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INTRODUCTION

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CHAPTER 1

INTRODUCTION

1.1 ABOUT THE PROJECT

We propose localization schemes for wireless sensor networks. Soft computing

plays a crucial role in localization schemes. Here we consider the edge weight of

each anchor node separately and combine them to compute the location of sensor

node. The edge weights are modeled by the fuzzy logic system (FLS) . Then

consider localization as a single problem and approximate the entire sensor

location mapping from the anchor node signals by a neural network(NN). The

stimulation and experimental results demonstrate the effectiveness of the proposed

schemes by comparing them with previous methods.

1.2 Problem Statement

In this paper, two range-free localization schemes based on RSS information are

presented. They employ soft computing techniques to overcome the limitations of

previous range-free localization methods. In the first scheme, the localization is

decomposed into a collection of individual problems in which we compute the

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proximity of a sensor node to each anchor node. That is, we consider the edge

weight of each anchor node separately and combine them to compute the location

of the sensor nodes. The edge weights are modeled by the fuzzy logic system

(FLS). Contrary to the first scheme, we consider the localization as a single

problem in the second scheme and approximate the whole mapping from the

anchor node signals to the locations of sensor nodes by a neural network (NN).

1.3 Objective

The main objective is to Limit hardware such as Storage, Processing,

Communication, Energy supply (battery power) ,Limited support for

networking such as Peer-to-peer network, Unreliable communication,

Dynamically changing, Limited support for software development, Real-time

tasks that involve dynamic collaboration among nodes, Software architecture

needs to be co-designed with the information processing architecture .

CHAPTER 2

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LITERATURE REVIEW

2.1 Localization in Cooperative Wireless Sensor Networks: A Review

Localization in Wireless Sensor Networks has become a significant research

challenge, attracting many researchers in the past decade. This paper provides a

review of basic techniques and the state-of-the-art approaches for wireless sensors

localization. The challenges and future research opportunities are discussed in

relation to the design of the collaborative workspaces based on cooperative

wireless sensor networks.

2.2 Localization in Wireless Sensor Networks: A Probabilistic Approach

In this paper we consider a probabilistic approach to the problem of localization in

wireless sensor networks and propose a distributed algorithm that helps unknown

nodes to determine confident position estimates. The proposed algorithm is RF

based, robust to range measurement inaccuracies and can be tailored to varying

environmental conditions. The proposed position estimation algorithm considers

the errors and inaccuracies usually found in RF signal strength measurements. We

also evaluate and validate the algorithm with an experimental testbed. The test bed

results indicate that the actual position of nodes are well bounded by the position

estimates obtained despite ranging inaccuracies.

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2.3 Robust Node Localization for Wireless SensorNetworksThe node localization problem in Wireless Sensor Networks has received

considerable attention, driven by the need to obtain a higher location accuracy

without incurring a large, per node, cost (dollar cost, power consumption and form

factor). Despite the eorts made, no system has emerged as a robust, practical,

solution for the node localization problem in realistic, complex, outdoor

environments. In this paper, we argue that the existing localization algorithms,

individually, work well for single sets of assumptions. These assumptions do not

always hold, as in the case of outdoor, complex environments. To solve this

problem, we propose a framework that allows the execution of multiple

localization schemes. This \protocol multi-modality" enables robustness against

any single protocol failure, due to its assumptions. We present the design of the

framework, and show a 50% decrease in localization error in comparison with state

of art node localization protocols. We also show that complex, more robust,

localization systems can be build from localization schemes that have limitations.

2.4 SOFT COMPUTING IN WIRELESS SENSORS NETWORKS

The embedded soft computing approach in wireless sensor networks is suggested.

This approach means a combination of embedded fuzzy logic and neural networks

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models for information processing in complex environment with uncertain,

imprecise, fuzzy measuring data. It is generalization of soft computing concept for

the embedded, distributed, adaptive systems.

2.5 A Probabilistic Fuzzy Approach for Sensor Location Estimation

in Wireless Sensor Networks

Nowadays, wireless sensor networks are widely used in a variety of applications

such as in military, vehicle tracking, disaster management and environmental

monitoring. Accurate estimation of the sensor position can be crucial in

many of these applications. In this research, we propose a localization algorithm

based on probabilistic fuzzy logic systems (PFLS) for range-free localization. The

algorithm utilizes received signal strength (RSS) from the anchor nodes

embedded with variant degrees of environmental noise. The proposed system is

compared with another algorithm based on Fuzzy Logic Systems (FLS) against

variant amount of noise. Simulation results demonstrate that FLS can be much

more accurate than PFLS method if the environment is noise-free. However, as the

environmental noise increases, the PFLS reaches better performance.

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SYSTEM ANALYSIS

CHAPTER 3

SYSTEM ANALYSIS

3.1 EXISTING SYSTEM

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Bulusu et al. proposed a range-free, proximity-based, and coarse-grained

localization algorithm in Bulusu et al. In this method, the anchor nodes broadcast

the their position (Xi,Yi) and each sensor node computes its position as a centroid

of the positions of all the connected anchor nodes to itself by

where (Xest,Yest) represents the estimated position of the sensor node and N is the

number of the connected anchor nodes to the sensor node. This scheme is simple

and economic but exhibits large amount of error. Kim and Kwon proposed an

improved version of Bulusu et al. In this method, anchor nodes are weighted

according to their proximity to the sensor nodes, and each sensor node computes

its position by

This method has the weakness that the choice of the weights (w1,w2,. . .,wn), is

very heuristic, and the performance highly depends on the design of the weights.

3.2 PROPOSED SYSTEM

We propose two intelligent localization schemes for wireless sensor

networks (WSNs). The two schemes introduced in this paper exhibit

range-free localization, which utilize the received signal strength

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(RSS) from the anchor nodes. Soft computing plays a crucial role in

both schemes. In the first scheme, we consider the edge weight of

each anchor node separately and combine them to compute the

location of sensor nodes. The edge weights are modeled by the fuzzy

logic system (FLS) . This proposal leads to following advantages such

as, Detection: Improved signal-to-noise ratio by reducing average

distance between source and sensor, Energy: A path with many short

hops has less energy consumption than a path with few long hops,

Robustness: Address individual sensor node or link failures . This

system is used under following applications, Environmental

monitoring ,Traffic, habitat, security, Industrial sensing and

diagnostics, Manufacturing, supply chains ,Context-aware computing,

Intelligent homes, Military applications: Multi-target tracking,

Infrastructure protection: Power grids

3.3 SYSTEM SPECIFICATION

3.3.1 HARDWARE SPECIFICATION

Hard disk : 40 GB

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RAM : 512 MB

Processor Speed : 3.00GHz

Processor : Pentium IV Processor

3.3.2 SOFTWARE SPECIFICATION

OS : Windows XP

Language : MatLab

Version : 2009

3.4 SOFTWARE DESCRIPTION

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MATLAB is a high-level language and interactive environment that enables

you to perform computationally intensive tasks faster than with traditional

programming languages such as C, C++, and Fortran. You can use MATLAB in a

wide range of applications, including signal and image processing,

communications, control design, test and measurement, financial modeling and

analysis, and computational biology. Add-on toolboxes (collections of special-

purpose MATLAB functions, available separately) extend the MATLAB

environment to solve particular classes of problems in these application areas.

MATLAB provides a number of features for documenting and sharing your work.

You can integrate your MATLAB code with other languages and applications, and

distribute your MATLAB algorithms and applications.

3.4.1 Key Features

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High-level language for technical computing

Development environment for managing code, files, and data

Interactive tools for iterative exploration, design, and problem solving

Mathematical functions for linear algebra, statistics, Fourier analysis,

filtering, optimization, and numerical integration

2-D and 3-D graphics functions for visualizing data

Tools for building custom graphical user interfaces

Functions for integrating MATLAB based algorithms with external

applications and languages, such as C, C++, Fortran, Java, COM, and

Microsoft Excel.

MATLAB is a well-respected software environment and programming

language created by MathWorks and now available directly from Agilent as

an option with most signal generators, signal analyzers, and spectrum

analyzers. MATLAB extends the capabilities of Agilent signal analyzers and

generators to make custom measurements, analyze and visualize data, create

arbitrary waveforms, control instruments, and build test systems. It provides

interactive tools and command-line functions for data analysis tasks such as

signal processing, signal modulation, digital filtering, and curve fitting.

MATLAB has over 1,000,000 users in diverse industries and disciplines,

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and it is a standard at more than 3,500 colleges and universities worldwide.

Three MATLAB configurations are available and range from basic

MATLAB capabilities that allow acquisition and analysis of data to full

support for signal processing, communications, filter design, and automated

testing.

3.4.2 MATLAB Capabilities

Extend the functionality of Agilent signal and spectrum analyzers with

MATLAB by analyzing and visualizing measurements, testing modulation

schemes, and automating measurements

Excite electronic devices using Agilent signal generators with simple or

complex waveforms created in MATLAB

Test the functionality of electronic devices by making measurements with

Agilent instruments and comparing them against known baselines in

MATLAB

Develop a GUI or application that enables users to perform data analysis or

testing

Characterize an electronic device to determine how closely it matches the

design

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Verify new algorithms or measurement routines using live data from Agilent

instruments

Design custom digital filters in MATLAB and apply them to signals

acquired from an Agilent instrument.

3.5 INTRODUCTION TO FUZZY LOGIC & FUZZY CONTROL

* "Fuzzy logic" has become a common buzzword in machine control. However,

the term itself inspires a certain skepticism, sounding equivalent to "half-baked

logic" or "bogus logic". Some other nomenclature might have been preferable, but

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it's too late now, and fuzzy logic is actually very straightforward. Fuzzy logic is a

way of interfacing inherently analog processes, that move through a continuous

range of values, to a digital computer, that likes to see things as well-defined

discrete numeric values.

For example, consider an antilock braking system, directed by a microcontroller

chip. The microcontroller has to make decisions based on brake temperature,

speed, and other variables in the system.

The variable "temperature" in this system can be divided into a range of "states",

such as: "cold", "cool", "moderate", "warm", "hot", "very hot". Defining the

bounds of these states is a bit tricky. An arbitrary threshold might be set to divide

"warm" from "hot", but this would result in a discontinuous change when the input

value passed over that threshold.

The way around this is to make the states "fuzzy", that is, allow them to change

gradually from one state to the next. You could define the input temperature states

using "membership functions" such as the following:

With this scheme, the input variable's state no longer jumps abruptly from one state

to the next. Instead, as the temperature changes, it loses value in one membership

function while gaining value in the next. At any one time, the "truth value" of the

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brake temperature will almost always be in some degree part of two membership

functions: 0.6 nominal and 0.4 warm, or 0.7 nominal and 0.3 cool, and so on.

The input variables in a fuzzy control system are in general mapped into by sets of

membership functions similar to this, known as "fuzzy sets". The process of

converting a crisp input value to a fuzzy value is called "fuzzification".

A control system may also have various types of switch, or "ON-OFF", inputs

along with its analog inputs, and such switch inputs of course will always have a

truth value equal to either 1 or 0, but the scheme can deal with them as simplified

fuzzy functions that are either one value or another.

Given "mappings" of input variables into membership functions and truth values,

the microcontroller then makes decisions for what action to take based on a set of

"rules", each of the form:

IF brake temperature IS warm AND speed IS not very fast

THEN brake pressure IS slightly decreased.

In this example, the two input variables are "brake temperature" and "speed" that

have values defined as fuzzy sets. The output variable, "brake pressure", is also

defined by a fuzzy set that can have values like "static", "slightly increased",

"slightly decreased", and so on.

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This rule by itself is very puzzling since it looks like it could be used without

bothering with fuzzy logic, but remember the decision is based on a setof rules:

All the rules that apply are invoked, using the membership functions and

truth values obtained from the inputs, to determine the result of the rule.

This result in turn will be mapped into a membership function and truth

value controlling the output variable.

These results are combined to give a specific ("crisp") answer, the actual

brake pressure, a procedure known as "defuzzification".

This combination of fuzzy operations and rule-based "inference" describes a

"fuzzy expert system".

Traditional control systems are based on mathematical models in which the

the control system is described using one or more differential equations that

define the system response to its inputs. Such systems are often implemented

as "proportional-integral-derivative (PID)" controllers. They are the products

of decades of development and theoretical analysis, and are highly effective.

If PID and other traditional control systems are so well-developed, why

bother with fuzzy control? It has some advantages. In many cases, the

mathematical model of the control process may not exist, or may be too

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"expensive" in terms of computer processing power and memory, and a

system based on empirical rules may be more effective.

Furthermore, fuzzy logic is well suited to low-cost implementations based

on cheap sensors, low-resolution analog-to-digital converters, and 4-bit or 8-

bit one-chip microcontroller chips. Such systems can be easily upgraded by

adding new rules to improve performance or add new features. In many

cases, fuzzy control can be used to improve existing traditional controller

systems by adding an extra layer of intelligence to the current control

method.

Fuzzy controllers are very simple conceptually. They consist of an input

stage, a processing stage, and an output stage. The input stage maps sensor

or other inputs, such as switches, thumbwheels, and so on, to the appropriate

membership functions and truth values. The processing stage invokes each

appropriate rule and generates a result for each, then combines the results of

the rules. Finally, the output stage converts the combined result back into a

specific control output value.

The most common shape of membership functions is triangular, although

trapezoids and bell curves are also used, but the shape is generally less

important than the number of curves and their placement. From three to

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seven curves are generally appropriate to cover the required range of an

input value, or the "universe of discourse" in fuzzy jargon.

As discussed earlier, the processing stage is based on a collection of logic

rules in the form of IF-THEN statements, where the IF part is called the

"antecedent" and the THEN part is called the "consequent". Typical fuzzy

control systems have dozens of rules.

Consider a rule for a thermostat:

IF (temperature is "cold") THEN (heater is "high")

This rule uses the truth value of the "temperature" input, which is some truth

value of "cold", to generate a result in the fuzzy set for the "heater" output,

which is some value of "high". This result is used with the results of other

rules to finally generate the crisp composite output. Obviously, the greater

the truth value of "cold", the higher the truth value of "high", though this

does not necessarily mean that the output itself will be set to "high", since

this is only one rule among many.

In some cases, the membership functions can be modified by "hedges" that

are equivalent to adjectives. Common hedges include "about", "near", "close

to", "approximately", "very", "slightly", "too", "extremely", and

"somewhat". These operations may have precise definitions, though the

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definitions can vary considerably between different implementations.

"Very", for one example, squares membership functions; since the

membership values are always less than 1, this narrows the membership

function. "Extremely" cubes the values to give greater narrowing, while

"somewhat" broadens the function by taking the square root.

In practice, the fuzzy rule sets usually have several antecedents that are

combined using fuzzy operators, such as AND, OR, and NOT, though again

the definitions tend to vary: AND, in one popular definition, simply uses the

minimum weight of all the antecedents, while OR uses the maximum value.

There is also a NOT operator that subtracts a membership function from 1 to

give the "complementary" function.

There are several different ways to define the result of a rule, but one of the

most common and simplest is the "max-min" inference method, in which the

output membership function is given the truth value generated by the

premise.

Rules can be solved in parallel in hardware, or sequentially in software. The

results of all the rules that have fired are "defuzzified" to a crisp value by

one of of several methods. There are dozens in theory, each with various

advantages and drawbacks.

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The "centroid" method is very popular, in which the "center of mass" of the

result provides the crisp value. Another approach is the "height" method,

which takes the value of the biggest contributor. The centroid method favors

the rule with the output of greatest area, while the height method obviously

favors the rule with the greatest output value.

3.5.1 What Are Fuzzy Inference Systems?

Fuzzy inference is the process of formulating the mapping from a given input to an

output using fuzzy logic. The mapping then provides a basis from which decisions

can be made, or patterns discerned. The process of fuzzy inference involves all of

the pieces that are described in the previous sections: Membership Functions,

Logical Operations, and If-Then Rules. You can implement two types of fuzzy

inference systems in the toolbox: Mamdani -type and Sugeno- type. These two

types of inference systems vary somewhat in the way outputs are determined. See

the Fuzzy inference systems have been successfully applied in fields such as

automatic control, data classification, decision analysis, expert systems, and

computer vision. Because of its multidisciplinary nature, fuzzy inference systems

are associated with a number of names, such as fuzzy-rule-based systems, fuzzy

expert systems, fuzzy modeling, fuzzy associative memory, fuzzy logic controllers,

and simply (and ambiguously) fuzzy systems.

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Mamdani's fuzzy inference method is the most commonly seen fuzzy

methodology. Mamdani's method was among the first control systems built using

fuzzy set theory. It was proposed in 1975 by Ebrahim Mamdani as an attempt to

control a steam engine and boiler combination by synthesizing a set of linguistic

control rules obtained from experienced human operators. Mamdani's effort was

based on Lotfi Zadeh's 1973 paper on fuzzy algorithms for complex systems and

decision processes. Although the inference process described in the next few

sections differs somewhat from the methods described in the original paper, the

basic idea is much the same.

Mamdani-type inference, as defined for the toolbox, expects the output

membership functions to be fuzzy sets. After the aggregation process, there is a

fuzzy set for each output variable that needs defuzzification. It is possible, and in

many cases much more efficient, to use a single spike as the output membership

function rather than a distributed fuzzy set. This type of output is sometimes

known as a singleton output membership function, and it can be thought of as a

pre-defuzzified fuzzy set. It enhances the efficiency of the defuzzification process

because it greatly simplifies the computation required by the more general

Mamdani method, which finds the centroid of a two-dimensional function. Rather

than integrating across the two-dimensional function to find the centroid, you use

the weighted average of a few data points. Sugeno-type systems support this type

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of model. In general, Sugeno-type systems can be used to model any inference

system in which the output membership functions are either linear or constant.

3.5.2 Fuzzify Inputs

The first step is to take the inputs and determine the degree to which they belong to

each of the appropriate fuzzy sets via membership functions. In Fuzzy Logic

Toolbox software, the input is always a crisp numerical value limited to the

universe of discourse of the input variable (in this case the interval between 0 and

10) and the output is a fuzzy degree of membership in the qualifying linguistic set

(always the interval between 0 and 1). Fuzzification of the input amounts to either

a table lookup or a function evaluation.

This example is built on three rules, and each of the rules depends on resolving the

inputs into a number of different fuzzy linguistic sets: service is poor, service is

good, food is rancid, food is delicious, and so on. Before the rules can be

evaluated, the inputs must be fuzzified according to each of these linguistic sets.

For example, to what extent is the food really delicious? The following figure

shows how well the food at the hypothetical restaurant (rated on a scale of 0 to 10)

qualifies, (via its membership function), as the linguistic variable delicious. In this

case, we rated the food as an 8, which, given your graphical definition of delicious,

corresponds to = 0.7 for the delicious membership function.

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3.5.3 Apply Fuzzy Operator

After the inputs are fuzzified, you know the degree to which each part of the

antecedent is satisfied for each rule. If the antecedent of a given rule has more than

one part, the fuzzy operator is applied to obtain one number that represents the

result of the antecedent for that rule. This number is then applied to the output

function. The input to the fuzzy operator is two or more membership values from

fuzzified input variables. The output is a single truth value.

As is described in Logical Operations section, any number of well-defined

methods can fill in for the AND operation or the OR operation. In the toolbox, two

built-in AND methods are supported: min (minimum) and prod (product). Two

built-in OR methods are also supported: max (maximum), and the probabilistic OR

method probor. The probabilistic OR method (also known as the algebraic sum) is

calculated according to the equation

probor(a,b) = a + b - ab

In addition to these built-in methods, you can create your own methods for AND

and OR by writing any function and setting that to be your method of choice.

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3.5.4 Apply Implication Method

Before applying the implication method, you must determine the rule's weight.

Every rule has a weight (a number between 0 and 1), which is applied to the

number given by the antecedent. Generally, this weight is 1 (as it is for this

example) and thus has no effect at all on the implication process. From time to

time you may want to weight one rule relative to the others by changing its weight

value to something other than 1.

After proper weighting has been assigned to each rule, the implication method is

implemented. A consequent is a fuzzy set represented by a membership function,

which weights appropriately the linguistic characteristics that are attributed to it.

The consequent is reshaped using a function associated with the antecedent (a

single number). The input for the implication process is a single number given by

the antecedent, and the output is a fuzzy set. Implication is implemented for each

rule. Two built-in methods are supported, and they are the same functions that are

used by the AND method: min (minimum), which truncates the output fuzzy set,

andprod(product), which scales the output fuzzy set.

3.5.5 Aggregate All Outputs

Because decisions are based on the testing of all of the rules in a FIS, the rules

must be combined in some manner in order to make a decision. Aggregation is the

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process by which the fuzzy sets that represent the outputs of each rule are

combined into a single fuzzy set. Aggregation only occurs once for each output

variable, just prior to the fifth and final step, defuzzification. The input of the

aggregation process is the list of truncated output functions returned by the

implication process for each rule. The output of the aggregation process is one

fuzzy set for each output variable.

As long as the aggregation method is commutative (which it always should be),

then the order in which the rules are executed is unimportant. Three built-in

methods are supported:

max (maximum)

probor (probabilistic OR)

sum (simply the sum of each rule's output set)

3.5.6 Defuzzify

The input for the defuzzification process is a fuzzy set (the aggregate output fuzzy

set) and the output is a single number. As much as fuzziness helps the rule

evaluation during the intermediate steps, the final desired output for each variable

is generally a single number. However, the aggregate of a fuzzy set encompasses a

range of output values, and so must be defuzzified in order to resolve a single

output value from the set.

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Perhaps the most popular defuzzification method is the centroid calculation, which

returns the center of area under the curve. There are five built-in methods

supported: centroid, bisector, middle of maximum (the average of the maximum

value of the output set), largest of maximum, and smallest of maximum.

Fig.1 An example of the fuzzy logic system (FLS).

CHAPTER 4

SYSTEM DESIGN

4.1 Modules

Fuzzy Rule Generation

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Node Distribution

Neural Network

Localization

1. Fuzzy Rule Generation

Fuzzy Input Variable:

X RSS from Anchor Node

Range [0,Rssmax]

Output Y Edge weight of each anchor node for a given sensor node.

Range- [0,max ]

Rule base about Edge Weight:

Rule if :RSS then :Weight

1 verylow verylow

2 low low

3 medium medium

4 High high

5 veryhigh veryhigh

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Node Distribution:

Determination of width and length of the area where the sensor nodes are to be

placed. This is done under the following condition

x(j+(i-1)*10)=(i-1)*10;

y(j+(i-1)*10)=(j-1)*10;

Neural Network:

1.Set thenumber of network replications.

2.Set number of hidden units in network.

3. Set the Total number of iterations

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4. Set the number of epochs for network.

5.Load the rules generated in Fuzzy to neural network.

6. Implement Cluster structure.

7. Compute the global training and test errors

Fig. 2. An example of the MLP neural networks.

Each sensor node finds its position by the following procedures:

STEP1: Find the adjacent anchor nodes using connectivity.

STEP2: Collect the IDs and positions of anchor nodes and measure

their RSSs.

STEP3: Calculate the edge weight of each anchor node. The edge

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weight should already be modeled by FLS. The FLS modeling

is explained in the subsequent subsections.

STEP4: Use the edge weights of all anchor nodes to determine

the position of sensor node by .,More specifically, when the positions of the anchor

nodes are (X1,Y1), (X2,Y2), . . ., (Xk,Yk), the position of the sensor node is

estimated as

Back Propagation:

A back propagation neural network uses a feed-forward topology, supervised

learning,

and back propagation learning algorithm. Back propagation is a general

purpose learning algorithm. It is powerful but also expensive in terms of

computational requirements for training. A back propagation network with a

single hidden layer of processing elements can model any continuous

function to any degree of accuracy (given enough processing elements in the

hidden layer).

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Fig- 3: Back Propagation Network

There are literally hundreds of variations of back propagation in the neural

network

literature, and all claim to be superior to basic back propagation in one

way or the

other. Indeed, since back propagation is based on a relatively simple form of

optimization known as gradient descent, mathematically astute observers

soon proposed modifications using more powerful techniques such as

conjugate gradient and Newtons methods. However, basic back

propagation is still the most widely used variant. Its two primary virtues are

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that it is simple and easy to understand, and it works for a wide range of

problems. The basic back propagation algorithm consists of three steps.

The input pattern is presented to the input layer of the network. These inputs

are

propagated through the network until they reach the output units. This

forward pass

produces the actual or predicted output pattern. Because back propagation

is a supervised learning algorithm, the desired outputs are given as part of

the training vector. The actual network outputs are subtracted from the

desired outputs and an error signal is produced. This error signal is then the

basis for the back propagation step, whereby the errors are passed back

through the neural network by computing the contribution of each hidden

processing unit and deriving the corresponding adjustment needed to

produce the correct output. The connection weights are then adjusted and

the neural network has just learned from an experience. Two major

learning parameters are used to control the training process of a back

propagation network. The learn rate is used to specify whether the neural

network is going to make major adjustments after each learning trial or if it is

only going to make minor adjustments. Momentum is used to control

possible oscillations in the weights ,which could be caused by alternately

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signed error signals. While most commercial back propagation tools provide

anywhere from 1 to 10 or more parameters for you to set, these

two will usually produce the most impact on the neural network training time

and

performance.

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Localization

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Calculate the deviation between the real position & estimated position of

sensor nodes .

Fig. 5. Distribution of all nodes for the simulation.

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Fig. 6. Simulation result of the proposed overall method by NN: (a) result of

location estimation; (b) error result.

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CONCLUSION:

Two intelligent range-free localization schemes for wireless sensor networks

(WSNs) are presented. In our proposed methods, the sensor nodes do not need

any complicated hardware to obtain the distance or TOA/AOA information.

The sensor nodes can estimate their positions with only RSS information

between itself and its neighbor anchor nodes. Soft computing techniques play

the crucial role in our proposed schemes. In the first scheme, we consider the

edge weight of each anchor node which is the neighbor of the sensor nodes

separately and combine these edge weights to compute the location of sensor

nodes.

Future Work :

The future work includes adapting the proposed localization methods to the

noisy indoor environment and reduces the time requirement to optimize the FLS

and to train the NN.

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