Artificial Intelligence Algorithms

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Oct 29, 2013 (3 years and 9 months ago)

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IOSR Journal Of Computer Engineering (IOSRJCE)

ISSN: 2278
-
0661, ISBN: 2278
-
8727

Volume
6
, Issue
3

(Sep
-
Oct. 2012), PP
0
1
-
0
8

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A
rtificial
I
ntelligence

Algorithms


Sreekanth Reddy Kallem

Department of computer science, AMR Institute of Technology, Adilabad,
JNTU,Hyderabad,

A.P, India
.


Abstract
-
Artificial intelligence (AI
) is the study of

how to make computers do things which, at the moment,
people do better
.

Thus Strong AI claims that in near future we will be surrounded by such kinds of machine
which can completely works like human being and machine could have human level intelligence
.

O
ne intention
of this article is to excite a broader AI audience about abstract algorithmic information theory concepts, and
conversely to inform theorists about exciting applications to AI
.The science of

Artificial Intelligence (AI)

might
be defined as the

construction of intelligent systems and their analysis.

Keywords
:

Artificial

Intelligence,

Algorithms


I.

Introduction


Artificial intelligence

(AI) is the

intelligence

of machines and

the branch of

computer science

that aims
to create it. AI textbooks define the field as "the study and design of intelligent agents"
[1]
where an

intelligent
agent

is a
system
that perceives its environment and takes actions that maximize its cha
nces of success.
[2]

John
McCarthy
,
who coined the term in 1955,
[3]

defines it as "the science and engineering of making intelligent
machines."
[4]


AI research is highly technical and specialized, deeply divided into subfields that often fail to
communicate with each other.
[5]

Some of the division is due to social and cultural factors: subfields have grown
up around particular institutions and the work of individual researchers. AI research is also divided by several
technical issues. There are subfields which are focussed on the solution of specific

problems
, on one of several
possible

approaches
, on the use of widely differing

tools

and towards the accomplishment of
particular

applications
. The central problems of AI include such traits as reasoning,knowledge, planning,
learning, communication, perception and the ability to move and manipulate objects.
[6]

General intelligence (or
"
strong AI
") is still among the field's long term goals.
[7]

Currently popular approaches include

statistical
methods
,

computational intelligence

and

traditional symbolic AI
. There are an enormous number of tools used
in AI, including versions of

search and mathematical optimization
,

logic
,

methods based on probability and
economics
, and many others.


The field was founded on the claim that a central property of humans, intelligence

the

sapience

of

Homo sapiens

can be so precisely described that it can be simulated by a machine.
[8]

This
raises philosophical issues about the nature of the

mind

and the ethics of creating artificial beings, issu
es which
have been addressed by

myth
,

fiction

and

philosophy

since antiquity.
[9]

Artific
ial intelligence has been the
subject of optimism,
[10]

but has also suffered

setbacks
[11]

and, today, has become an essential part of the
technology industry, providing the heavy lifting for many of the most difficult problems in computer science.
[12]


1.1
. Applications

of AI
:

1.1.
1.
R&D Plan for Army Applications of AI/Robotics
:

Robotic Reconnaissance Vehicle with Terrain Analysis,



Automated Ammunition Supply Point (ASP),



Intelligent Integrated Vehicle Electronics,



AI
-
Based Maintenance Tutor,



AI
-
Based Medical System Development.


1.1.
2.
Expert system:

An expert system, is an interactive computer
-
based decision tool that use both facts and heuristics to solve
difficult
decision making problems based on knowledge acquired from an expert.



An expert system compared with traditional computer:

Inference engine + Knowledge = expert system

(Algorithm + data structures = program in traditional computer)

1.1.
3. Fuzzy Logic:

Fuzzy

logic is more than thirty years old and has a long
-
lasting misunderstanding with artificial Intelligence,
although the formalization of some forms of commonsense reasoning has motivated the development of fuzzy
logic.

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1.1.
4
. Mobile

Robotics and Games (
Path Planning)
:

Mobile robots often have to replan quickly as the world ortheir knowledge of it changes. Examples
include both
physical robots

and computer
-
controlled robots (or, more
generally, computer
-
controlled
characters) in computer
games. Efficient

replanting

is especially important for
computer games

since they often
simulate a large number of
characters and

their other software components, such as
the graphics

generation,
already place a high demand on
the processor
. In the following, we discuss tw
o cases where
the knowledge

of a
robot changes because its sensors
acquire more

information about the initially unknown terrain as
it moves

around.


II.

Genetic Algorithm

This article describes how to solve a logic problem using a Genetic Algorithm. It assumes

no prior
knowledge of GAs.
A genetic algorithm is a search technique used in computing, to find true or approximate
solutions to optimization and search problems, and is often abbreviated as GA. Genetic

algorithms

are
categorized as global search heuristic
s. Genetic

algorithms

are a particular class of evolutionary

algorithms

that
use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover(also
called recombination).


Genetic

algorithms

are implemented as a compu
ter simulation in which a population of abstract
representations (called chromosomes or the genotype or the genome) of candidate solutions (called individuals,
creatures, or phenotypes) to an optimization problem evolves towards better solutions. Tradition
ally, solutions
are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually
starts from a population of randomly generated individuals, and happens in generations. In each generation, the
fitness of every

individual in the population is evaluated, multiple individuals are stochastically selected from
the current population (based on their fitness), and modified (recombined and possibly mutated) to form a new
population. The new population is then used in t
he next iteration of the algorithm.


Follow that?? If not, let's try a diagram. (Note

that

this is a Microbial GA, there are lots of GA types,
but I just happen to like this one, and it's the one this article uses.)



Fig. 1 Genetic Algorithm

I prefer to
think of a GA as a way of really quickly (well, may be quite slow, depending on the problem) trying
out some evolutionary prog
ramming techniques, that mother
nature has always had.



III.

Path Finding Algorithms/Ai


T
he algorithms

we shall discuss in this tutorial are the decisionbased algorithms as well as the simplest
of the recursive type

algorithms. This is because they are easy to comprehend as well aseasy toimplement. We
will ignore BFS, Dijkstraand A* since these

are ve
ry co
mplex algorithms which may
require special data
structures and

are harder to implement.

Path
-
finding consumes a significant amount of
resources, especially

in moveme
nt
-
intensive games such as
(mas
sively) multiplayer games. We investigate several
path
-
finding techniques, a
nd explore the impact on
perfor
mance of workloads derived from real player
movement sin

a multiplayer game. We find that a map
-
conforming, hierarchical

path
-
finding strategy performs best, and
in combination

with caching optimizat
ions
can greatly re
-
duce path
-
finding cos
t. Performance is dominated pri
marily by algorithm, and only to a lesser
degree byworkload variation. Understanding the real impact
of path
-
finding techniques allows for refined
testing
and optimization

of game desi
gn.


Path
-
finding in computer games is commonly approached as a graph search problem. The world is de
-
composed, abstracted as a graph model, and
searched, typically

using some variant of IDA* (Korf 1985),
basedon the well
-
known A* (Hart et al. 1968) algori
thm. Underlying world decompositions can have a
significantimpact on performance. Common approaches includethose based on uniformly shaped grids, such as
squareor hexagonal tilings (Yap 2002), as well as the use ofquadtrees (Chen et al. 1995; Davis 2000) o
r
variableshaped tiles (Niederberger et al. 2004) for adaptivityto more arbitrary terrain boundaries. Properties
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ofthe decomposition, its regularity, convexity, or Voronoiguarantees, as well as geometric computations, such
asvisibility graphs, or even heur
istic roadmap informa
tion (Kavrakiet al.
1996) can then be used to
improvesearch efficiency.


Hierarchical path
-
finding incorporates multiple graph orsearch
-
sp
ace decompositions of different
granularity asa way of reducing search cost, perhaps with some los
sof optimality. Hierarchical information has
been used toimprove A* heuristics (Holte et al. 1996), and proposedin terms of using more abstract, meta
-
information al
-
ready available in a map, such as doors, rooms, floors,departments (Maio and Rizzi 1994). L
ess
domain
-
specific are graph reduction techniques based on recursively combining nodes into clusters to form a
hierarchical structure (Sturtevant and Buro 2005). Our approachhere is most closely based on the HPA* multi
-
level hierarchical design, where nod
e clustering further considersthe presence of collision
-
free paths between
nodes (Boteaet al. 2004).


Path
-
finding can also be based on the physics of dynamic character interaction. In strategies based on
potential fields (Khatib 1985)
or more complex stee
ring
behaviours (Reynolds 1999; Bayazit et al. 2002) a
character’s path is determined by its reaction to its environment. This reactive approach can be combined
withsearch
-
based models to improve heuristic choices duringsearching (Pottinger 2000). There are

many
possibleheuristics to exploit; in our implementations we use a“diagonal distance” metric to approximate the cost
of unknown

movement (Patel 2003).


3.1
. Heuristic Function
:


A heuristic is a technique that improves the efficiency of search process,
possibly by sac
rificing claims
of completeness. While the almost perfect heuristic is significant for theoretical analysis, it is not common to
find such a heuristic in practice.
heuristics play a major role in search strategies because of exponential natur
e of
the most problems.
Heuristic help to reduce the number of alternatives from an exponentialnumberto polynomial
number.

Heuristic search has been widely used in both deterministic and probabilistic planning. Yet, although
bidirectional heuristic search h
as been applied broadly in deterministic planning problems, its function in the
probabilistic domains is only sparsely studied.

A heuristic function is a function that maps from problem state descriptions to measures of desirability, usually
represented as

number
.

Heuristic functions generally have different errors in different states.

Heuristic functions
play a crucial rule in optimal planning, and the theoretical limitations of algorithms using such functions are
therefore of interest. Much work has focu
sed on finding bounds on the behavior of heuristic search algorithms,
using heuristics with specific attributes.


3.2
.
Depth
-
First Search
:


The first strategy is

depth
-
first search.

In depth
-
first search, the fro
ntier acts like a last
-
in first
out
stack
.

The elements are added to the stack one at a time. The one selected and taken off the frontier at any time
is the last element that was added.

This Algorithm is also called as Recursive Algorithm.


Fig. 2
The order nodes are expanded in depth
-
first search
.



Recursive algorithms are popular because they are the most powerful

and are used in association with
tile based games such as rpgs. The

thing with these set of algorithms is that they require a

TONof computing
power. This is because these algorithms
generally run in

exponential time which to put simply means that for a
small increase

in map size, running time will inc
rease a lot. If you're serious

about making games, i suggest you
learn a high level language such asc++ or java. If you're just here to
fool around then i suggest you

stick to small
map sizes.

In order to design a recursive path finding algorithm, one mustunderstand the concept of recursion.
Basically a recursive functionis a function which calls itself. To illustrate this concept, lets

lo
ok at a simple
function which calculates fibonacci numbers.(fibonacci numbers are a sequence of numbers where each
successivenumber is the sum of the previous two ie. 1, 1, 2, 3, 5, 8, 13)
.

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Code:

Function

fib (int n) {

If

(n == 0) return 0

If

(n == 1) return 1

Return

fib (
n
-
1) + fib(n
-
2)

}


If we look carefully at the code we see that the function fib() callsupon itself to calculate the previous
two numbers. This process keepsrepeating until it either calculates the value of 0 or 1 which wealr
eady know the
value of.

Lets go step by step how this functionworks:

1) If we try to find the 6th element in the sequence we call fib(6)

2) Since n is not equal to 0 or 1, we must calculate the valuerecursively.

3) The value of fib(6) is equal to fib(5)+fi
b(4) which are the two
previous numbers in the series. So we call the
fib function to

calculate these two numbers.

4) This process keeps repeating itself untill it encounters the twopreconditions which return a constant value. If
it weren't for these
precond
itions, the function would keep calling itself untill yourcomputer runs out of RAM!!!


3.3
.

Breadth
-
First Search
:


In

breadth
-
first search

the

frontier is implemented as a FIFO (first
-
in, first
-
out) queue. Thus, the path
that is selected from the frontier

is the one that was added earliest.


This approach implies that the paths from the start node are generated in order of the number of arcs in
the path. One of the paths with the fewest arcs is selected at each stage.


Fig. 3
The order in which nodes are
expanded in breadth
-
first search


Depth
-
first search is appropriate when either



space is restricted;



many solutions exist, perhaps with long path lengths, particularly for the case where nearly all paths lead to
a solution; or



The

order of the neighbors of a node are added to the stack can be tuned so that solutions are found on the
first try.



It is a poor method when



it is possible to get caught in infinite paths; this occurs when the graph is infinite or when there are cycles in
the graph; or



Solutions

exist at shallow depth, because in this case the search may look at many long paths before finding
the short solutions.

Depth
-
first search is the basis for a number of other algorithms, such as



iterative deepening

.

Breadth
-
first search is useful when



space is not a problem;



you want to find the solution containing the fewest arcs;



few solutions may exist, and at least one has a short path length; and



infinite paths may exist, be
cause it explores all of the search space, even with infinite paths.

It is a poor method when all solutions have a long path length or there is some heuristic knowledge available. It
is not used very often because of its space complexity.


3.4
.
A* search
algorithm
:


The A* algorithm combines features of uniform
-
cost search and pure heuristic search to efficiently
compute optimal solutions. A* algorithm is a best
-
first search algorithm in which the cost associated with a
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node is f(n)=g(n)+h(n), where g(n) i
s the cost of the path from the initial state to node n and h(n) is the heuri
stic
estimate or the cost or a path from node n to a goal. Thus, f(n) estimates the lowest total cost of any solution
path going through node n. At each point a node with lowest f

value is chosen for expansion. Ties among nodes
of equal f value should be broken in favour of nodes with lower h values. The algorithm terminates when a goal
is chosen for expansion.


A* algorithm guides an optimal path to a foal if the heuristic functio
n h(n) is admissible, meaning it
never overestimates actual cost. For example, since airline distance never overestimates actual highway
distance, and manhatten dista
nce never overestimates actual moves in the gliding tile.


A* uses a

best
-
first search

and finds a least
-
cost path from a given initial

node

to one

goal node

(out of
one or more possible
goals). As A* traverses the graph, it follows a path of the lowest

known

heuristic cost,
keeping a sorted

priority queue

o
f alternate path segments along the way.

I

t uses a distance
-
plus
-
cost

heuristic

function (usual
ly denoted

) to determine the order in which the
search visits nodes in the tree. The distance
-
plus
-
cost heuristic is a sum of two functions:

the path
-
cost function, which is the cost from the starting node to the current node (usually denoted

)

an

admissible

"heuristic estimate" of the distance to the goal (usually denoted

).

The


part of the


function must be an

admissible heuristic
; that is, it must not overestimate the distance
to the goal. Thus, for an application like

routing
,


might represent the
straight
-
line distance to the goal,
since that is physically the smallest possible distance between any two points or nodes.


If the

heuristic

h

satisfies the additional condition


for ev
ery edge

x, y

of the
graph (where

d

denotes the length of that edge), then

h

is called

monotone, or consistent
. In such a case, A* can
be implemented more efficiently

roughly speaking, no node needs to be processed more than once (see

closed
set

below)

and A* is equivalent to running
Dijkstra's algorithm

with the

reduced
cost

.

Example
:


An example of an A star (A*) algorithm in action where nodes are cities connected with roads and h(x)
is the straight
-
line distance to target point:


Key:

green: start;
blue: goal; orange: visited

Note:

This example uses a comma as the

decimal separator
.


3.4.1.
Complexity
:

The

time complexity

of A* depends on the heuristic. In the worst case, the number of nodes expanded
is

exponential

in the length of the solution (the shortest path), but it is

polynomial

when the search space is a
tree,
there is a single goal s
tate, and the heuristic function

h

meets the following condition:


where


is the optimal heuristic, the exact cost to get from


to the goal. In other words, the error of

h

will not
grow faster than the

logarithm

of the “perfect heuristic”


that returns the true distance from

x

to the goal (see
Pearl 1984
[11]

and also Russell and Norvig 2003, p.

101
[12]
)


The main drawback of A*, and indeed of any best
-
first search,is its memory requirement.Since at list
the entire Open list must b
e saved, A* is severely space
-
limited in practice, and is no morePractical than
breadth
-
first search on current machines. For example, while it can be Run successfully in the Eight Puzzle, it
exhausts available memory in a matter of minutes on the Fifteen
Puzzle.


3.5
.

A Generic Searching Algorithm
:


This section describes a generic algorithm to search for a solution path in a graph. The algorithm is
independent of any particular search strategy and any particular graph.

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Fig.4

Problem solving by graph searching



The intuitive idea behind the generic search algorithm, given a graph, a set of start nodes, and a set of goal
nodes, is to incrementally explore paths from the start nodes. This is done by maintaining a

frontier
(or

fringe
)

of
paths from the start node that have been explored. The frontier contains all of the paths that could form initial
segments of paths from a start node to a goal node. (See

Figure

3.3
, where the frontier is the set of paths to the
gray shaded nodes.) Initially, the frontier contains trivial paths containing no arcs from the start nodes. As the
search procee
ds, the frontier expands into the unexplored nodes until a goal node is encountered. To expand the
frontier, the searcher selects and removes a path from the frontier, extends the path with each arc leaving the last
node, and adds these new paths to the fr
ontier. A search strategy defines which element of the frontier is
selected at each step.

1:

Procedure

Search(
G,S,goal
)


2:

Inputs

3:

G
: graph with nodes

N

and arcs

A


4:

S
: set of start nodes


5:

goal
: Boolean function of states


6:

Output

7:

path from
a member of

S

to a node for which

goal

is true


8:

or



if there are no solution paths


9:

Local

10:

Frontier
: set of paths


11:

Frontier ←{

s

: s

S}


12:

while

(
Frontier ≠{}
)


13:
select

and

remove


s
0
,...,s
k


from

Frontier


14:

if

(

goal(s
k
)
)

then


15:

return


s
0
,...,s
k



16:

Frontier ←Frontier

{

s
0
,...,s
k
,s

:

s
k
,s


A}


17:

return



Fig.5.

Generic graph searching algorithm
.



The generic search algorithm is shown in

Figure

3.4
. Initially, the frontier is the set of empty paths
from start nodes. At each ste
p, the algorithm advances the frontier by removing a path


s
0
,...,s
k


from the
frontier. If

goal(
s
k
)

is true (i.e.,

s
k

is a goal node), it has found a solution and returns the path that was found,
namely


s
0
,...,s
k

. Otherwise, the path is extended by one more arc by finding the neighbors of

s
k
. For every
neighbor

s

of

s
k
, the path


s
0
,...,s
k
,s


is
added to the frontier. This step is known as

expanding
the

node

s
k
.

This algorithm has a few features that should be noted:



The selection of a path at

line

13

is non
-
deterministic. The choice of path that is selected can affect the
efficiency; see the

box

for more
details on our use of "select". A particular search strategy will determine
which path is selected.



It is useful to think of the

return

at

line

15

as a temporary return; another path
to a goal can be searched for
by continuing to

line

16
.



If the procedure returns


, no solutions exist (or there are no remaining solutions if the proof has been
retried).



The algorithm only tests if a path ends in a goal node

after

the path has been selected from the fronti
er, not
when it is added to the frontier. There are two main reasons for this. Sometimes a very costly arc exists
from a node on the frontier to a goal node. The search should not always return the path with this arc,
because a lower
-
cost solution may exis
t. This is crucial when the least
-
cost path is required. The second
reason is that it may be expensive to determine whether a node is a goal node.

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If the path chosen does not end at a goal node and the node at the end has no neighbors, extending the
path
means removing the path. This outcome is reasonable because this path could not be part of a path from a
start node to a goal node.


IV.

Problem Reduction Algorithms


Problem reduction search can be
planning how

best to solve a problem that can be recursively
decomposed into sub
-
problems in multiple ways.

1)

Matrix multiplication problem

2)

Tower of Hanoi

3)

Blocks world problems

4)

Theorem proving


4.1. AO* Algorithm:

1. Let G consists only to the node representing the
initial state

call this node INTT.Compute
h' (INIT).

2. Until INIT is labeled SOLVED or hi (INIT) becomes greater than FUTILITY, repeat the




following procedure.

(I)


Trace the marked arcs from INIT and select an unbounded node NODE.

(II)


Generate
the successors of NODE
. if there are no successors then assign FUTILITY as




h' (NODE). This means that NODE is not solvable. If there are successors then for each one




called SUCCESSOR, that is not also an ancester of NODE do the followin
g



(a) add SUCCESSOR to graph G


(b) if successor is not a terminal node, mark it solved and assign zero to its h ' value.


(c) If successor is not a terminal node, compute it h' value.

(III) propagate the newly discovered
information up the graph by doing the following . letS be a




set of nodes that have been marked SOLVED. Initialize S to NODE. Until S is empty repeat




the following procedure;

(a) select a node from S call if CURRENT and remove it from S.

(
b) compute h' of each of the arcs emerging from CURRENT , Assign minimum h' to




CURRENT.

(c) Mark the minimum cost path a s the best out of CURRENT.

(d) Mark CURRENT SOLVED if all of the nodes connected to it through the new marked





are have been labeled SOLVED.

(e) If CURRENT
has been marked SOLVED or its h
' has just changed, its new status must





be p
ropagate backwards up the graph. H
ence all the ancestors of CURRENT are added to S.


4.1.1.
AO*

Search Procedure:

1. Place

the start node on open.

2. Using the search tree, compute the most promising solution tree TP .

3. Select node n that is both on open and a part of tp, remove n from open and place it no closed.

4. If n is a goal node, label n as solved. If the start node

is solved, exit with success where tp is the solution tree,
remove all nodes from open with a solved ancestor.

5. If n is not solvable node, label n as unsolvable. If the start node is labeled as unsolvable, exit with failure.
Remove all nodes from open ,
with unsolvable ancestors.

6. Otherwise, expand node n generating all of its successor compute the cost of for each newly generated node
and place all such nodes on open.

7. Go back to
step (
2)

Note:

AO* will always find minimum cost solution.

Properties
of AO*:

• AO* is a generalization of A* for AND
-
OR graphs

• AO*, like A*, is admissible if the heuristic function isadmissible and the usual assumptions (finite
branchingfactor etc) hold

• AO*, like A* is also optimal among the class of heuristicsearch alg
orithms that use an additive cost /
evaluation function
.


V.

Conclusion


We conclu
de that by using
this algorithm

we can solve AI problems easily.AI algorithms are also
called as a problem solving algorithms.

Artificial Intelligence will surpass human intelli
gence. Although it has
proven itself to be similar to the human brain, computers do not think in the same way. In this report, we have
discussed Algorithms of AI and their impact on

problem solving and

our lives.

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http://www.hutter1.net/ai/uaibook.htm
l
.

[2]

Elaine Rich, Kevin Knight,


Artificial Intelligence
"
,

Second Edition,

page no.3
.

[3]



Universal Artificial Intelligence

Subtitle: Sequential Decisions based on
Algorithmic Probability
.

[4]

http://en.wikipedia.org/wiki/Artificial_intelligence
.

[5]

http://www.codeproject.com/Articles/16286/AI
-
Simple
-
Genetic
-
Algorithm
-
GA
-
to
-
solve
-
a
-
card
-
pro
.

[6]

http://artint.info/html/ArtInt_51.html.

[7]

David Poole
,
AlanMackworth
,


Artificial Intelligence: Foundations of Computational Agents
”,
2010
.

[8]

http://www.kirupa.com/forum/showthread.php?72863
-
Tutorial
-
Path
-
Finding
-
Algorithms
-
AI
.

[9]

http://webdocs.cs.ualberta.ca/~lanctot/files/papers/pathfinding
-
orbius
-
2006.pdf
.

[10]

http://e
n.wikipedia.org/wiki/A*_search_algorithm
.

[11]

Richards E.Korf* , “
Artificial In
telligence Search Algorithms

,
Computer Science Department University of California, Los
Angeles,Ca.90095
June 23,1998.

[12]

http://www.synergy.ac.in/intranet/e
-
book/(Ebook%20
-
%20Paper
)
%20Artificial%20Intelligence%20Search%20Algorithm
s.pdf.

[13]

Robin

,“
A star algorithm
”December 18th, 2009 |

[14]

http://intelligence.worldofcomputing.net/ai
-
search/a
-
star
-
algorithm.html
.

[15]

http://artificialintelligence
-
notes.blogspot.in/2010/07/problem
-
reduction
-
with
-
ao
-
algorithm.html
.

[16]

http://www.cs.iastate.edu/~cs572/WWW
-
honavar07/cs572problem
-
reduc
tion.pdf
.

[17]

lowa state university, deparment of computer science artificial int
elligence research laboratory.

[18]

ReferedF
rom Artificial Intelligence TMH.

[19]

http://www.eetindia.co.in/VIDEO_DETAILS_700000062.HTM

Prof. PallabDasgupta of the Department of Computer Science and
Engineering, IIT Kharagpur
conducted this lecture.

[20]

KoushalKumar ,GourSundarMitra Thakur“ Advanced Applications of Neural Networks and A
rtificial Intelligence: A Review.”
Published Online June 2012 in MECS (http://www.mecs
-
press.org/) DOI: 10.5815/ijitcs.2012.06.08,pg.no.58
.

[21
]

]
By National Research Council, ”Applications of Robotics and Artificial Intelligence to Reduce Risk and Improve

Effectiveness”.pg.no.2
-
3

[22]

RC Chakraborty, ”Expert Systems: AI course lecture 35
-
36”,pg no. 03.

[23
]

]
Didier Dubois
-
Henri Prade,” The place of Fuzzy Logic in AI”
.

[24
]

]
Andrew Ilyas,” An Isearch research paper(Artificial Intelligence)”, May 13, 2010
.

[25]

NirPochter and Jeffrey S. Rosenschein, ”Requirements on Heuristic Functions when Using A* in Domains with Transpositions”.

[26]

Robin, ”Heuristic Search(artificial intelligence articles on artificial intelligence)”. December 14
th
,2009.

[27]

Peng Dai,”

Heuristic Search Ideas for Deterministic and Probabilistic Problems”, Journal, Pg.no.1.

[28]

Sven Koenig Maxim LikhachevYaxin Liu David Furcy, “Incremental Heuristic Search in Artificial Intelligence”, Pg.no. 08.

IOSR Journal of Computer Engineering (IOSRJCE)

ISSN: 2278
-
0661, ISBN: 2278
-
8727

Volume
6
, Issue
3

(Sep
-
Oct. 2012), PP
09
-
14

www.iosrjournals.org

www.iosrjournals.org
9

| Page


Wireless Micro
-
Sensor Network Models


Ms. Swapnali S. Maske
1
, Mrs. Tejaswini A. Pawar
2

1
Department of Information Technology,

BVCOE,

Shivaji University, Kolhapur, India.

2
Department of Computer Science and Engineering,

BVCOE,

Shivaji University, Kolhapur
, India.


Abstract:
Wireless sensor network is important in sensing, Collecting and disseminating information about
environmental Phenomenon. This paper contain emerging field to classify wireless micro sensor network
according to different communication
functions, data delivery models and network dynamics. This taxonomy
will aid in defining appropriate communication infra structure for diff. sensor network application subspaces. It
allows network designer to choose the Protocol architecture. According to
their application this taxonomy will
enable new sensor network models to define for use in further research in this area.

The overall communication
behavior in a wireless micro sensor Network is application driven. It is useful to decouple the application
communication used for information dissemination from the infrastructure communication used to configure
and optimize the Network. This separation will aid network designers in selecting the appropriate sensor
network architecture that will best match the
characteristics of the communication traffic of a given application.
This will allow the network protocol to achieve the application
-
specific goals of energy
-
efficiency, low latency,
and high accuracy in the sensing application. We also believe that a sens
or
-
initiated proactive path recovery
approach with local patching will be beneficial in efficient information dissemination in wireless micro
-
sensor
networks.

The taxonomy presented will be helpful in designing and evaluating future network protocols for
w
ireless micro
-
sensor networks.

Often, it is possible to implement a sensor network for a specific phenomenon
in a number of different ways. Consider the problem of monitoring a tornado. One option would be to fly
airplanes to sense the tornado (mobile phen
omenon; mobile sensors; continuous data delivery). Another would
be to have a sensor grid statically placed on the ground and report data as the tornado passes through (mobile
phenomenon; static sensors; continuous data delivery). Yet another would be to r
elease lightweight sensors into
the tornado (static phenomenon; mobile sensors; Continuous data delivery).


I.

Introduction

Development in hardware and wireless network technology introduce a new area were small wireless
devices will provide access to informa
tion any time, anywhere as well as actively participate in creating smart
environment. Sensor networks hold the promise of revolutionizing sensing a wide range of application domains.
This is because of their reliability, accuracy, flexibility, cast
-
effec
tiveness and case of development. Sensor are
rapidly deployed in remote inhospitable area for a surveillance application sensors are used to analyze the
motion of a tornado; Sensors are used in forest for fire detection; sensors are attached to tool cabs i
n a large
metropolitan area to study the traffic conditions and plan routes effectively. There is wide range of applications
for sensor network with differing requirements. In report the classification wireless micro
-
sensor networks from
a communication pr
otocol perspective, I look at characteristic and goals of typical micro
-
sensor network as well
as different types of communication that required achieving their goals.

The remaining report is organized as Chapter 2 contains Performance Metrics of Sensor Ne
twork.
Chapter 3 contains Architecture of Sensor Network. Chapter 4 contains The Data Delivery Models. Chapter 5
contains the Network Dynamic Models for Sensor Network. Chapter 6 contains Some Network Protocol,
Following are the terminologies used in the r
eport.


1.1 Sensor

The device that implements the physical sensing of environmental phenomena and reporting of
measurements (through wireless communication). Typically it consists of five Components
-
sensing hardware,
memory battery, embedded processor, and

trance
-
receiver [1]. The basic sensor network is shown in Figure 1.1.

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-
Sensor Network Models

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Fig. 1.1 The basic sensor network

1.2 Observer

The end user interested in obtaining information disseminated by the sens
or network about the
phenomena.

The observer may indicate interests (or queries) to the network and receive response to these queries [2].


1.3 Phenomenon


The entity of interest to the observer that is being served and optionally analyzed/filtered the
sensor
networks. There may be multiple phenomena under observation concurrently in the same network.

While monitoring the phenomena some latency and accuracy must be restricted. In a typical sensor network
individuals sensor serve the local values and diss
eminate information to other sensors and to the observer.
Sensors network shares many challenges of traditional wireless network. This include limited energy available
to each node band width
-
limited, error
-
prone channel communication in sensor network is
end
-
to
-
end. The
function of network is to repeat information about phenomenon to observer. The observer not necessarily aware
of the sensor networks infrastructure and
individual

sensor as end point communication.


II.

Performance Metrics

Followin
g are the metrics to evaluate sensor network protocols.


2.1 Energy Efficient

As sensor nodes are battery operated, protocols must be energy efficient or maximize system lifetime


2.2 Scalability

It is also a critical footer for large
-
scale network it is l
ikely that locating interactions through hierarchy and
aggregation will be critical for ensuring scalability.


2.3 System Lifetime

System lifetime is the time until half of nodes die or by application directed metrics such as when the network
stop providin
g the application with desired information about phenomena.


2.4 Latency

The observer is interested in knowing about the phenomena within a given delay. The precise semantics of
latency are application dependent.


2.5 Accuracy

Primary object of observer is

to obtain the accurate information, where accuracy is determined by given
application. The given infrastructure should be adaptive that the application obtain desired accuracy and latency
with minimum energy used.


2.6 Fault tolerance

Due to surrounding p
hysical conditions the sensor may fail, or also fails because of their energy ran out. This is
difficult to replace the existing sensor, so the network is fault tolerance so that actual network condition is
transparent to given application.




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-
Sensor Network Models

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II.

Sensor Netwo
rk Architecture

A Sensor network is a tool for measuring and relaying information about the phenomena to the
observer within the desired performance bond and deployment cost.

The organization of the network as shown in following Figure 3.1




Fig. 3.1 Sensor network architecture

3.1 Infrastructure

The infrastructure consist of the sensor and their deployment status [2], more precisely it consist of
characteristics of sensor e.g. sensing accuracy, memory siz
e, battery life, transmission ranges and deployment
strategy (e.g. sensor density, sensor location and mobility.)


3.2 Network P
rotocols

The network protocol is responsible creating paths and accomplishing communication between the sensors and
observation
.


3.3 Application / Observer

The observer interest queries about the phenomenon as approximated by distributed data that the sensor is
capable of serving.



These queries could be static or dynamic. The translation of data could be done by the application

software at the observer and /or the sensor nodes or directed by human observer.

The network protocol in a sensor network is responsible for all communication both among sensors as well as
between the sensors and observer.

In order to determine how the ne
twork protocol behaves for diff. scenario’s, it is important to classify their
feature. In next session different types of communication required in sensor network.


3.4
Communication models


The communication within a sensor network can be classified in t
o two
-
category application and
infrastructure [1].


Application communication relates to the transfer of sensor data with the goal of informing the observer
about the phenomena within application communication, there are two modes co
-
operative and non co
-
o
perative.

Non co
-
operative sensors do not cooperate at the application revel for information transfer.

The extreme case will be when no sensor communication with its neighbors. All sensors were independently.

Co
-
operative sensors might be required to commu
nicate with its neighbor s either periodically or after the
occurrence of specific event.

Infrastructure communication refers to the communication needed to configure, maintain and optimize
operation. The infrastructure communication is needed to keep the
network functional ensure robust operation
in dynamic environment as well as optimize overall performance.

A sensor network requires both the application an
d infrastructure communication.

In static sensor networks an
initial phase of infrastructure communi
on is needed to set up the networks
furthermore
, if the sensors are
mobile, additional communications needed for path discovery/ reconfiguration.


III.

Data Delievery Models

Sensor networks can be classified in terms of data delivery models required by applicat
ions (observer)
interest as

1.Continues

2. Event driven

3.Observer initiated

4. Hybrid

4.1 Continues

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-
Sensor Network Models

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In the Continues models the sensor communicates their data continuously at a pre
-
specified rate [1] [2].
Clustering is the most efficient for static ne
tworks where data is continuously transmitted.


4.2 Event Driven

In the event driven data models the sensors reporting information only if an event of interest occurs,
here observer is interested in the occurrence of a specific phenomenon or a set of pheno
menon [1] [2].


4.3 Observer Initiated

In the observer initiated (or request reply) model the sensors only report their results in response to an explicit
request from the observer (either directly, or indirectly through other sensors)


4.4 Hybrid

Hybrid m
odels are one where the above three approaches can co exists in the network.

In this way we have only classify the data delivery from the application perspective. For any of the above
models the communication approaches can be classified as 1. Flooding (Br
oadcast based) 2. Unicast 3.
Multicast. In flooding approach sensor broadcast information in neighboring node and it rebroadcast this data
until reaches to the observer.


IV.

Network Dynamics Models

A path is formed between phenomena and the observer by a sens
or network the sensor network
protocol creates and maintains this path (or multiple paths) under dynamic condition while meeting the
application requirements of low energy, low latency, high accuracy and fault tolerance.

There are several approaches to con
struct and maintain path between the observer and the
phenomenon. They are classified as static sensor network and mobile / dynamic sensor network, the important
point is mobility because it is most common source of dynamic conditions other source includes

sensors failure
and change in observer interest.


5.1 Static Sensor Network

There is no motion among communication sensors, the observer and the phenomenon. An example is a
group of sensors spread for temperature sensing.

In this type of network, sensor n
odes requires an initial one time setup infrastructure communication to create
the path between the observer and the sensors with the remaining traffic exclusively application
communication.


5.2 Dynamic Sensors Network

In dynamic sensor network, either th
e sensors themselves, the observer or the phenomenon are mobile,
whenever a phenomenon associated with sensors moves, the path between observer and phenomenon get failed,
in such situation either observer or the sensor must rebuild a new path. There are tw
o types of rebuilding of new
paths between observer and sensors.


The first approaches is observer
-
initiated approach, in this case during initial setup the observer can
build multiple paths between itself and the phenomenon. The observer can use the most
beneficial path, if path
fails another path can be used, if all paths fail, the observer must rebuild new paths. This observer initiated
approach is a reactive approach where path recovery action is only taken after observe a broken path [1] [2].

Another a
pproach is sensor
-
initiated approach. In sensor initiated path recovery procedure, path recovery is
initiated by a sensor that is currently a part of logical path between the observer & the phenomenon. It is
planning to move out of range. The sensors perfo
rm some local patching by broadcasting a participation request
to neighboring sensor for given logical flow to build a new path. If any sensor sends participation reply message
then the new path is build, if none of sensor reply then sensor can de
fault to
sending a path invalid
ation request
to the observation so that the observer can start building the path.


Dynamic sensor networks can be further classified by considering the motion of the components. This
motion is important. Each of following requires di
fferent infrastructure; data delivery models and protocols


5.2.1 Mobile observ
er

In this case the observer is mobile with respect to the sensors and phenomena. An example of this
paradigm is sensors deployed in an inhospitable area for environment monitor
ing. For example, a plane might
fly over a field periodically to collect information from a sensor network. Thus the observer, in the plane, is
moving relative to the sensors and phenomena on the ground.

Wireless Micro
-
Sensor Network Models

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Fig. 5.2.1 Mobile observer

5.2.2 Mobile sensors

In this case, the sensors are moving with respect to each other and the observer. For example, consider
traffic monitoring implemented by attaching sensors to taxis. As the taxis move, the attached
sensors
continuously communicate with each other about their own observations of the traffic conditions. If the sensors
are co
-
operative, the communication paradigm imposes additional constraints such as detecting the link layer
addresses of the neighbors
and constructing localization and in formation dissemination structures. We know
that the overhead of maintaining a globally unique sensor ID in a hierarchical fashion like an IP address is
expensive and not needed. Instead, these sensors should communicat
e only with their neighbors with the link
layer MAC address. In such networks, the above
-
mentioned proactive algorithm with local patching for
repairing a path can be used so that the information about the phenomenon is always available to the observer
reg
ardless of the Mobility of the individual sensors.



Fig. 5.2.2 Mobile sensor

5.2.3

Mobile Phenomena

In this case, the phenomenon itself is moving. A typical example of this paradigm is sensors

deployed
for animal detection. In this case the infrastructure level communication should be event
-
driven. Depending on
the density of the phenomena, it will be inefficient if all the sensor nodes are active all the time. Only the
sensors in the vicinity
of the mobile phenomenon need to be active. The number of active sensors in the vicinity
of the phenomenon can be determined by application specific goals such as accuracy, latency, and energy
efficiency.




Fig 5.2.3 Mobile phenomenon


V.

Overview Of Some Network Protocols

In this section we consider several existing protocols for sensor networks and analyze them in the
context of our taxonomy.





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-
Sensor Network Models

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6.1 Ad Hoc Routing Protocol

Ad hoc routing protocols

may be used as the network protocol for sensor networks [2]. However, such
protocols will generally not be good candidates for sensor networks because of the following reasons:

(i) Sensors have low battery power and low available memory;

(ii) The routing

table size scales with the network size;

(iii) These networks are designed for end
-
to
-
end communication and react inappropriately to mobility;

(iv) Their addressing requirements may be inappropriate for sensor networks
;


(v) Ad hoc routing protocols do
not su
pport cooperative dissemination;

As ad hoc routing protocols do not inherently support data aggregation or fusion, they will not perform well in
sensor network applications.


6.2 LEACH


LE
ACH is an energy efficient protocol for sensor networks design
ed for sensor networks with continuous data
delivery mechanism and no mobility [2]. LEACH uses a clustering architecture where member nodes send their
data to the local cluster
-
head. Cluster
-
heads aggregate the data from each sensor and then send this info
rmation
to the observer node. LEACH uses rotation of the cluster head in order to evenly distribute the energy load.
Once clusters are formed, cluster members use TDMA to communicate with the cluster
-
head. Thus LEACH is
suitable for networks where every no
de has data to send at regular intervals. However, it needs to be extended
for event driven models as well as for mobile sensors.


6.3 Direct Diffusion

Directed Diffusion (DD) is a data
-
centric protocol, where nodes are not addressed by their addresses
bu
t by the data they sense [2]. Attribute
-
value pairs name data. In directed diffusion observer nodes express the
interest in term of a query, which diffuses through the network using local interactions. Once a sensor node that
satisfies the query (source no
de) is reached, that node starts transmitting data to the sink node, again using local
interactions. The absence of a notion of a global id (e.g., IP address) makes directed diffusion efficient for
networks with mobility as well. Directed diffusion is appl
icable for Event
-
driven and query
-
driven networks as
defined in our taxonomy.


VI.

Conclusion

The overall communication behavior in a wireless micro sensor Network is application driven. It is
useful to decouple the application communication used for informat
ion dissemination from the infrastructure
communication used to configure and optimize the Network. This separation will aid network designers in
selecting the appropriate sensor network architecture that will best match the characteristics of the
communic
ation traffic of a given application.

This will allow the network protocol to achieve the application
-
specific goals of energy
-
efficiency, low latency,
and high accuracy in the sensing application. We also believe that a sensor
-
initiated proactive path re
covery
approach with local patching will be beneficial in efficient information dissemination in wireless micro
-
sensor
networks.

The taxonomy presented will be helpful in designing and evaluating future network protocols for
wireless micro
-
sensor networks.

Often, it is possible to implement a sensor network for a specific phenomenon in a number of different
ways. Consider the problem of monitoring a tornado. One option would be to fly airplanes to sense the tornado
(mobile phenomenon; mobile sensors; contin
uous data delivery). Another would be to have a sensor grid
statically placed on the ground and report data as the tornado passes through (mobile phenomenon; static
sensors; continuous data delivery). Yet another would be to release lightweight sensors int
o the tornado (static
phenomenon; mobile sensors; Continuous data delivery).

The accuracy is a function of the sensing technology of the sensors and their distance from the
phenomenon. However, since the performance is measured at the observer end, it is a
lso a function of the
performance of the communication model. We hope that this taxonomy will assist in developing relevant
simulation models to enable empirical study of the performance of the different sensor network organizations
and assist in making de
sign and deployment decisions.


References

[1]

Sameer Tilak, Nael B, Abu
-
Ghazaleh and Wendi Heizelmn,”A Taxonomy of Wireless Micro Sensor Network Models”,

[2]

Sameer Tilak, Nael B, Abu
-
Ghazaleh and Wendi Heizelmn ,” The Updatesd A Taxonomy of Wireless Mi
cro Sensor Network
Models”,ACM And Mobile Computing Review, April 2002,Volume 1,Number 2.

IOSR Journal of Computer Engineering (IOSRJCE)

ISSN: 2278
-
0661, ISBN: 2278
-
8727

Volume
6
,
Issue
3

(Sep
-
Oct. 2012), PP
15
-
24

www.iosrjournals.org

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15

| Page


Mobile Networking and Ad hoc routing protocol
s

validation


1
Simanta Sarma,
2
Dr. Sarbananda Das

1
(
HOD

&
Asstt. Professor, Department of Computer Science, S.B.M.S College, Sualkuchi, Assam, India)

2
(Rtd. Principal, North Gauhati College, North Guwahati,
Assam, India)


Abstract:

In this paper we describe mobile network

and efficient routing protocol for wireless ad hoc networks.
We report on its implementation, on performance comparisons and on a formal validation result. Moreover we
discuss
Cellular

system design,
global System for mobile Communication,
Formal Protocol Verification
and
operating over infrared or Bluetooth.
This paper evaluates two model checking tools, SPIN and UPPAAL, using
the verification of the Ad hoc Routing protocol as a case s
tudy. Insights are reported in terms of identifying
important modeling considerations and the types of ad hoc protocol properties that can realistically be verified.

Keywords
.
:

Cellular Phone network, mobile

ad hoc networks, routing protocols,
Wireless networks, ad hoc
routing, routing protocol Implementation, formal validation,
model checking,
Infrared or Bluetooth
, GSM
.


I.

Introduction


Cellular communications has experienced explosive growth in the past two decades. Today millions of
people
around the world use cellular phones. In modern area Cellular phones are most important factor in
human life. Cellular phones allow a person to make or receive a call from almost anywhere. Likewise, a person
is allowed to continue the phone conversation wh
ile on the move. Cellular communications is supported by an
infrastructure called a cellular network, which integrates cellular phones into the public switched telephone
network.

Cellular service has seen tremendous acceptance, especially in the last few y
ears, with millions of new
subscribers each year and the new subscriber rate growing. Some estimates predict that half a billion cellular
phones will be in service by the end of the next decade.
A
D
-
HOC networks are typically described as a group of
mobile
nodes connected by wireless links where every node is both a leaf node and a router.

For a cellular
system the major resources availa
ble are:
1. Bandwidth







2.
Power

Out of which bandwidth is a major issue of concern. Because the
spectrum allocated for cellular
communication is limited. With the great increase in number of wireless devices such as mobile phones, the
demand for wireless communications has grown exponentially over the last decade and is expected even more in
the futu
re. More and more multimedia traffic are being transmitted via wireless media, and such applications
require diverse QoS. Hence there is scarcity of bandwidth.

H
igh
-
speed cellular networks working today are
expected to support
multimedia applications
, whic
h require QoS provisions. Since frequency spectrum is the
most expensive resource in wireless networks, it is a challenge to support QoS using limited frequency
spectrum.


II.

Mobile Ad
-
Hoc
Network


Theoretical mobile ad hoc networking research [CCL03] starte
d some decades ago. But commercial
digital radio
technologies

appeared in the mid
-
nineties. Since then, few
proposals

for enabling ad hoc
communications were made. The first technology (IEEE802.11, also referred to as Wi
-
Fi [ANS99]) is still
strongly leadi
ng the market, although there is great room for improvement. This section provides an overview
and a technical description of the technologies that have been proposed hitherto. A common feature of most
wireless networking technologies is that they operate
in the unlicensed Industrial Scientific and Medical (ISM)
2.4GHz band. Because of this choice of frequency band, the network can suffer interferences from microwave
ovens, cordless telephones, and other appliances using this same band plus, of cours, other

networks. In
particular, Farrell and Abukharis studied the impact on Bluetooth on IEEE802.11g [ST04]


2.1 Packet radio


Packet radio [GFS78] was used for the earliest versions of mobile ad hoc networks. It was sponsored
by DARPA in the 1970s. It allows
the transmission of digital data over amateur radio channels. Using special
radio equipment, packet radio networks allowing transmissions at 19.2 kbit/s, 56 kbit/s, and even 1.2 Mbit/s
have been developed. Since the modems employed vary in the modulation t
echniques they use, there is no
standard for the physical layer of packet radio networks. Packet radio networks use the AX.25 data link layer
protocol, derived from the X.25 protocol suite and designed for amateur radio use. AX.25 has most frequently
been
used to establish direct, point
-
to

point links between packet radio stations, without any additional network
Mobile Networking and

Ad hoc routing protocols validation

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layers. However, in order to provide routing services, several network layer protocols have been developed for
use with AX.25. Most prominent among

these are NET/ROM, ROSE, and TexNet. In principle, any network
layer protocol may be used, including the Internet protocol (IP), which was implemented in the framework of
the AMPRNet project.


2.2 IEEE802.11


Wi
-
Fi is a wireless networking technology base
d on the IEEE802.11 specifications. The first

and still
most used

Wi
-
Fi standard is referred to as IEEE802.11b in the scientific literature. It was then declined into
IEEE802.11a, IEEE802.11g and IEEE802.11n. IEEE802.11i and IEEE802.11h, which respectively

focus on
Quality of Service (QoS) and security, are out of the scope of this document. All Wi
-
Fi technologies operate on
the 2.4GHz band, except from IEEE802.11a which operates within the 5GHz band. These technologies use
significantly different PHY layer
s which, from the user point of view, make them differ in term of the
bandwidth (i.e. the data rate) that they provide. Typically, Wi
-
Fi enabled devices have coverage distances
ranging from 50 to more than 100 meters. In practice, this coverage distance de
pends greatly on the nature of the
antenna and on the environment in which the devices evolve.


2.2.1 IEEE802.11a


IEEE802.11a uses Orthogonal Frequency Division Multiplexing (OFDM). It is the only wireless radio
technology that works in the 5GHz band. The

main idea behind OFDM is that since low
-
rate modulations (i.e
modulations with relatively long symbols compared to the channel time characteristics) are less sensitive to
multipath, it should be better to send a number of low rate streams in parallel than

sending one high rate
waveform. OFDM then works by dividing one high
-
speed signal carrier into several lower
-
speed subcarriers,
which are transmitted in parallel. High
-
speed carriers, which are 20MHz wide, are divided into 52 sub channels,
each approximat
ely 300KHz wide. OFDM uses 48 of these sub channels for transporting data, while the four
others are used for error correction. OFDM delivers higher data rates and a high degree of multipath reflection
reconstruction,

thanks to its encoding scheme and erro
r correction.


2.2.2 IEEE802.11b


IEEE 802.11b uses Direct Sequence Spread Spectrum (DSSS) as the physical layer technique for the
standard. DSSS uses a complex technique which consists in multiplying the data being transmitted by a
noise
signal. This noi
se signal is a pseudo
-
random sequence of 1 and

1 values, at a frequency much higher than the
original signal. The resulting signal wave looks much like white noise. This white noise can be filtered at the
receiving end in order to recover the original dat
a. This filtering happens by again multiplying the same pseudo
-
random sequence by the received signal (because 1
×
1= 1, and

1
× −
1 = 1). This process, known as “de
-
spreading”, mathematically constitutes a correlation of the transmitted pseudo
-
random sequ
ence with the
receiver’s assumed sequence. For allowing de
-
spreading to work correctly, the transmit and received sequences
must
synchronized
. So far, IEEE 802.11b is the implementation of the IEEE 802.11 standard that has been most
heavily studied in the
framework of mobile ad hoc networks.


2.2.3 IEEE802.11g


IEEE802.11g, just like IEEE802.11a, uses orthogonal frequency
-
division multiplexing (OFDM), it then
boasts similar bandwidths. OFDM is described in Section 2.2.1. But unlike IEEE802.11a, IEEE802.11g
works
in the 2.4 GHz band. Since the draft 802.11g standard combines fundamental features from both 802.11a and
802.11b, it leads to the development of devices that can inter
-
operate with technologies based on both of the
previous versions of the specifica
tion.


2.3 Bluetooth


Bluetooth is essentially the same kind of microwave radio technology that has given us wireless door
chimes and automatic garage door openers. It was initially restricted to an operating distance of just 10 meters
and a speed of appro
ximately 1 Mbit/s. When Bluetooth devices come within range of each other, they establish
contact and form a temporary network called a Personal Area Network (PAN). In the Bluetooth terminology,
this is also known as a Piconet. A multi
-
hop ad hoc network f
ormed by the interaction of Bluetooth devices is
called a Scatternet. When using Bluetooth, the devices must establish a network session before being able to
transmit any data. Bluetooth uses the Frequency
-
Hopping Spread Spectrum (FHSS) technique. Unlike
I
EEE802.11 which establishes a communication link on a certain frequency (a channel), FHSS breaks the data
down into small packets and transfers it on a wide range of frequencies across the available frequency band.
Bluetooth transceivers jump among 79 hop
frequencies in the 2.4 GHz band at the rate of 1,600 frequency hops
per second. 10 different types of hopping sequences are defined, 5 of the 79 MHz range/79 hop system and 5 for
the 23 MHz range/23 hop system.

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This technique trades off bandwidth, in orde
r to be robust and secure. More precisely, Spread Spectrum
communication techniques have been used for many years by the military because of their security capabilities.


2.4 Hiperlan


The HiperLAN2 standard is very close to 802.11a/g in terms of the physi
cal layers it uses

both use
OFDM technology

but is very different at the MAC level and in the way the data packets are formed and
devices are addressed. On a technical level, whereas 802.11a/g can be viewed as true wireless Ethernet,
HiperLAN2 is more simi
lar to wireless Asynchronous Transfer Mode (ATM). It operates by sharing the 20MHz
channels in the 5GHz spectrum in time, using Time Division Multiple Access (TDMA) to provide QoS through
ATM
-
like mechanisms. It supports two basic modes of operation: centr
alized mode and direct mode. The
centralized mode is used in the cellular networking topology where each radio cell is controlled by an access
point covering a certain geographical area.


2.5 ZigBee


ZigBee
-
enabled devices conform to the IEEE 802.15.4
-
2003

standard. This standard specifies its lower
protocol layers, the physical layer (PHY), and the medium access control (MAC). It targets Low
-
Rate Wireless
Personal Area Network (WPAN). ZigBee
-
style networks research began in 1998. Zigbee was intended to
ope
rate in contexts in which both Wi
-
Fi and Bluetooth are not suitable. Zigbee operates in the unlicensed 2.4
GHz, 915 MHz and 868 MHz ISM bands. It uses direct
-
sequence spread spectrum (DSSS) coding. This makes
the data rate to reach

250 kbit/s per channel i
n the 2.4 GHz band, 40 kbit/s per channel in the 915 MHz band,
and 20 kbit/s in the 868 MHz band. The maximum output power of ZigBee antennas being generally 1 mW, the
transmission range of ZigBee nodes is between 10 and 75 meters. Observations have shown
that the
transmission range is heavily dependent on the environment.


2.6 Broadband wireless networking


WiMAX (IEEE 802.16) stands for

Worldwide Interoperability for Microwave Access. IEEE 802.16
boasts data rates up to 70 Mbit/s over a distance of 50 km.

However practical limits from real world tests seem
to be between 500 kbit/s and 2 Mbit/s at a distance of around 5
-
8kms. WiBro is a wireless broadband internet
technology being developed by the Korean telecoms industry. It has been announced that WiBro b
ase stations
will offer an aggregate data throughput of 30 to 50 Mbit/s and cover a radius of up to 5 km. The technology will
also offer Quality of Service.


HIPERMAN [HPF03, HPF04], which stands for High Performance Radio Metropolitan Area Network,
is a E
uropean alternative to WiMAX. The
standards were

created by the European Telecommunications
Standards Institute (ETSI). It provides a wireless network communication in the 2
-
11 GHz bands. The
adequation of these technologies to ad hoc networking is discuss
able, since they would permit to establish ad
hoc networking at a level at which technologies for infrastructure networks (like GSM or UMTS) are available.


III.

Ele
ments Of Cellular System Design

3.1 Frequency Reuse:


Frequency Reuse means, two users in two
distant cells can operate on same frequency. The

cellular
system makes an efficient use of available channels by using low power transmitters to allow frequency reuse at
smaller distances. Frequency Reuse can either be in time domain or in frequency domain
, it is done by TDMA
scheme that is allocation of different time slots to the frequency reuse scheme. In frequency domain, it is done
by FDMA scheme, i.e. repeat carrier frequency after some time and frequency reuse distance.


3
.2 Frequency Reuse Distance (D):



It means the minimum distance which allows the same frequency to be reused





K

=
frequency reuse pattern




R
=
radius of the cell


3.3 Call Blocking
Probability
:


Blocking probability is the probability of blocking

calls out of N

number of calls generated in a busy
hour condition.

It is measured in Erlangs.


3.4 Co
-
channel Interference Ratio:


The
S/I
ratio at the desired mobile receiver i
s given as:

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Where:

Ik
= the interference due to the
k
th interferer

NI
= the number of interfering cells in the first tier.


In a fully equipped hexagonal
-
shaped cellular system, there are always six Co
-
channel
-
interfering cells
in the first tier (i.e.,
NI
= 6, see Figure 2.7). Most of the co
-
channel interference results from the first tier.
Contribution from second and higher t
iers amounts to less than 1% of the total interference and, therefore, it is
ignored. Co
-
channel interference can be experienced both at the cell site and the mobile stations in the center
cell. In a small cell system, interference will be the dominating f
actor and thermal noise can be neglected. Thus
the
S/I
ratio can

be given as:



where:

2 ≤ γ ≤ 5 = the propagation path loss, and
_
depends upon the terrain environment

Dk
=the distance bet
ween mobile and
k
th interfering cell,

R
= the cell radius


IV.

Global Syst
em For Mobile Communication (GSM
)


GSM

(Global System for Mobile communications): is the most popular standard for

mobile
phones

in the world. The

GSM Association
estimates that 80% of the global mobile market uses the standard.
GSM is a

digital mobile telephony system that is widely used in Europe and other parts of the world. GSM uses
a variation of time division multiple access (TDMA) and is the most widely used
of the three
digital

wireless

telephony technologies (TDMA, GSM, and

CDMA). GSM digitizes and compresses data, then
sends it down a channel with two other streams of user data, each in its own time slot. It operates at either the
900

MHz

or 1800 MHz freque
ncy band.
Mobile services based on GSM technology were first launched in
Finland in 1991. GSM, together with other technologies, is part of the evolution of wireless mobile
telecommunications that includes High
-
Speed Circuit
-
Switched Data (HCSD), General P
acket Radio System
(GPRS), Enhanced Data GSM Environment (EDGE), and Universal Mobile Telecommunications Service
(UMTS).



4.1 GSM SYSTEM ARCHITECTURE:


Mobile Station (MS)



Mobile Equipment (ME)


Subscriber Identity Module (SIM)


Base Station
Subsystem (BSS)

Base Transceiver Station (BTS)

Base Station Controller (BSC)


Network Switching Subsystem(NSS)

Mobile Switching Center (MSC)

Home Location Register (HLR)

Visitor Location Register (VLR)

Authentication Center (AUC)

Equipment Identity Register

(EIR)


GSM ARCHITECURE DIAGRAM:


Interfaces:

Um= Interface between MS and BTS.

Abis= Interface between BTS and BSC.

A= Interface between BSC and MSC.

B= Interface between MSC and VLR.

C= Interface between GMSC and HLR.

D= Interface between

VLR and HLR.





E= Interface between MSC and other MSC.

F= Interface between MSC and EIR.

G= Interface between VLR and other VLR
.



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Figure1:GSM architecture






a) Mobile Station (MS):

The Mobile Station is made up of two
entities:

1.

Mobile Equipment (ME)

:


It is a
portable,

vehicle mounted or handheld
device.
It is uniquely identified by IMEI (International
Mobile Equipment Identity).

Functions:


1.

It monitors power and signal quality of surrounding cells for optimum handover.



2. Used for voice and data transmission.

2. Subscriber

Identity Module (SIM)



In the GSM Network, the SIM card identifies the user. The SIM is a small memory device
,

which

contains the identification numbers of the user (IMSI) and a list of available networks .The SIM card also
contains tools needed for authentication and ciphering.


4.2 MULTIPLE ACCESS TECHNOLOGIES

4.2.1 Frequency Division Multiple Access (FDMA):



In the FDMA system, one specific frequency is allocated to one user engaged in a call. When there are
numerous calls, the network tends to get overloaded, leading to failure of the system. In a full
-
rate (FR) system,
eight time slots (TS) are ma
pped on every frequency, while in the half
-
rate (HR) system, sixteen TSs are
mapped on every frequency (Figure 2).














Figure 2:

FDMA


4.2.2 Time Division Multiple Access (TDMA) :


TDMA systems divide whole transmission time into time slots, and in each slot only one user is
allowed to either transmit or receive. TDMA shares a single carrier frequency with several users, where each
user makes use of non
-
overlapping time slots.

Each
TRX handles one carrier frequency and can be a hopping
carrier frequency or a fix carrier frequency. If the carrier frequency is hopping it continuously changes between
different radio frequencies. This is done to reduce the interference with other channel
s and cells (Figure
3
).



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Figure
3
:TDMA



In order to multiply users per carrier frequency the GSM uses Time Division Multiple
Access
(
TDMA). The TRXs divides the time in 8 Time Slots (TS) of a length of approximately 0.577ms. Very

simplified you can say that one user uses one time slot to make a call in GSM. One period of 8 TSs is called a
TDMA Frame and has the length of approximately 4.615 ms. In each cell one of the TRXs, called C0 has to
configure one of its TSs to the Broadcas
t Control Channel (BCCH) and is not allowed to hop, this TS is referred
to as TS0. A TS configured to carry the BCCH cannot be used for speech or data sessions. Due to frequency
hopping the rest of the TSs of the TDMA Frame can be able to use frequency hop
ping depending on what
technique is used. Each TS on a TDMA frame is referred to as a physical channel.


4
.2.3 Code Division Multiple Access (CDMA) :



Code
-
division multiple access combines modulation and multiple access to achieve a
certain degree

of
information efficiency and protection. Initially developed for military applications, it gradually developed into a
system that gave the promise of better bandwidth and service quality in an environment of spectral congestion
and interference.







Figure
4
:CDMA



In this technology, every user is assigned a separate codes depending upon the transaction. One user
may have several codes in certain conditions. Thus, separation is not based on frequency or time, but on the
basis of codes. These codes
are nothing but very long sequences of bits having a higher bit rate than the original
information. The major advantage of using CDMA is that th
ere is no plan for frequency ref
use, the number of
channels is greater, there is optimum utilization of bandwidt
h, and the confidentiality of information is well
protected

(Figure 4)
.



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4
.3 LOGICAL CHANNELS IN GSM NETWORK:



Fig
5 channels in GSM network

4
.3.1 Traffic Channels (TCH
)




TCH transport user information (speech/data)



-

TCH are
bidirectional dedicated channels between the network and the MH

4
.3.2 Broadcast Channels (BCH)



-
To help the MH (Mobile Handset) measures



-

to turn to a BTS




-

to listen for the cell information



-

to start roaming, waiting for calls to arr
ive, making calls




-

Because BTSs are not synchronized with each other, every time a MH decides to camp to another
cell, its FCCH, SCH, and BCCH must be read.

4
.3.3Common Control Channels (CCCH)


CCCH support the establishment of a dedicated communica
tion path (dedicated channel) between the
MH and the BTS
.
Three types of CCCH

1. Paging Channel (PCH)

2. Random Access Channel (RACH)

3. Access Grant Channel (AGCH)

4
.3.3.1Paging Channel (PCH)


-
Used by BTS to page particular MH in the cell


-
MH actively l
isten to PCH to check contact information within certain time

-
Contact could be incoming call or short message


-
Contact information on PCH include

-
IMSI (MH’s identity number), or TMSI (temporary number)

-
Transmit on down
-
link , point to point

4
.3.3.2Access Grant Channel (AGCH)


-
The network assigns a signaling channel via AGCH

-
A Stand alone Dedicated Control Channel (SDCCH) is assigned

-
Transmit on down
-
link, point to point

4
.3.3.3Random Access Channel (RACH)


-
Used by MH to request a dedicated

channel for call setup