allows fine
grained control of the way tasks get access to CPU time, networking and
sensors. Like TinyOS and Contiki, SOS is an event
-
driven operating system.


The prime feature of SOS is its support for loadable module
s. A complete
system is built from smaller modules, possibly at run
-
time. To support the
inherent dynamism in its module interface, SOS also focuses on support for

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dynamic memory management.
BTnut

is based on cooperative multi
-
threading and plain C code, and is packaged with a developer kit and
tutorial
.


Middleware

There is considerable research effort currently invested in the design of
middleware

for WSN's. In general approaches can be classified into
distributed database, mobile agents, and event
-
based.


Programming languages

Programming the sensor nodes is difficult

when compared with normal
computer systems. The resource constrained nature of these nodes gives rise
to new programming models although most nodes are currently programmed
in C.



c@t (Computation at a point in space (@) Time)



DCL (Distributed Compositiona
l Language)



galsC



nesC



Protothreads



SNACK



SNAPpy (Python)



SQTL


Java
Sun SPOT


Algorithm

WSNs are composed of a large number of sensor nodes, therefore, an
algorithm for a WSN is implicitly a
distributed al
gorithm
. In WSNs the
scarcest resource is energy, and one of the most energy
-
expensive operations
is data transmission. For this reason, algorithmic research in WSN mostly
focuses on the study and design
of
energy aware

algorithms for data
transmission fr
om the sensor nodes to the base stations. Data transmission is
usually multi
-
hop (from node to node, towards the base stations), due to the

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polynomial growth in the energy
-
cost of radio transmission with respect to
the transmission distance.

The algorithmi
c approach to WSN differentiates itself from
the
protocol

approach by the fact that the mathematical models used are more abstract,
more general, but sometimes less realistic than the models used for protocol
design.

Simulators

There are platforms specific
ally designed to simulate Wireless Sensor
Networks, like
TOSSIM
, which is a part of
TinyOS
. Traditional network
simulators like
ns
-
2

have also been used. A platform independent component
based simulator with wireless sensor network framework,
J
-
Sim
(www.j
-
sim.org) can also be used. An extensive list o
f simulation tools for Wireless
Sensor Networks can be found at the
CRUISE WSN Simulation Tool
Knowledgebase

several techniques to retrieve data from the nodes ,some of the protocols rely
on flooding mechanisms , other map the data to nodes by applying the
concept of DHT
.

Visual sensor network

A
visual sensor network

is

a network of spatially distributed
smart camera

devices capable of processing and fusing images of a scene from a variety of
viewpoints into some form more useful than the individ
ual images. A visual
sensor network may be a type of
wireless sensor network
, and much of the
theory and application of the latter applies to the former. The
network
generally consists of the cameras themselves, which have some local
image
processing
, communication and storage capabilities, and possibly one or
more central compu
ters, where image data from multiple cameras is further
processed and
fused

(this processing may, however, simply take place in a
distributed fashion across the cameras and their

local controllers). Visual
sensor networks also provide some high
-
level services to the user so that the
large amount of data can be distilled into information of interest using
specific queries.

The primary difference between visual sensor networks and
other types of
sensor networks is the nature and volume of information the individual
sensors acquire: unlike most
sensors
, cameras are directional in their
field of

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view
, and they capture a large amount of visual information which may be
partially processed independently of data from other cameras in the network.
Alternatively, one may say that while most sensors mea
sure some value such
as temperature or pressure, visual sensors measure

patterns
.

In light of this,
communication in visual sensor networks differs substantially from
traditional sensor networks.



Data visualization

The data gathered from wireless sensor
networks is usually saved in the
form of numerical data in a central base station. Additionally, the Open
Geospatial Consortium (OGC) is specifying standards for interoperability
interfaces and metadata encodings that enable real time integration of
hetero
geneous sensor webs into the Internet, allowing any individual to
monitor or control Wireless Sensor Networks through a Web Browser. There
are several techniques to retrieve data from the nodes ,some of the protocols
rely on flooding mechanisms , other map

the data to nodes by applying the
concept of DHT

Mesh networking

Mesh networking

is a way to route data, voice and instructions between
nodes
. It allows for continuo
us connections and reconfiguration around
broken or blocked paths by “hopping” from node to node until the
destination is reached. A mesh network whose nodes are all connected to
each other is a
fully connected network
. Mesh networks differ from other
networks in that the component parts can all connect to each other via
multiple hops, and they generally are not mobile. Mesh networks can be
seen as one type of
ad hoc network.
Mobile ad hoc networks

(MANET) and
mesh networks are therefore closely related, but MANET also have to deal
with the problems introduced by the mo
bility of the nodes.

Mesh networks are self
-
healing: the network can still operate even when a
node breaks down or a connection goes bad. As a result, this network is very
reliable. This concept is applicable to wireless networks, wired networks,
and softw
are interaction. The animation at right illustrates how wireless

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mesh networks can self form and self heal. For more animations see
His
tory
of Wireless Mesh Networking

Wireless mesh networks

is the most topical application of mesh
architectures. Wireless mesh was originally developed for milit
ary
applications but have undergone significant evolution in the past decade. As
the cost of radios plummeted, single radio products evolved to support more
radios per mesh node with the additional radios providing specific functions
-

such as client access
, backhaul service or scanning radios for high speed
handover in mobility applications. The mesh node design also became more
modular
-

one box could support multiple radio cards
-

each operating at a
different frequency.



M
esh network applications



Wireless mesh network



Distinct radio node deployments of Wireless Mesh Networking



BioWeb



Wireless ad hoc netwo
rk



Wireless community network



Mobile ad hoc network

(MANE
T)



Vehicular ad
-
hoc network



Dust Networks

Dust Networks, Inc

is a
Silicon Valley
-
based company specializing in t
he
design and manufacture of
wireless sensor networks

for industrial
applications including process monitoring, condition monitoring, asset
management, Envi
ronmental, Health and Safety (EH & S) monitoring and
energy management.


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Dust Networks works with industry and standards groups such as the
HART
Communications Foundation

help drive the adoption of interoperable
wireless sensor networking products.

Smart Dust

The Smart Dust project attempted to demonstrate that a complete
sensor/communicati
on system can be integrated into a cubic millimeter
package, which involved both advances in miniaturization, integration, and
energy management. This work was independent of any sensor and looked at
both commercial and military applications including:



Def
ense
-
related sensor networks such as battlefield surveillance,
treaty monitoring, transportation monitoring, scud hunting, ...



Virtual keyboard: Glue a dust mote on each of your fingernails.
Accelerometers will sense the orientation and motion of each of y
our
fingertips, and talk to the computer in your watch.



Inventory Control: The carton talks to the box, the box talks to the
palette, the palette talks to the truck, and the truck talks to the
warehouse, and the truck and the warehouse talk to the internet
.
(evolved into RFID)



Product quality monitoring: temperature, humidity monitoring of
meat, produce, dairy products



Impact, vibration, temp monitoring of consumer electronics failure
analysis and diagnostic information, e.g. monitoring vibration of
bearing
s for frequency signatures indicating imminent failure



Smart office spaces



Interfaces for the Disabled

The project received much attention from the press as it all seemed to border
on science fiction. Dust Networks eventually evolved into a company
providi
ng commercial applications for industrial monitoring and control.

Sensor Web


The
Sensor Web

is a type of sensor network or
geographic information
system

(GIS) that is especially well suited for
environmental monitoring

and
control. The term des
cribes a specific type of
sensor network
: an
amorphous network

of spatially distributed
sensor

platforms (
pods
)
that
wirelessly communicate with each other. This amorphous architecture is

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unique since it is both synchronous and
router
-
free, making it distinct
more
typical
TCP/IP
-
like network schemes. The architecture allows every pod to
know what is going on with every other pod throughout the Sensor Web at
each measurement cycle.




SNM
-

The Sensor Network Museum













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CONCLUSION



In
conclusion of wireless sensor
networks present fascinating chalenges
for
the application of distributed signal processing and distributed control.
These systems will challenge us to apply appropriate techniques and metrics
in light of the technology opportunities (cheap processing and sensing
nodes) and challenges
.



































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BIBLIOGRAPHY


1.www.webop
edia.com

2.www.wikipedia.com

3.www.encyclopedia.com





































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S.NO

CONTENTS

PAGENO



1.

2.

3.

4.

5.

6.

7.

8.

9.

10.


Introduction

Applications

Characteristics

Visual Sensor Networ
ks

Data Visualizations

Mesh Network

D
ust

Networks

Sensor Node

Conclusion

Bibliography






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