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1

CHAPTER1



WIRELESS SENSOR NETWORKS



1.1.

INTRODUCTION



The concept of sensing physical phenomena has been inspired from
biological living

creatures. Sensors have been in
existence for few decades
now and are being used in

everyday life.
Like any sentient organism, the
smart environment relies first and fore most on sensory
d
ata

from the real
world.


The study of wireless sensor networks is challenging

in that it
requires an enormous breadth of knowledge from an enormous variety of
disciplines. In this chapter outline communication networks, wireless sensor
networks and smart sensors, physical transduction principles, commercially
available wireless sen
sor systems, self
-
organization, signal processing and
decision
-
making, and finally some concepts for home automation
.



The design issues in
[1]

wireless sensor network are

the following



Fau
lt tolerance.



S
calability.



P
roduction costs.



O
pera
ting environment.



S
ensor network topology.



H
ardware constraints.



Transmission media.



P
ower consumption.


2

Power consumption is the most important design issues in the wireless
sensor networks.

The power consu
mption of each node in an

wireless

sensor network
can be divided according to functionality into: (i) the power
utilized for the transmission of a message; (ii) the power utilized for the
reception of a message; and (iii) the power utilized while the system is idle.

This suggests two comp
lementary levels at which power consumption can
be optimized: (i) minimizing power consumption during the idle time and
(ii) minimizing power consumption during communication.




The architecture of Wireless sensor networks is given below






Fig 1.1 Architecture of WSN


3

1.2. CHARACTERISTICS

Unique characteristics of a WSN are:



Small
-
scale sensor nodes



Limited power they can harvest or store



Harsh environmental conditions



Node failures



Mobility of nodes



Dynamic
network topology



Communication failures



Heterogeneity of nodes



Large scale of deployment



Unattended operation












4

1.3. APPLICATIONS


The applications for WSNs are many and varied. They are used in
commercial and Industrial applications to monitor data that would be
difficult or expensive to monitor remain for many years (monitoring some
environmental variables) without the need

to recharge/replace their power
supplies. They could form a perimeter about a property and monitor the
progression of intruders (passing information from one node to the next).

Typical applications of WSNs include monitoring, tracking, and
controlling. So
me of the specific applications are habitat monitoring, object
tracking, nuclear reactor controlling, fire detection, traffic monitoring, etc. In
a typical application, a WSN is scattered in a region where it is meant to
collect data through its sensor nod
es.



Environmental monitoring
.



Habitat monitoring
.



Acoustic detection
.



Military surveillance
.



Medical monitoring
.



Smart spaces
.



Process Monitoring
.



Structural health monitoring
.



Health Monitoring
.




5

CHAPTER2



ABOUT SENSOR NODE



A general definition of a
sensor
is “a device that produces measurable
response to a change in a ph
ysical or chemical condition” [2
]. More
specifically, a sensor is "a device that responds
to a stimulus, such as heat,
light, or pressure, and generates a signal that can be measured or
interpreted"
.




T
he Sensor Network community often (but not always) defines a
sensor node as a small, wireless device, capable of responding to one or
several s
timuli, processing the data and transmitting the information over a
short distance using a radio link. Sensor nodes employ electronic circuits
th
at minimize power consumption [1
]. Typically sensors are thought of as
measuring light, sound and temperature.
However, sensors can measure
other variables, such as electro
magnetic fields or vibrations .

Sensor transmit
values wirele
ssly to one or several sinks [1
].




A
Sensor Network
is a wireless, ad hoc network, made of a large
number (hundreds or thousands) of
nodes, whose positions occur randomly.
The OSI model and the classic layered view of communication networks
may or may not apply directly to sensor networks. Other models of sensor
network communications include a protocol stack model that includes
physic
al, medium access control, network, transport and application layers
as well as power management, mobility management and
task management
planes [1]
However, no model is used universally.


6


2.1. SENSOR NODE


A
sensor node
, also known as a mote
(chiefly in North America), is a
node in a wireless Sensor network that is capable of performing some
processing, gathering sensory information and communicating with other
connected nodes in the network. The typical architecture of the sensor node
is show
n in figure 2.
1






Fig
-
2.1
Components of a Sensor Node

The main components of a sensor node as seen from the figure
2.1

are
microcontroller, transceiver, external memory, power source and one or
more sensors.





7

2.2. COMPONENTS

OF A SENSOR

NODE


2.2.1. Microcontroller


Microcontroller performs tasks, processes data and controls the
functionality of other components in the sensor node. Other alternatives that
can be used as a controller are: General purpose desktop micr
oprocessor,
Digital signal processors, Field Programmable Gate Array (FPGA) and
Application
-
specific integrated circuit (ASIC). Microcontrollers are most
suitable choice for
sensor node
. Each of the four choices has their own
advantages and disadvantages.

Microcontrollers are the best choices for embedded systems. Because
of their flexibility to connect to other devices, programmable, power
consumption is less, as these devices can go to sleep state and part of
controller can be active. In general purpose
microprocessor the power
consumption is more than the micro controller; therefore it is not a suitable
choice for sensor node. Digital Signal Processors are appropriate for
broadband wireless communication. But in Wireless Sensor Networks, the
wireless com
munication should be modest i.e., simpler, easier to process
modulation and signal processing tasks of actual sensing of data is less
complicated. Therefore the advantages of DSP’s are not that much of
importance to wireless sensor node.


Field Programmabl
e Gate Arrays can be reprogrammed and
reconfigured according to requirements, but it takes time and energy.
Therefore FPGA's is not advisable. Application Specific Integrated Circuits
are specialized processors designed for a given application. ASIC's prov
ided

8

the functionality in the form of hardware, but microcontrollers provide it
through software.

2.2.2. Transceiver


Sensor nodes make use of ISM band which gives free radio, huge
spectrum allocation and global availability. The various choices of
wireless
transmission media are radiofrequency, Optical communication (Laser) and
Infrared. Laser requires less energy, but needs line of sight for
communication and also sensitive to atmospheric conditions. Infra

red like
laser, needs no antenna but is li
mited in its broadcasting capacity. Radio
Frequency (RF) based communication is the most relevant that fits to most
of the WSN applications. WSN’s use the communication frequencies
between about 433 MHz and 2.4 GHz. The functionality of both transmitter
an
d receiver are combined into a single device know as transceivers are used
in sensor nodes. Transceivers lack unique identifier. The operational states
are Transmit, Receive, Idle and Sleep. Current generation radios have a
built
-
in state machine that perf
orms this operation automatically.


Radios used in transceivers operate in four different modes: Transmit
Receive, Idle, and Sleep. Radios operating in Idle mode results in power
consumption, almost equal to power consumed in Receive mode. Thus it is
bette
r to completely shutdown the radios rather than in the Idle mode when it
is not Transmitting or Receiving. And also significant amount of power is
consumed when switching from Sleep mode to Transmit mode to transmit a
packet.


9

2.2.3. External Memory


F
rom an energy perspective, the most relevant kinds of memory are on
-
chip memory of a microcontroller and FLASH memory
-

off
-
chip RAM is
rarely if ever used. Flash memories are used due to its cost and storage
capacity. Memory requirements are very much app
lication dependent. Two
categories of memory based on the purpose of storage (User memory used
for storing application related or personal data) Program memory used for
programming the device. This memory also contains identification data of
the device if
any.

2.2.4. Power Source


Power consumption in the sensor node is for the Sensing,
Communication and Data Processing. More energy is required for data
communication in sensor node. Energy expenditure is less for sensing and
data processing. The en
ergy cost of transmitting 1 Kibitz a distance of 100 m
is approximately the same as that for the executing 3 million instructions by
100 million instructions per second/W processor. Power is stored either in
Batteries or Capacitors. Batteries are the main
source of power supply for
sensor nodes. Namely two types of batteries used are chargeable and non
-
rechargeable. They are a
lso cadmium
,

NiZn(nickel
-
zinc), Nimh (nickel
metal hydride), and Lithium
-
Ion. Current sensors are developed which are
able to renew their energy from solar or vibration energy. Two major power
saving policies used are Dynamic Power Management (DPM) and Dynamic
Voltage
Scaling (DVS).


10

2.2.5. Sensors


Sensors are hardware devices that produce measurable response to a
change in a physical condition like temperature and pressure. Sensors sense
or measure physical data of the area to be monitored. The continual an
alog
signal sensed by the sensors is digitized by Analog
-
to
-
Digital converter and
sent to controllers for further processing. Characteristics and requirements of
Sensor node should be small size, consume extremely low energy, operate in
high volumetric den
sities, are autonomous and operate unattended, and be
adaptive to the environment. As wireless sensor nodes are micro
-
electronic
sensor device, can only be equipped with a limited power source of less than
0.5 Ah and 1.2 V.

Sensors are classified into thr
ee categories.



Passive, Omni Directional Sensors: Passive sensors sense the data
without actually manipulating the environment by active probing. They
are self powered i.e., energy is needed only to amplify their analog
signal. There is no notion of
“direction” involved in these measurements.



Passive, narrow
-
beam sensors: These sensors are passive but they have
well
-
defined notion of direction of measurement. Typical example is
‘camera’.



Active Sensors: These groups of sensors actively probe the envir
onment,
for example, a sonar or radar sensor or some type of seismic sensor,
which generate shock waves by small explosions.

The
overall

theoretical work on WSN’s considers Passive, Omni directional
sensors each sensor node has a certain area of coverage f
or which it can

11

reliably and accurately report the particular quantity that it is observing.
Several sources of power consumption in sensors are



Signal sampling and conversion of physical signals to electrical ones,



Signal conditioning
.



12

CHAPTER3




EXISTING SYST
EM


The
max

min zPmin
algorithm requires information about the power level
of each computer

in the
network. Having accurate knowledge of this
information is not a problem in small networks.

However, for large networks
it is difficult to aggregate and maintain this
information.

This

makes it hard
to implement the
max

min zPmin
algorithm for large network
s.



Another

online algorithm called
zone
-
based
routing

[
3]

that relies on
max

min zPmin
and is scalable.

The

experiments show that the
performance of zone
-
based routing is very close to the performance of
max

min

zPmin
with respect to optimizing the lifetime of the network.



Zone
-
based routing is a hierarchical approach in which the area
covered by the (sensor) network is divided into a small number of zones.
Each zone has many nodes and thus there is a lot
of redundancy in routing a
message through it. To send a message across the entire area we find a
‘global’ path from zone to zone and give each zone control over how to
route the message within itself. Thus, zone
-
based power
-
aware routing
consists of (i) a
n algorithm for estimating the power level of each zone; (ii)
an algorithm for computing a path for each message across zones; and (iii)
an algorithm for computing the best path for the message within each zone
(with respect to

the power lifetime of the zo
ne
)
.



13


To improve the scalability of routing algorithms for large networks,
many hierarchical routing m
ethods are proposed.

These zones
, which are the
route maintenance units, are used t
o find the routes.
This
work

focused on
how to find the correct route efficiently, but did not consider optimizing
power while sending messages.



These above said algorithms

are

useful only

if the nodes are in
thousands. But the

proposed swarm intelligent technique is u
sed where the
nodes are in some millions.




















14

CHAPTER 4

PROPOSED SYSTEM



4
.1 About Swarm Intelligence


4
.1.1

Swarms and Emergence


Swarm Intelligence
(SI) [
4
] is an artificial intelligence technique based
around on the study of collective behavior in decentralized, self
-
organized
systems. The expression "swarm intelligence" was introduced by Beni &
Wang in 1989, in the context of cellular robotic systems.

SI
systems are typically made up of a population of simple agents interacting
locally with one another and also with their environment. Usually there is no
centralized control structure dictating how the individual agents should
behave, but local interactions

between such agents often lead to the
emergence of a global behavior. Examples of systems like this can be found
in nature, including ant colonies, bird flocking, bee swarming, animal
herding, bacteria molding and fish schooling.


4
.1.2 Swarm Intelli
gence as an optimization method


Two of the most successful swarm intelligence techniques [
5
]
currently in existence are
Ant Colony Optimization
(ACO) and
Particle
Swarm Optimization
(PSO). ACO is a meta
-
heuristic optimization
algorithm that can be used to find approximate solutions to difficult
combinatorial optimization problems. In ACO artificial ants build solutions
by moving on the problem graph and they, mimicking real ants,

15

dep
osit artificial
pheromone
on the graph in such a way that future artificial
ants can build better solutions. ACO has been applied successfully to an
impressive number of optimization problems. PSO is a global minimization
technique for dealing with problem
s in which a best solution can be
represented as a point or surface in an n
-
dimensional space.


Fig 4.1 Swarm technique


4
.1.3 Use of Ant Colony Optimization technique


ACO is the primary optimization algorithm being used here [
5
]. The
reason behind
choosing ACO is its distributed nature and inherent
randomness. The algorithm used for optimization is a purely distributed
algorithm. This holds serious implication
s for WSN. Since in a WSN

the
nodes are constrained by power, storage and processing power
limitations, a
purely distributed algorithm like ACO prevents per node computation load,
reduces state information to be stored per node. The randomness built in the
algorithm prevents convergence to local maxima/minima but has a tendency
to search for glo
bal optimization of parameters.




16

4.2 ALGORITHM:

4.2.1 POWER ANT DESCRIPTION:


Initially each node will initiate ants to

discover the network. An
Ant is a packet constructed

and sent by one of the nodes trying to gather


information
about the conditions of nodes in the network in its forward
journey, refer to figure 4.2 for the structure of an ant.




Fig 4.2 Format of forward ant




A

power metric is embedded in the routing
information. Ants
gather information about the power residuals of the nodes that they visit;
such information is used to update the routing table of the sending node so
that it can be used to

[
6
]

judge better routes based on the power residuals and
delay e
xperienced. Data pieces gathered about delay and power are stored in
the pheromone table, which has values computed based on the power and
delay experienced by the packets in the receiving intermediate nodes.



The pheromone table contains a measur
e of desirability for the packets
heading towards a certain destination follow a certain path based on the
values collected by the previous ants. Such values are considered the
pheromone that will be followed by packets in order to reach a destination.
The
se values or pheromone are computed based on the power and the delays
that were observed by the forward collecting ants.



17



The information collected by the ants
is used

to update the pheromone
table entries after receiving a backward ant. After updat
ing
the pheromone

table using a backward ant the routing table (the destination list) is built in
order to be used for data routing.




A forward ant sent by a node reaches only nodes that are in the
transmission range and on. Therefore, nodes
that are out of the range or are
shutdown do not receive the forward ant. A backward ant follows its way
back to the source that initiated it. If a node lying on the path between the
source and the destination of the ant moved out of

the
communication rang
e
or was shutdown, the backward ant is dropped and processing for the ant is
stopped. Otherwise, the ant is forwarded for the next

h
op .



















18

4.2.2
PROCESSING OF ANTS
:

PROCESSING OF FORWARD ANTS:


The processing of the ants



Fig 4.
3

Flow chart showing initiation of forward ant














19

PHEROMONE TABLE:


Pheromone

(Hormone) is a

chemical produced

by the organisms to attract
the other organisms in the same family in order to follow the former one.
The pheromone table is a two dimensional array of a maximum size (N
-
1) *
(N
-
1), where N is the
number of the nodes in the WSN
. Every node in the
network

has a pheromone table that records information about the state of
the nodes in the network.







20

PROCESSING OF BACKWARD ANTS:



After the ant reaches the requested destination it is converted to a
backward ant. The forward bit of the packet is set

to “0” then the packet is
unicasted to the node where it came from to the destination. At each
intermediate node on the way back, the backward ant is used to update the
pheromone table
.

When a backward ant arrives at
node it is processed in the
following
steps.

1
-

An entry is popped from the stack. Then this

entry is used to update the
pheromone table
.

2
-

An entry in the pheromone table is updated

as follows


Pheromone[D][I].PheromoneValue


The entry describes the desirability of the packets sent t
o destination D
to be forwarded through the intermediate node I. The value of the entry is a
function that is directly related with power and inversely related to delay.
The value of the "PhermoneValue" computed for an entry satisfies the
relation

0 ≤
Pheromone[D][I].PheromoneValue ≤ 1

where D≠I

For entries where D=I,

the entry is never used and is
disabled. This means
that there is a direct channel

between the source and the destination that can
be

used without the need to cross intermediate nodes.

3
-
T
he ant is sent as a unicast through the reverse path of the forward ant.

4
-

Steps 1 through 3 are repeated until the backward ant reaches the source
requesting the route. The backward ant is repeatedly forwarded by the
intermediate nodes as a unicast follo
wing the reverse path from the
destination D, until it reaches the source S.


21

5
-

If the source S or one of the intermediate nodes has moved or shutdown
the ant is immediately dropped and processing stops.



This swarm provides us with values of t
he

a.

Number of hops

b.

Congestion window

c.

Round trip time.

These above said values are given as input to the fuzzifier block in fuzzy
control logic.

4.3 FUZZY CONTROL LOGIC:


Fuzzification



Fuzzification is defined as

the mapping from a real
-
valued
point to a
fuzzy set

[7]
. The FLC system

in this paper receives

other robots’

information as the

inputs of the FLC and

the fuzzification

module converts

inputs into fuzzy

linguistic variable

inputs.




Fig 4.4

Fuzzy logic c
ontroller


Fuzzy inference engine.


Fuzzy inference engine is used to generate fuzzy outputs by evaluating

the
fuzzy rules with the fuzzy inputs. The fuzzy rules may use an expert’s
experience and control

knowledge. These fuzzy rules are described in
IFTHEN form and use th
e above linguistic variable.T
he fuzzy inference

22

engine need convert the input information into robot’s next movement

direction.




P
-
Battery power


E
-
Events detected in the region.


Delay query
-
delaying query fro
m BS to event region.


Decrease nodes
-
putting nodes to sleep.


P

E

d_q

D_n

Low

High

High

Zero

High

High

Low

Zero

Low

Low

Medium

Zero

High

Low

Zero

High


Table

4.1

Fuzzy Rules

Defuzzification.



The output of the fuzzy inference engine is a

fuzzy set. FLC module, the
cente
r of gravity method

is used to get a crisp output to control the robot’s
next moving direction. The robot’s swarm behavior controller uses other
robots current location to find out all expansion cells the robot has and use
s
the moving direction generated by the FLC to define the expansion cell
located on the path of the moving direction as the robot’s optimal
deployment location.


Our simulation explains the result of the power aware routing with the
help of power
metric
s [
8]

combined with the existing system.




23


CHAPTER5




IMPLEMENTATION ASPECTS


5.1. SYSTEM REQUIEMENTS



1. Hardware Configuration



Intel Pentium IV processor



256 MBRAM



512 Kb cache



40 GB hard disk



3.5”floppy drive



1
7”SVGA monitor



VGA CARD



TVS 104 internet keyboard



Logitech mouse




2. Software Configuration




Linux



Ns
-
2.29


5.2. OVERVIEW OF NS
-
2 SIMULATION TEST BED


NS
-
2 is n event driven packet level network simulator developed as a
part of the VINT project
(Virtual Internet Test bed).Version 1 of NS was
developed in 1995 and with version 2 in 1996. Version 2 included a
scripting language called Object oriented Tel (OTcl).It is an open source
software package available for both Windows 32 and Linux platforms.

NS
-
2
has many and expanding uses included.




24




To evaluate that performance of existing network protocols



To evaluate new network protocols before use.



To run large scale experiments not possible in real experiments



To simulate a variety of ip networks.


5.2.1 Characteristics of NS
-
2




NS
-
2 implementation the following features



Multicasting



Simulation of wireless networks



Terrestrial (cellular, Adhoc, GPRS, WLAN, BLUETOOTH), satellite



IEEE 802.11 can be simulated, Mobile IP and Ad hoc protocols

such
as DS
R,

TORA, DSDV and ADOV




Routing



5.2.2 Software Tools used with NS
-
2


In the simulation, there are the two tools are used.



NAM(Network Animator)



XGraph








25


5.2.2.1. NAM (Network Animator)


NAM provides a visual interpretation of the network topology created.
The application was developed as part of the VINT project. Its feature is as
follows.



Provides a visual interpretation of the network
created



Can be executed directly from a Tcl script



Controls include play; stop fast forward, rewind, pause, a display
speed controller button and a packet monitor facility.



Presented information such as throughput, number packets on each
link


5.2.2.2 X Gr
aph


XGraph is an X
-
Window application that includes:



Interactive plotting and graphing



Animated and derivatives


To use XGraph in NS
-
2 the executable can be called within a TCL
script. This will then load a graph displaying the information visually
disp
laying the information of the file produced from the simulation. The
output is a graph of size 800 x 400 displaying information on the traffic flow
and time.




26

5. 3. SOFTWARE PROFILE:


5.3.1. Tcl/TK


Tcl quickly gained wide acceptance on its own and is
generally
thought to be easy to learn, but powerful in competent hands. It is most
commonly used for rapid prototyping, scripted applications, GUIs and
testing. Tcl is used extensively on embedded systems platforms, both in its
full form and in several oth
er small
-
foot printed versions. Tcl is also used for
CGI scripting and as the scripting language for eggdrop bots.


Tcl was originally developed as a reusable command language for
experimental computer aided design (CAD) tools. The interpreter was
implemen
ted as a C library that could be linked into any application. It is
very easy to add new functions to the Tcl interpreter, so it is an ideal
reusable "macro language" that can be integrated into many applications.


Tcl programs are usually called "scripts"

because the programs do not
need to be compiled into a binary form before running them, but nowadays
the interpreter internally compiles procedure bodies the first time they are
called, so Tcl does not suffer from the performance penalty associated with
t
raditional interpreters. Tcl does however still enjoy the advantage of being a
dynamic language, where for example commands can be created, redefined,
and destroyed at execution time.



Event
-
driven interface to sockets and files. Time based and user
defin
ed events are also possible. Flexible scope, with variable visibility

27

restricted to lexical (static) scope by default, but
up level

and upvar allowing
procs to interact with the enclosing functions scopes. Simple exception
handling using exception code ret
urned by all command executions. All
commands defined by Tcl itself generate informative error messages on
incorrect usage. Readily extensible, via C, C++, Java, and Tcl. Interpreted
language using bytecode for improved speed whilst maintaining dynamic
mod
ifiability Full Unicode (3.1) support, first released 1999. Platform
independent: Win32, UNIX, Linux, Mac, etc. Close integration with
windowing (GUI) interface Tk. Easy to maintain code. Tcl scripts are often
more compact and readable than functionally eq
uivalent code in other
languages. Can be used for many purposes, and in many environments: as a
text
-
only scripted language, as a GUI
-
capable language for applications, as
an embedded language in: web pages (server
-
side; or client
-
side, as Tclets),
and dat
abases (server
-
side, in PostgreSQL). Exists as development version
(e. g. Active State Tcl), as tclkit (kind of runtime version, only about 1
megabyte in size), as starpack (single
-
file executable of a script/program), as
BSD licensed freely distributable
source
.

Tcl did not originally support object oriented syntax, being a
functional language, but recent versions do support extensions which
provide OO functionality, such as the XOTcl extension to Tcl. Other OO
extensions also exist, such as incr Tcl, Sni
t, and (simple tcl
-
only object
-
oriented programming).

Functional programming can easily be done in Tcl, as higher
-
order
functions or functional abstractions are built into the language, though it is
not widely used for this purpose.




28





























29

REFERENCES:



1)

I.F. Akyildiz
, W
. Su,Y. Sankarasubramaniam and E. Cayirci. "A
Survey on Sensor Networks", IEEE Communications Magazine,
August 2002.

2)


J. Kabara, Class no
tes from TELECOM 2721, Wireless
Data
Networks. Lecture number 5,
June

2005.

3)


Javed

Aslam, Qun Li
, and Daniela Rus
,Three power
-
aware routing
algorithms for sensor networks, Department

of Computer Science
wireless communications and mobile computing
.
2003

4)

Swarm Intelligence: From Natural to Artificial Systems (Santa Fe
Institute Studies o
n the Sciences of Complexity)


5)


Internet Resource: Ant Colony Optimization and Swarm Intelligence:
4th International Workshop, ANTS 2004, Brussels, Belgium,
September 5
-
8, 2004, Proceeding

6)

Swarm Congestion & Power Aware Routing Protocol for MANETs

Ahmed S.

Nagy, Amr A. El
-
Kadi, Senior Member, IEEE, and Mikhail
N. Communication Networks and Services Research Conference ©
2008 IEEE

7)


E. Cox. The Fuzzy Systems Handbook, Second edition, 1998

8)

International Journal of Computers and Applications, power
-
aware
metri
cs for wireless sensor network A. Salhieh and L. Schwiebert

Vol.
26, No. 4, 2004


30