Integrating IoT Sensory Inputs For Cloud Manufacturing Based Paradigm

F

MULTI SENSOR INPUT FUSION IN

WSN FOR AUTOMATED SORTING IN

A CLOUD BASED PLASTIC

RECYCLING PLANT

A THESIS SUBMITTED TO AUCKLAND UNIVERSITY OF TECHNOLOGY

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SERVICE-ORIENTED COMPUTING

Supervisors

Prof. Alan Litchfeild

June 2016

By

Kavita Pillai

School of Engineering, Computer and Mathematical Sciences

Copyright

Copyright in text of this thesis rests with the Author. Copies (by any process) either

in full, or of extracts, may be made only in accordance with instructions given by the

Author and lodged in the library, Auckland University of Technology. Details may be

obtained from the Librarian. This page must form part of any such copies made. Further

copies (by any process) of copies made in accordance with such instructions may not

be made without the permission (in writing) of the Author.

The ownership of any intellectual property rights which may be described in this

thesis is vested in the Auckland University of Technology, subject to any prior agreement

to the contrary, and may not be made available for use by third parties without the

written permission of the University, which will prescribe the terms and conditions of

any such agreement.

Further information on the conditions under which disclosures and exploitation may

take place is available from the Librarian.

© Copyright 2016. Kavita Pillai

2

Declaration

I hereby declare that this submission is my own work and

that, to the best of my knowledge and belief, it contains no

material previously published or written by another person

nor material which to a substantial extent has been accepted

for the qualification of any other degree or diploma of a

university or other institution of higher learning.

Signature of candidate

3

Acknowledgements

I would like to thank my mother and my husband for their unparalleled support.

4

Abstract

The first step in thermoplastic recycling is identifying the plastic waste categorically.

This manual task is often inefficiency and costly. This study therefore analyzes the

problem and presents a automatic classifier based on a WSN infrastructure. The

classifier fuses data from two different sources using Kalman filter and neural network.

The algorithm is run on a matlab simulator to test the results.

5

Publications

Title of the first publication publication details

Title of the second publication publication details

Title of the third publication publication details

And so on publication details

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Contents

Copyright 2

Declaration 3

Acknowledgements 4

Abstract 5

Publications 6

1 Introduction 10

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.2 Thermoplastic type, identifiers, parameters: . . . . . . . . . . . . . . . . 12

1.3 Hardware architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

1.4 Software architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2 Literature Review 31

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.2 Autonomic Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.3 IoT, WSN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.3.1 Wireless sensory network . . . . . . . . . . . . . . . . . . . . . 40

2.3.2 BaseStation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2.3.3 Routing in IoT sensory network . . . . . . . . . . . . . . . . . 42

2.3.4 Communication with base station . . . . . . . . . . . . . . . . . 45

2.4 Data Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

2.4.1 Advantages of data fusion . . . . . . . . . . . . . . . . . . . . . 45

2.4.2 Motivation of sensor fusion . . . . . . . . . . . . . . . . . . . . 46

2.4.3 Issues with Data fusion . . . . . . . . . . . . . . . . . . . . . . . 47

2.5 Sensor fusion methodologies . . . . . . . . . . . . . . . . . . . . . . . . 47

2.5.1 PNN Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

2.5.2 Kalman filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

2.6 Clusters and data aggregation . . . . . . . . . . . . . . . . . . . . . . . . 49

2.6.1 Event detection and processing . . . . . . . . . . . . . . . . . . 50

2.6.2 Localization of sensor objects . . . . . . . . . . . . . . . . . . . 50

2.7 Semantic Sensor N/W Ontology . . . . . . . . . . . . . . . . . . . . . . . 51

7

3 Method 57

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

3.2 Problem investigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.2.1 Problem investigation . . . . . . . . . . . . . . . . . . . . . . . . 58

3.2.2 Treatment design . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

3.2.3 Treatment implementation and evaluation . . . . . . . . . . . . 62

4 Analysis 65

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

4.2 Supervised learning classifier . . . . . . . . . . . . . . . . . . . . . . . . 66

4.3 Feature Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.4 Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4.4.1 Feature fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4.4.2 Decision fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.4.3 Final PNN Classification . . . . . . . . . . . . . . . . . . . . . . . 71

5 Discussion 82

5.0.1 Factors that could effect the accuracy of the algorithm . . . . . 83

6 Conclusion and future study 86

6.0.1 Future studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

References 90

8

List of Tables

1.1 Difference between a plastic polymer and a Thermoplastic polymer

taken from . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

1.2 Acceptable ranges for polymer attributes adapted from . . . . . . . . . 17

2.1 Assumptions to cloud manufacturing . . . . . . . . . . . . . . . . . . . 33

9

List of Figures

1.1 Thermoplastic structure on heating adapted from . . . . . . . . . . . . 15

1.2 Thermoplastic structure on cooling adapted from . . . . . . . . . . . . 16

1.3 Figure 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

1.4 Sensor Fusion Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

1.5 Fused Feature matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

1.6 Sensor Fusion based PNN classifier . . . . . . . . . . . . . . . . . . . . 29

1.7 Sensor Fusion flow chart . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.1 Feedback control process in autonomic system . . . . . . . . . . . . . . 53

2.2 Figure 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

2.3 PNN Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

2.4 Semantic sensor network ontology representation . . . . . . . . . . . . 55

2.5 Semantic sensor network ontology representation . . . . . . . . . . . . 56

3.1 Data fusion architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.2 Problem investigation approach . . . . . . . . . . . . . . . . . . . . . . 59

3.3 Layered Approach architecture . . . . . . . . . . . . . . . . . . . . . . . 60

3.4 Layered Approach architecture . . . . . . . . . . . . . . . . . . . . . . . . 61

3.5 Multisensor system design process adapted from . . . . . . . . . . . . . 62

3.6 Figure 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.1 Data in PCA after feature fusion . . . . . . . . . . . . . . . . . . . . . . 67

4.2 Classifier structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.3 Data Fusion Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.4 Data Fusion Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

4.5 Difference between normalized and actual values of merged data adap-

ted from . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

4.6 Smoothing of tensile and temperature data . . . . . . . . . . . . . . . . 75

4.7 Difference in state d and d’ of multi-sensor data . . . . . . . . . . . . . 76

4.8 Fusion of tensile and temperature data . . . . . . . . . . . . . . . . . . 76

4.9 Difference between normalized and actual values of merged data . . . 77

4.10 Plotting of original image . . . . . . . . . . . . . . . . . . . . . . . . . . 78

4.11 Texture and contour generation . . . . . . . . . . . . . . . . . . . . . . 79

4.12 Pixel-wise comparison of source and target . . . . . . . . . . . . . . . 80

4.13 Center of gravity and density of the target . . . . . . . . . . . . . . . . . 81

10

4.14 Pixel-wise comparison of source and target . . . . . . . . . . . . . . . . 81

5.1 Temperature data before smoothing . . . . . . . . . . . . . . . . . . . . 83

5.2 Temperature data after smoothing . . . . . . . . . . . . . . . . . . . . . 84

5.3 Tensile yield before and after smoothing . . . . . . . . . . . . . . . . . 85

6.1 Pattern in Semantic sensor network taken from . . . . . . . . . . . . . . 88

6.2 Overview of the Semantic Sensor Network ontology classes and prop-

erties taken from . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

11

Chapter 1

Introduction

1.1 Introduction

All plastic products including bottles pose a serious threat to environment due to massive

consumption of petroleum for plastic manufacturing, their non-biodegradable nature,

and their low density to volume ratio. This is the elementary reason behind plastics

being the major part of municipal waste throughout the world. In the past decade

intensive research has been dedicated to, developing and providing efficient solutions

for plastic bottle recycling. The research in this area attempt to 1) Increase recyclable

plastic consumption worldwide 2) In particular recyclable thermoplastic

Thermoplastic is made from polymer resin that melts into homogenized liquid when

heated and turns hard when cooled (Jumaidin, Sapuan, Jawaid, Ishak & Sahari, 2016).

These type of plastics can be re-molded, reheated, and repeatedly solidified, thereby

making it recyclable. Major types of thermoplastic polymer used in today’s industries

include: 1) Acrylonitrile butadiene styrene (ABS): these are used for manufacturing

sports equipment, and automobile parts. 2) Polycarbonate used for manufacturing

compact discs, drinking bottles (PET bottles), storage containers etc. 3) Polythene:

used to make shampoo bottles, plastic grocery bags etc.(Jumaidin et al., 2016), (Coskun,

12

Chapter 1. Introduction 13

2015) The interest in recycling thermoplastic is due the fact that it is a crucial component

of everyday use. Furthermore, a single unit of usage (for e.g. a single plastic bottle)

might be composed of several types of thermoplastic. Since most forms of thermoplastic

resin polymer can be reused (Deng, Djukic, Paton & Ye, 2015) majority of research

dealing with sustainable usage of plastic for environmental conservation deals with

combining information technology, sensors, and web based control modules to generate

autonomous systems for plastic recycling .

The very first process in recycling plastics is sorting the plastic based on color, shape

roughness. Currently most of the plants throughout the world use manual processes for

plastic recycling (Peres, Pires & Oréfice, 2016), wherein trained operatives assist in

classifying the polymer of plastic based on color and shape. This approach however

is both expensive and ineffective. The reasoning behind this primitive approach in

sorting the plastic components is that recycling systems lack human perception (Peres

et al., 2016). This means that a human sensory receptors are capable of combining

the signals and data from the body (this includes sight, touch, smell, sound, and taste)

through which, even with a vague knowledge of our surrounding environment we can

dynamically create, visualize and update models of real world objects. Therefore, this

study considers the use-case scenario of automatic thermoplastic bottles classification

system based on combination of chemical property of the resin, colour and texture.

The hardware architecture of this thesis will be composed of three sensors including:

1) computer vision in spectra, 2) NIR (near infrared spectroscopy, and 3) inductive

sensors. The software architecture will be based on statistical classifiers and design to

achieve multi sensor data fusion. The purpose of this study is to develop a sustainable

classification solution for plastic recycling plants using the advancement in the fields of

web services, sensors, internet of things, and cloud computing.


1.2 Thermoplastic type, identifiers, parameters:

In order to create a sorting algorithm, it is of the utmost importance to know the

identifying characteristic of thermoplastics and the unique combination of attributes that

differentiates a type of thermoplastic from another (Oliva Teles, Fernandes, Amorim &

Vasconcelos, 2015). In modern consumer market it can be found that there exist many

forms of thermoplastics used on a daily basis. Each of the variety has got a mix of

parameters that give it a unique look and feel.

Conventional Thermoplastics are known as the two phase system (Deng et al., 2015).

The classifying property being the temperature (Lionetto, Dell’Anna, Montagna &

Maffezzoli, 2016). The first phase being the temperature required for a thermoplastic to

reach its liquid resin form (Otheguy, 2010). The second phase being the temperature

required for it to reach it solidified form. Therefore, the very first identifier parameter is

the temperature range. Some of the other properties that will help in the classification

process are: 1) Tensile Properties: tensile properties of a thermoplastic polymer deal

with the elasticity of the polymer. They describe to what extend a thermoplastic resin can

be stretched. Test may indicate how a polymer will perform in actual usage condition

(H. Zhang et al., 2015). 2) Tensile at Break: Also known as the ultimate tensile. This

indicate the point at which a thermoplastic polymer will break. The force required to

stretch the polymer until it beaks is measured in units (typically pounds) per square

inch or psi. It can also be presented as megaPascals (MPa)(H. Zhang et al., 2015).

Thermoplastic polymers with higher tensile at break values will be more difficult to

break by stretching compared to its lower value counterparts. 3) Tear strength: this

represents how resistant the thermoplastic polymer is against tearing. This property

is somewhat similar to tensile at break except for a propagation point. This point

symbolizes the force at which the polymer tears completely. Therefore, tensile at break

refers to point where the polymer starts breaking and the tensile strength refers to the end


point when the polymer is completely torn. The unit of measurement for this attribute

is typically given in psi or kiloNewtons per meter (kN/m) (H. Zhang et al., 2015). 4)

Tensile Modulus: In this test the polymer is stretched over a range of elongated points

and its stretching is measure over range of elongations. The unit of measurement for

this parameter is the percentage of the original length of the thermoplastic polymer.

For e.g. the outcome could be 50, 100, or 300 percent of the original length of the

polymer resin. Thermoplastic polymer that has a strong tensile strength (elasticity) may

become weaker as it elongates this is also known as necking (H. Zhang et al., 2015).

5) Elongation at break. This measures the stretching length of the polymer before it

breaks. This is also measured in terms of percentage of the original strength. A soft

polymer will have a higher value than when it hardens (H. Zhang et al., 2015).

In case of thermoplastic polymers there are certain factors that depend on the

methods in which thermoplastic resins are molded and the mold used that effect the

tensile properties (Jumaidin et al., 2016). It is for this reason that both direction of

flow and its transverse are measure for tensile properties of thermoplastic polymers. 1)

Direction of flow: the orientation of thermoplastic polymers on molding greatly effects

its tensile properties. The tensile properties may vary greatly depending on the time

when the polymer was stretched. It must be determined if the polymer is stretched in

the direction of polymer flow or in the transverse direction during the molding process

(Coskun, 2015). 2) Extruded or injection based plaques. It is very important to conduct

test on outcomes of a similar plaque (i.e. compare the outcomes of extrusion plaque

with another of the same type of plaque, as comparing the same with an intrusion based

mold will lead to an ambiguous outcome) (Xie et al., 2016).

Compression set for thermoplastic polymers: A compression set is the permanent

deformation that occurs to the polymer when it is exposed to a specific temperature.

The test performed on thermoplastic polymers to check for compression set deformities

is called the ‘ASTM’ or the ‘ASTM-D395’. This method for testing defines that normal


range of compression in a thermoplastic polymer should be around 25- 30 percent. In

any recycling plant, this test is conducted to check the true positive deformed units

and the sampling is done every half hour. The samples chosen as true positives are the

ones that fail to reach its original height. This deformity occurs at a specific time, in a

specific temperature (Otheguy, 2010).

Hardness in Thermoplastic polymer: The relative hardness or softness is the primary

property to be considered while classifying the thermoplastic polymer. Hardness is

also closely related to other properties of polymers such as tensile qualities and flexural

modulus. The hardness in polymer are commonly measured by an instrument called as

the Shore durometer. A metal indenter is pushed in the surface of the hardened polymer

using a spring measuring how far it penetrates. The depth penetration can be in a range

of 0 to 0.100 inches. Here zero indicates that the indenter is at the maximum depth and

value of 100 indicates that no penetration is detected. The readings can be taken either

immediately or after a certain delay. Readings taken immediately are always higher

than the delayed reading. The delayed reading represents the resiliency of the polymer

along with the hardness (H. Zhang et al., 2015).

Flexural modulus is the measurement of resistance to bending in a thermoplastic

polymer. This property is closely related and is often confused with hardness which

typically measures resistance to indentation. The two properties of hardness and flexural

modulus have direct correlation (meaning if the value of hardness goes up, so does

flexural modulus). Both the property deal with how a thermoplastic polymer feel in

customer’s hands(H. Zhang et al., 2015).

How does a thermoplastic polymer differ from ordinary plastic material? Thermo-

plastic polymers repeatedly soften or melt when heated and solidifies when cooled.

Thermoplastic can therefore burn to some degree. Temperatures of thermoplastic poly-

mers can vary greatly with the type and grade of the polymer. In most thermoplastic

polymers molecular chains can represented as loosely coupled, intertwining strings


resembling a spaghetti. Figure 1 shows the structure of the molecules on heating.

Figure 1.1: Thermoplastic structure on heating adapted from

(Thermoplastic Elastomer (TPE) FAQs, 2015)

On cooling the molecules are held together more firmly as shown in figure 2.

Some of the basic distinguishing factors are between thermoplastic and conventional

plastic polymers given in table 1

Shrinkage in thermoplastic polymers: When the polymers harden they shrink the

overall size of the molded part. Even shrinkage levels can reduce the deformities of

the molded thermoplastics. Shrinkage normally relates to molding and removal of

final thermoplastic (for e.g. a bottle form). The shrinkage level should always be even

(H. Zhang et al., 2015).


Figure 1.2: Thermoplastic structure on cooling adapted from


Therefore based on the above attributes table 2 gives a the range of acceptable

values for recycling of thermoplastic polymer based on for the training dataset for our

multisensory environment.

Shrinkage in thermoplastic polymers: When the polymers harden they shrink the

overall size of the molded part. Even shrinkage levels can reduce the deformities of the

molded thermoplastics. Shrinkage normally relates to molding and removal of final

thermoplastic (for e.g. a bottle form). The shrinkage level should always be even.

Therefore based on the above attributes table 2 gives a the range of acceptable

values for recycling of thermoplastic polymer based on for the training dataset for our

multisensory environment.


Table 1.1: Difference between a plastic polymer and a Thermoplastic polymer taken

from


VARIABLE Thermoplastic polymer Plastic polymer

Fabrication Rapid (seconds) Slow (Minutes)

Scrap Reusable High Percentage,waste

Curing Agents None Required

Machinery Conventional,Thermoplastic Equipment Special Vulcanizing,Equipment

Additives Minimal or None Numerous Processing,Aids

Design Optimization Unlimited Limited

Remold Parts Yes Unlikely

Heat Seal Yes No

Table 1.2: Acceptable ranges for polymer attributes adapted from

(Otheguy, 2010)

Physical,Characteristic Min Value Max,Value

Gravity 1.20,N/Kg 1.20,N/Kg

Molding,Shrinkage Flow 0.10% 0.30%

Tensile,Strength NA13800

psi

Flexural,Modulus NA 870000,psi

Flexural,Strength NA 23200,psi

Mold,Temperature 149,degrees Fahrenheit 203 degrees,Fahrenheit

Cooling,Temperature 194 degrees,Fahrenheit 212 degrees,Fahrenheit

Cooling,time 4,hours 6,hours


1.3 Hardware architecture

In order to design an adequate system for thermoplastic classifier we need to give

consideration to the following questions. 1) The type of sensors to be used. 2) How

do we integrate these sensory inputs to form a singular model for the classifier? 3)

Designing the prototype for presenting the designed solution.

Classification of thermoplastic requires us to take into account data from different

devices, as a single sensor data will not give us sufficient information to successful

classify a polymer into a type. Therefore, we require the conjunction of different sensors

each of which, gives us data for a single attribute (e.g. temperature). This process of

combining data from different sensor is called sensor fusion. Each of the sensor used

in this study has a specific task (identifying attributes) associated with the automated

classifier.

1) CCD camera sensor: This sensor has two-fold use in our classifier firstly it can be

used to identify the gravity of the object in its perception. And secondly it can be used

to identify the color and texture associated with the thermoplastic shard processed by

it. Out of the three types of thermoplastic materials considered in this study i.e. ABS,

PET, and polythene, especially PET bottles are characterized by its solid texture and is

available in different shapes and colors. Therefore, this sensor can be used to correctly

identify the type of thermoplastic based on color texture and shape (Chen et al., 2016)

(S. Wang et al., 2016) (Tzschichholz, Boge & Schilling, 2015).

2)Inductive sensors: Inductive sensors are a type of non-contact proximity sensors

useful for identifying the contents of the plastic shards without physically touching

the components. In particular, this sensor is useful in identifying components which

have been metallized. This sensor consists of induction loop, the inductance of which

changes in accordance to the material contents of the plastic shards. The changes in

the loop inductance reflects in current flow fluctuation which is captured by the sensors


(C. Wang, Liu & Chen, 2015) (Chang et al., 2016).

3)NIR sensors: Near infrared spectroscopy or NIR sensors is a device to measure

density and case of thermoplastics it can be used to measure and study the transmittance

or absorption between different plastics when exposed to infrared laser. The NIR

sensors can also measure surface temperature and particle state calibration in polymer

resins. NIR sensor output is multivariate. It is often used to extract specific features

of polymer resins like surface temperature, shrinkage, hardness etc. (Beguš, Begeš,

Drnovšek & Hudoklin, 2015) (Müller, Burger, Borisov & Klimant, 2015) (Iyakwari,

Glass, Rollinson & Kowalczuk, 2016)

4)Polymer FBG sensors: A fiber optic sensor uses optical fiber for either sensing

or as a means to relay signals from sensor to electronic measuring unit. Fiber optic

sensors are mostly used to measure strain, temperature, pressure among other qualities.

It provides a very distributed sensing over large distances. Polymer FBG sensor is an

extension of fiber optic sensor. It is very attractive due to its many qualities such as

flexibility, high strain measurement range, and low density. It can also vary the intensity

of light and can used for remote sensing. In case of thermoplastic resin, polymer based

fiber bragg grating sensor can be used to measure all tensile properties and temperatures.

5)Hyperspectral system: A spectral image is used to collect and process information

across an electromagnetic spectrum. This means that hyperspectral image identifies a

unique spectrum for each pixel in the scanned object. Its purpose is to identify certain

characteristics of materials and is a part of detecting processes. The human perception

spectrum is capable of classifying light in mostly 3 bands namely green, blue, and

red), however a spectral image classifies a spectrum in many more bands including

those which are not visible to human eye. The simplest form of hyperspectral system

is a combination of monochrome camera and hyperspectral lens and a spectrograph.

(Kouyama et al., 2016) (Li, Chen, Zhao & Wu, 2015) (Castorena, Morrison, Paliwal &

Erkinbaev, 2015)


1.4 Software architecture

Multisensory data fusion can be processed at four different levels of categorization

in accordance with the stage at which fusion takes place. This can be at signal level,

spectrum level, feature level, and decision level as illustrated by Figure 1.3 on page 26.

1) Signal level fusion: this occurs through combination of signals from different

sensors to create a new fused signal having a better signal to noise ratio compared to

the original (AO, YONGCAI, LU, BROOKS & IYENGAR, 2016). 2) Spectrum level

fusion: information associated with each point is derived from a matrix of associated

features from different sensors and is plotted on a spectral image to create a model

interpreted by a computing system for further evaluation(AO et al., 2016). 3) Feature

level fusion: information from objects recognized by the sensors in the form of salient

features are extracted and placed in a matrix from. The salient features depend on the

environment example of the features could be edges or textures. These features extracted

from different sensors are fused and placed in feature matrix (cross ref later)(AO et al.,

2016). 4) Decision level fusion: These include fusion based on lower level categories

stated above (including signal, spectrum as well as feature level extraction) to yield a

final fused decision. The thermoplastic contents of a recycling plant are firstly classified

based on lower level fusion based on features and spectra. The individual extraction

results are finally combined applying decision rules to complete the sorting process(AO

et al., 2016) (Duro et al., 2016)(Mönks, Trsek, Dürkop, Geneiß & Lohweg, 2016).

In this study all levels of fusion except signal fusion are used and form the important

part in formulating the fusion algorithm. We consider this study through wireless sensory

network (WSN) and cloud manufacturing platform. In a cloud based manufacturing

set up there is bound to be large number of sensors. A large number of sensors are

randomly distributed in the deployment area. The sensors are randomly distributed in

the deployment setup. The sensor are categorically organized into clusters based on


their type and spatial proximity. Each cluster has a cluster head, which are responsible

for synchronizing and collaborating the fusion activity and transferring data to base

station. The Sensor nodes collect the following information

1 CCD camera: Collect shape, color (Tzschichholz et al., 2015) 2 NIR sensor:

Surface temperature. (Hafizi, Epaarachchi & Lau, 2015) (Müller et al., 2015) 3 In-

ductive sensors: Collect component information specifically metallic content in the

shards.(Ding, Chen, Ma, Chen & Li, 2016) 4 Polymer FBG sensor: Tensile properties of

resin. A general block diagram of the fusion process mentioned above is shown in 1.4.

The sensor nodes collect data D1, D2, D3 from the environment. The data value from

these nodes may not be precisely true. There exists redundancies and noise in the data

generated by the sensor nodes. These uncertain data can be generated by sensors either

due to manufacturing or environmental conditions. These uncertainties can adversely

affect the decision fusion and efficient sensor power consumption in a wireless sensor

network (Mishra, Lohar & Amphawan, 2016) (Pereira, McGugan & Mikkelsen, 2016) .

This study uses a hybrid of Kalman filter and probabilistic neural network. Data

fusion itself uses the Kalman filters, while feature extraction and decision fusion as

mentioned in (above ref) occurs through PNN derivatives. The algorithm process flow

for the entire process is as follows: Step 1 Preprocess data: The data should be free

of inconsistencies and errors. PNN training step is used here. The training data uses a

predictor variable. This is shown in equation (Oliva Teles et al., 2015) 1.1 as follows:

Φ = d′ − d (1.1)

The predictor variable differentiates the data captured by sensor to the normalized

value of the data. This represent the proximity of captured value to the actual value.

Here d’ is the normalized value shown by training previous data sets and d is captured


value. Therefore, percentage of correct data packets received by the equation 1.2. Here

C is the correct percent of packets received. While N is the total number of events

processed.

C =Φ

N× 100 (1.2)

Step 2: Feature extraction is commonly used in structural analysis of dynamic

components. It plays an important role in improving the performance of the classifier

in sorting of thermoplastic according to their features. Since frequency and its relative

modal parameters are relatively simple to extract from structural characteristics and re-

sponses received from sensory devices. Normalized frequency is a unit of measurement

corresponding to frequency. It is represented in continuous time variable t. Normalized

frequency is represented as cycles/sample, therefore in our classifier this is represented

by seconds/ samples (number of samples). There are three delimiters to our feature

extraction (Land et al., 2012).

1.Frequency change ratio: The first delimiter measures the changes in the com-

ponents in sensor data. This given by equation 1.2 this measures the changes in the

structural components being classified.(Land et al., 2012)

1.3

NFCRi =FFCi

∑nj−1FFCj

(1.3)

2.The second measures the changes in modal points in the sensor data. This is the

measure of accurate dimensionality of the classifier. This is shown in equation 1.3.

(Iounousse, Er-Raki, El Motassadeq & Chehouani, 2015)

FMCRi =MCRi(k)

∑nj−1 ∣MCRj(k)∣

(1.4)

3. The curvature change ratio showed by equation 1.4 shows difference in structures


of the sensed components.

NCCDj =∣Cui(j) −Cdi(j)∣∑ ∣Cui(j) −Cdi(j)∣

(1.5)

Here FFCi, MCRi are the frequency change ratio, modal change ratio derived from

sensory data training.

Step 3: Feature level data fusion: The features extracted are then fed in to individual

matrices. The fused matrix is then calculated. This is a well-rounded matrix representing

all information for the classifier to successfully decide the grade and category of plastic.

Example of this process through the example of our thermoplastic classifier is seen in

figure 1.6 and the fused feature matrix example is shown in Figure 1.5.

Step 4: FIS induced decision fusion: This step shows the proposed data fusion

process takes place. Decision theory and making appropriate decision plays an important

role in decision fusion. Due to complex situations encountered in multisensory fusion

process. Selection of appropriate criterion for derivation of the most optimal path

by selecting an estimation/filtering algorithm is a lengthy and complicated process in

itself. Decision fusion concerns mainly with how to make decisions and/or the most

appropriate decision to be taken given a status of an object, occurrence of an event or a

given scenario. This is usually done on basis of some objective analysis. Sometimes a

decision is taken on the basis of data extracted from sensors and the processing of these

datasets.(Tong, Liu, Zhao, Chen & Li, 2016)

There are two types of decision processes(Rawat & Rawat, 2016). First is the

normative decision making process: This describes how decision should be made and

not how they are actually made. The other type is descriptive decision making. The

main difference of former from the latter is the element of artificial intelligence or

AI. This entails how human think and their rationale. This AI has several facets these

are (i)logical decision making, (ii) perception, (iii) planning, (iv) learning, and (v)


action. The descriptive decision making also includes 1) Identifying the actual issue/

current situation 2) ability to collect data/ necessary details pertaining to the problem.

3) ability to arrive at intermittent solution / generating feasible processing option. 4)

conducting intermittent analysis on solution arrived at. 5)implementation of intermittent

solution. Take action to implement the final decision. For the purpose of this study we

use the descriptive decision making. The final decision model is used for successful

classification. The consequent processes to attain a successful decision fusion is further

discussed in this section.(Rawat & Rawat, 2016)

The sensors are embedded with a fuzzy logic controller. This purpose of a fuzzy

logic controller is to calculate the confidence level of collected data this depends on the

current condition of the sensor nodes. Each sensor node is responsible for collecting

the data and the confidence factor. The data fusion algorithm collects both data and the

confidence factor. In order to collect the confidence factor, signal to noise ratio of a

sensor input device is collected. Poor signal to noise ratio is indicative of poor sensor

health and therefore has a lower confidence factor. (Tian, Sun & Li, 2016)

The fuzzy logic controller determines if the sensor data and the signal to noise ratio

are in acceptable range (Mönks et al., 2016), this is determined through training data. A

sensor which is out of range will have confidence factor between 0 to 100 percent, the

confidence value of the sensor can be 100 percent only if its health and environmental

factors are desirable. A minimum acceptable value is decided for each sensory input

if the screened value is blow this acceptable range the value is rejected. For fusion

purposes the message packet will also include the node id of a source node (Mönks et

al., 2016).

DF =(CF1 ×D1) + (CF2 ×D2 + (CF3 ×D3) + (CF4 ×D4) + .... + (CFn ×Dn)

CF1 +CF2 +CF3 +CF4...CFn

(1.6)


Cluster heads are responsible for data aggregation from its subordinates. The multi

sensor fusion process is started by cluster heads at the completion of each round of data

collection process. The data aggregation process is shown in equation 1.5. Here Dn

represents the data received from similar cluster members CFn is the confidence factor

of data collected by each sensor node. Data fusion is conducted through combination of

data collected by different cluster heads. In other words, data fusion combines data with

higher certainty levels. Because sensor fusion combines data from different sources, it

provides a better view of the environment (Ari, Yenke, Labraoui, Damakoa & Gueroui,

2016), (Dehghani, Pourzaferani & Barekatain, 2015).

The example of the overall process is as follows for e.g. we consider data from three

temperature units, four tensile yield units and three component analysis units. Let the

temperature data collected be 10 degrees Celsius, 15 degrees Celsius, and 20 degrees

Celsius respectively having confidence factors of 75, 65, and 41 percent respectively.

The FD of temperature would be 15.93, similarly if FD of tensile yield and component

analysis is 49.8 and 26.8 respectively. The feature fusion matrix will have the values

15.98, 49.8, and 26.8 respectively. The feature fusion matrix from different sources

(cluster heads) are finally fused. After the fusion is successfully completed if there

is no change occurred at any clustered heads no packets are send to the base station,

alternately if there is occurrence of constant change the base station is alerted. The

successfully fused data is finally stored for training. The entire process is shown in

flowchart represented in Figure 1.7.

1

∏c

=1

(2n)n

2 σnexp[−

(x − xij)T (x − xij)

2σ2(1.7)

pi(x) =1

(2π)n

2 σn

1

Ni

(Ni)

∑(i=1)

exp[−(x − xij)T (x − xij)

2σ2] (1.8)


Ð→x k + 1 = A.Ð→x k +B.Ð→u k +w (1.9)


Figure 1.3: Levels of Data Fusion adapted from

(Duro, Padget, Bowen, Kim & Nassehi, 2016)


Figure 1.4: Sensor Fusion Process


Figure 1.5: Fused Feature matrix


Figure 1.6: Sensor Fusion based PNN classifier


Figure 1.7: Sensor Fusion flow chart

Chapter 2

Literature Review

2.1 Introduction

The advent of IoT, wireless capabilities and increasing number of devices with varying

features and potential is changing the business paradigm radically. Cloud computing

allows its users to tap the full potential of web services to create powerful and loosely

coupled infrastructure that are autonomous (Hao & Helo, n.d.).

Cloud based manufacturing (CBM) is scalable, agile infrastructure for manufac-

turing operations (Wu, Rosen, Wang & Schaefer, 2015b). CBM has a decentralized

structure and functions as a part of network. CBM consist of loosely coupled systems

working together in real time, the backbone of CBM’s contains, several enabling tech-

nologies like cloud computing, service oriented architecture (SOA) (Wu, Rosen, Wang

& Schaefer, 2015a).

It can therefore be inferred that cloud computing framework makes it possible to

collect, assemble and use IoT data. Therefore, cloud computing is the center of manu-

facturing network.There are three dimensions to cloud based manufacturing network

they are

34

Chapter 2. Literature Review 35

1 IoT (Internet of things) : This represents all the devices (and their sensors respect-

ively) connect to a cloud manufacturing network. This also includes the data

created by these devices.

2 IoS (Internet of Services) : This includes all the underlying services including

manufacturing services, cloud services and web-services in general. In gen-

eral manufacturing processes includes several components like raw material,

semi finished products, demand supply datum, product and process structure

information.In order to present a formal representation of these processes and

information(datum), in addition to the conventional compute, storage and network

included in IaaS, PaaS and SaaS, cloud manufacturing has many more services.

These may include integration as a service, simulation as a service, maintenance

as a service, design as a service (Schroth & Janner, 2007).

3 IoU (Internet of users): IoU are entities interacting with the system including service

providers (cloud service providers and others),operators, end users (consumers)

(Wu et al., 2015a).

Authors Wu et.al (Wu et al., 2015a) have presented eight requirements to cloud

manufacturing, these are mentioned in table 2.1 on the next page.These include a

cloud based shared storage, Internet connectivity ability to share data and information,

multitenancy architecture. This paper is based on the assumption that these requirements

are true and met for all cloud based manufacturing units.

The system architecture of cloud manufacturing follow a layered approach as follows

1 Perception layer.

2 Network layer.

3 Middleware layer.


Table 2.1: Assumptions to cloud manufacturing

Requirement Requirement description

R1

To connect individual service providers and consumers in the networked

manufacturing setting, a CBM system should support social media-based

networking services. Social media such as Quirky allows users to utilize

/leverage crowd-sourcing in manufacturing. In addition, social media does

not only connect individuals; it also connects manufacturing-

related data and information, enabling users to interact with a global

community of experts on the Internet.

R2

To allow users to collaborate and share 3D geometric data instantly, a CBM

system should provide elastic and cloud-based storage that allows files to be

stored, maintained, and synchronized automatically.

R3Should have an open-source programming framework that can process and

analyze big data stored in the cloud for e.g MapReduce.

R4

To provide SaaS applications to customers, a CBM system should support the

multitenancy architecture. Through multi-tenancy, a single software instance

can serve multiple tenants via a web browser. According to Numecent,

a cloud platform, called Native as a Service (NaaS), is developed to deliver

native Windows applications to client devices. In other words, NaaS can

“cloudify” CAD/CAM software such as Solidworks without developing

cloud-based applications separately. With such a multi-tenant platform, such

programs can be run as if they were native applications installed on the

user’s device.

R5

To allocate and control manufacturing resources (e.g., machines, robots,

manufacturing cells, and assembly lines) in CBM systems effectively and

efficiently, real-time monitoring of material flow, availability and capacity of

manufacturing resources become increasingly important in cloud-based process

planning, scheduling, and job dispatching. Hence, a CBM system should be able

to collect real-time data using IoT technologies such as radio-frequency identification

(RFID) and store these data in cloud-based distributed file systems

R6 Should provide IaaS, PaaS, HaaS, and SaaS applications to users

R7 Should support an intelligent search engine to users to help answer queries

R8Should provide a quoting engine to generate instant quotes

based on design and manufacturing specification


4 Application layer.

5 Business layer.

The lowest layer consists of sensors and sensory devices including RFID, GPS

cards etc., this layer is called as the perception layer.Second layer consists of network

modules like GPRS, Zigbee, 3G, infrared, blue-tooth this layer provides us means of

transmitting information perceived through sensor devices to middle-ware web services,

IoT gateways, user interface modules to capture the data transmitted and pass it onto

the next layer. Middle ware layer takes care of web services and their interaction, it

also includes database modules for storage example MongoDB, and other nosql,mysql

databases (Wu et al., 2015a). This layer also consist of ubiquitous computing and cloud

computing modules this is a very important module specially in cloud manufacturing

(Mitton, Papavassiliou, Puliafito & Trivedi, 2012) it allows the users to extract the data

from anywhere it can also be used to attain some degree of remote control features

as stated by(smart home). Application layer typically consists of user interfaces or

management applications that use or manipulate the data from lower layer.Business

layer is used for designing business process flows these include model data, flow charts

and graphs (Y. Zhang et al., 2012) .

The main features that characterize cloud manufacturing framework are 1)Data

availability 2)Complexity. These features make this framework a viable option for IoT.

Cloud manufacturing provides hi speed access to on demand resources, also it provides

quick storage options for sensory data. Cloud computing model of manufacturing

consist loosely coupled modules interacting with each other over a common backbone

(Wu et al., 2015a). Cloud manufacturing model due to on-demand compute and storage

are able to process large data-sets which is not possible in conventional manufacturing.

This manufacturing model follows an input-reply approach wherein a master node

divides the input task into sub-tasks which are then aligned to be process by child


nodes (Schaefer, 2014),(Hao & Helo, n.d.),(Kyriazis & Varvarigou, 2013) ,(Yang, Shi &

Zhang, 2014). This therefore allows complex operations to be assigned and completed

in parallel. On completion of a sub task the child node notifies the parent node of the

same. The correspondence between parent and the child nodes takes place through

HTTP codes as follows.

1 1XX level codes : represent informational notification (optional errors)

2 2XX level codes : indicates that the task was completed successfully by a sub node.

3 3XX level codes: This indicates redirection. It means that the node has several other

child nodes, and is waiting for its sub-tasks to get executed.

4 4XX level codes: This represents error-sets in child nodes.

5 5XX level codes : This represents errors in the parent nodes.

These error codes are analogous to Rest API HTTP codes used for web-services.

Another assumption to cloud manufacturing model is that the machines and devices in

one layer of hierarchy have the same make and configuration. This means that child

devices under one parent are identical to others. This makes the QoS of these machines

and devices quantifiable (Lartigau, Xu, Nie & Zhan, 2015),(Huang, Li & Tao, 2014).

From studies such as (Aydogdu, Akın & Saka, 2016),(Chakaravarthy, Marimuthu,

Ponnambalam & Kanagaraj, n.d.) , (Loubiere, Jourdan, Siarry & Chelouah, n.d.),

(Anuar, Selamat & Sallehuddin, n.d.) it can be inferred that an allocation optimization

algorithm like ABC (artificial bee colony) is more suited for loosely coupled system

requiring on precision in their operations. ABC algorithm gives the optimal task

allocation path in a cloud for manufacturing. In this algorithm there are three entities

1)worker nodes 2)scout nodes 3) and onlooker nodes (Anuar et al., n.d.).

The worker nodes do a job using one resource, there has to be a one to one rela-

tionship between a worker node and a resource. The onlookers are those nodes that are


waiting for a signal of completion from the worker nodes. According to the input-reply

approach mentioned above in 2.1 on the preceding page worker nodes can be considered

as master nodes and the onlooker nodes is the child node which is dependent on its

master to complete the processing of its jobs and then allocated the sub tasks to its

children, If the child nodes are non responsive the task are routed to other child nodes.

It is for this reason that all child nodes under a master node need to have a same make

(in terms of configuration and model of the machine they need to be exact replicas of

each other). The scout nodes are those master nodes which have encountered an error.

Master node on encountering an error gives a 5XX level error code. This masters job

are re routed to its other replicas, who then process the master’s job and communicate

with failed master’s children. This model is suitable for cloud manufacturing, as clouds

provide scalable, and on demand resources and routing facilities to under-allocated

reserves to speed up the operation(buyya cloud char).

Uncertainties in manufacturing operations can be defined as discrepancies between

desired output and the actual one (Hasani, Zegordi & Nikbakhsh, 2012a). Uncertainties

in manufacturing can be categorized as aleatoric and epistemic uncertainties. Aleatoric

uncertainties are represented by unknown outcomes in every run (Senge et al., n.d.).

This can be caused due human cognitive impairments that could result in ambiguities

and vagueness (Hasani, Zegordi & Nikbakhsh, 2012b). This type of uncertainty is

caused by inadequate, erroneous, missing data (Francalanza, Borg & Constantinescu,

2016). Epistemic uncertainties arise due to wrong modeling of data due to assump-

tions and approximation (Urbina, Mahadevan & Paez, 2011). In this paper we will

consider overall uncertainties and whether real time data available through wireless

sensory networks(made accessible by scalable cloud storage), can help the machines

to take decision (with the least impact to other production processes) in case any such

uncertainties arise (Hasani et al., 2012b), (Francalanza et al., 2016), (Hasani et al.,

2012a).


2.2 Autonomic Machines

In the words of (Buyya, Calheiros & Li, 2012) Autonomic systems are self-regulating,

self-healing, self-protecting, and self-improving. In other words, they are self-managing

. Many articles relating to autonomic computing (Parashar & Hariri, 2005), (Yahya,

Yahya & Dahanayake, n.d.) have compared this concept with human neural decision

making and rationalization. According to (Parashar & Hariri, 2005) human nervous

system could be cited as the best example of autonomic system present in nature, with

the help of sensory inputs the human nervous system is able to monitor and adapt to

changes both internal and external to itself.

According to (Maggio et al., 2012) Autonomic computing system must posses three

compulsory requirements

1 Automatic : This means that systems must be able to carry out their operations

without manual intervention.This inherently includes thin knowledge layer that

contains know how about this system (He, Cui, Zhou & Wang, 2016).

2 Adaptive : The ability of system to take decision when a uncertain event occurs.

This includes alterations to its state, configuration, functionality etc (He et al.,

2016), (Yahya et al., n.d.). In operation context this will include dealing with both

temporal and spatial changes/ uncertainties occurring on both long and short term

basis. It must be able to predict and anticipate demand on its resources to avoid

any down time.

3 Aware: An autonomic system must be aware of both the operational context and

its state as compared with its operative environment as a whole. In order to be

adaptive the machine or a system must be aware of its operative environment.This

means that a system must know itself in context of what resources are accessible,

why and how it is connected to other operatives (Parashar & Hariri, 2005),


(Maggio et al., 2012) .

Majority of the articles that deal with such self-adaptive, self-managing systems

such as (Maggio et al., 2012), (He et al., 2016), (Yahya et al., n.d.) manifest certain

common characteristics. These systems are often ’reflective’ this means that they posses

the ability to reason in context of their state and environment (White, Hanson, Whalley,

Chess & Kephart, 2004). Secondly these systems are able to rationalize, take decisions

during or in a close time-frame to run-time(while the machine or the system is running).

Based on the work of (White et al., 2004) certain paradoxical features to autonomic

computing are:

1 Uncertainty: These are presented in section 2.1 on the previous page

2 Environmental non determinism: this means that the environment in which the sys-

tem works requires different response in every run (therefore there is requirement

of decision making during run time).

3 Extra functional capabilities: This includes those features that may not be inherently

a part of the system,degree to which a machine combats the anomaly that is not a

part of its design. It is a representation of the system trade-offs that arise due to

unexpected outcome.

4 Incomplete control of system or its components: This could be due to embedded

components, human involvement in sub processes, or continual action.

Authors (Yahya et al., n.d.) have propounded that autonomic systems follow feedback

process. A feed back control loop is a continual process behind autonomic component’s

rationale and logic. These process’s are depicted through 4 canonical activities(collect,

analyze, decide and act) as shown in .


1 Collect: collection of data from sensors,events,probes. This involves collecting

data from executing processes in real time, Data collected includes contextual

information about component states (Schaefer, 2014).The data is filtered for any

noise(unwanted or redundant sources) and is stored for future use. This data can

be used to generate a trend pattern by comparing current and past states.

2 Analyze : the data stored is analyzed to generate inferences and symptoms.

3 decide : to resolve a future course of action or how to act when such a event arises

in the future.

4 Act: machine components need to be checked for determining which are the managed

components and how these can be manipulated( for e.g., by new algorithms or

parameter tuning). Are the changes to system pre-configured(the error has already

occurred in the past, was logged and resolved ) or are assembled, composed or

generated at runtime (opportunistically). This step involves acting on executing

processes and its context.

2.3 IoT, WSN

According to Gartner forecast "6.4 billion connected things will be in use worldwide

in 2016, up 30 percent from 2015, and will reach 20.8 billion by 2020. In 2016, 5.5

million new things will get connected every day". These IoT devices(things) consist of

many low power sensors(Y. Zhang et al., 2012) .

IoT sensor networks observe and monitor environments which change rapidly over

a period. These dynamic behavior is caused due to internal problems(that are integral

part of system design that requires a certain degree of elasticity) or they are cause by

external factors(unforeseen changes or behavior)(Chi, Yan, Zhang, Pang & Da Xu,

2014). IoT are typically composed of multiple, autonomous, small, cheap, and low


power sensor nodes. In a cloud manufacturing paradigm there nodes forward sensed

data to cloud storage (Soliman, 2014) and are then usually used for further processing.

The sensors could measure pressure, impact, temperature,thermal acoustics and many

other dynamic parameters within a industrial set up. IoT’s have a tremendous potential

for creating generic industrial machines to reduce fault positives and downtime faced

by machines.

In order to achieve this automation task we have to look at 1)event scheduling 2)

reliability of data( data should be without noise that change the pattern of learning

and prediction, non redundant data set) 3)Data availability 4)Data security 5)Data

aggregation 6)localization 7)clustering of nodes(IoT3) 8)fault detection. In the context

of this study:

1 Knowledge acquisiton (unbiased) in order to develop automation and enhance the

productivity and performance of machines in a industrial setup.The outcome of

this step would be the training data (Kyriazis & Varvarigou, 2013).

2 optimization: This is done by detecting and describing in consistencies through

the training data. Finding patterns of these in consistencies in training data and

generating algorithms for machines (White et al., 2004).

Machine learning will be suitable for machine automation through IoT sensor network

because

1 IoT sensors can monitor dynamic environment that is typified by frequently changing

data over time (Mitton et al., 2012). For e.g. A node’s current location may change

due eroding soil, landslides, weather turbulence due wind, or floods. It is desirable

to use sensors as they can adapt to this environment and operate efficiently.

2 IoT sensors can be used to collect new knowledge from unreachable locations. Using

this knowledge system designers can develop learning algorithms that are able to


calibrate itself to any newly acquired expertise (Kyriazis & Varvarigou, 2013) .

3 IoT sensors can be deployed in complicated machine structures where designers

cannot build accurate model of the system behavior. This means there is no

estimate to how a machine will behave at run-time.

4 Majority of the articles on this topic (m2miot, cmfd io2 iot3) propose a M2M

framework that use IoT but very few speak about using data, mining knowledge

and extracting important correlations from them. These correlations represent

learning trends to enable automation with minimum human intervention.

2.3.1 Wireless sensory network

Wireless sensor networks are heterogeneous distributed systems that primarily work

from data collected from a physical environment. In its most fundamental form, sensor

nodes are small autonomous, short range transceivers having sensors. These sensors

are able to cooperate and communicate with each other wirelessly (Gokturk, Gur-

buz & Erman, 2016). Sensor nodes sense (or gather) information about a physical

object. Although a single sensor node has only a limited range, a wireless sensor

network composing of many such similar nodes can provide detailed measurement

encompassing a large region (ref). Wireless sensor network has many applications

such as those in agriculture, nature monitoring (for e.g. monitoring active volcanoes,

forest fires etc.), industries (measuring temperature, pressure etc.), health monitoring

systems(Antonopoulos & Voros, 2016).

WSN is composed of mainly several sensor subsystems.

1)Sensor node is the most important element in a wireless sensory network. The

functionality of a sensor node includes sensing data, converting the raw physical to

digital form, interpret the communication protocol to send and receive data packets. In

order to achieve this, a sensor node must be equipped with certain physical resources.


The sensor resources can be further sub classified into four subsystems (Y. Wang, Chen,

Wu & Shu, 2016).

2)Sensor conversion units: To sense a value and convert them into digital form

which can be transmitted over a network (Y. Wang et al., 2016).

3)Sensor processing units: Storage of gathered and configured data in the local

memory, this also includes assembly functions to further process the gathered data,

information, or messages (Y. Wang et al., 2016).

4)Sensor interpreter unit: Interpret the received data frames according to protocol

and also assemble the data packets to be transmitted over a wireless network either to

other nodes or to the cluster head (Y. Wang et al., 2016).

5)Power unit: This unit is responsible for alerting the WSN monitoring interface

with sensor health diagnostics and the sensor battery status. This component regularly

alerts this message to cluster head to calculate the confidence factor (Y. Wang et al.,

2016).

Proper implementation and working of all subsystems can affect the network per-

formance. In wireless sensor networks the most common attributes adversely affecting

the performance of the network are the frame loss rate, bandwidth, QoS (quality of

service), and power consumption (Gokturk et al., 2016). Since some sensors are placed

in extremely hazardous, or too expensive it is not possible to connect these sensors

with the power grid and therefore sensors are equipped with batteries that have to be

frequently replaced (Antonopoulos & Voros, 2016)

Low power consumption of sensor nodes is crucial for several reasons, such as the

fact that sensor nodes are often placed in areas where there is no possibility (or it is too

expensive, dangerous etc.) of connecting sensor nodes to power grid (Edwards-Murphy,

Magno, Whelan, O’Halloran & Popovici, 2016). Therefore, they have to be equipped

with batteries, which have to be replaced with fresh ones occasionally. These periods

may differ from few days to years. As mentioned, sensor nodes are often placed in


areas with low accessibility, which leads us to the importance of power consumption

attribute when dealing with WSN. The battery status is also an important factor while

calculating the confidence factor (Edwards-Murphy et al., 2016).

2.3.2 BaseStation

A base station can be viewed as an entry point to a sensor network. A base station(BS)

is usually computationally more advanced than a sensor node (Arkin et al., 2014). A

base station processes larger memory (Could be connected to cloud storage) and may

be connected to a power grid. The base station is responsible for gathering data from

different computational cluster heads (Cayirpunar, Kadioglu-Urtis & Tavli, 2015). All

analysis of the content in a wireless sensor network is done by base stations. A BS also

handles, individual node configuration and routing (Gao, Zhang, Qi, Li & Tong, 2016).

The base station is usually composed of a wireless trans-receiver, monitoring in-

terface and could include a computing terminal station. A minimalistic design of a

wireless sensor network will include a monitoring module running on the terminal in the

base station that receives data to a database (Devaraju, Suhas, Mohana & Patil, 2015)

2.3.3 Routing in IoT sensory network

. In designing a sensory network corresponding to a cloud based manufacturing infra-

structure it is important to consider power and memory constraints of sensory nodes,

changes in topology, communication failures,localization of data, decentralization, node

clustering and data aggregation (Wu et al., 2015a), (Chi et al., 2014). To design a

routing protocol corresponding to IoT sensor networks it is mandatory to give con-

sideration corresponding design challenges like scalability (provided by cloud), data

coverage,energy consumption(of sensor nodes), fault tolerance e.t.c.(cloud mfgd 2).

Sensor nodes are inherently low power devices having limited processing capabilities,


low bandwidth and memory. There are two conventional methods of representing the

routing problem of sensory nodes in cloud network.

1 The cloud network is represent as a graph G where G = (V,E) hereV represents the

nodes and E represents bi-directional communication links. The optimum routing

is represented as the minimal cost of traveling all the nodes starting at source and

vising all the destination vertexes in the available graph edge.

2 A spanning tree T = (V,E) cost of which includes travelling to all of its childess

nodes. This solution is complex even when the entire topology is already known.

Machine learning makes it possible for a system(node) to learn from its experi-

ences, selecting the most effective route(low cost) and adapt to a dynamic environ-

ment.Learning to route dynamically is cheap and energy saving and prolongs the life of

cloud based IoT sensor networks(as it can adapt to any additions or changes). In his

paper (Guestrin, Bodik, Thibaux, Paskin & Madden, 2004) has mentioned the usage

of a distributed regression framework to model the routing problem. He propagates

that instead of transmitting to one another or outside a sensor node network the nodes

transmit only a subset of its parameters significantly reducing the communication

required.(Barbancho, León, Molina & Barbancho, 2007) have presented optimal routing

using self organizing maps(SOM). SOM is a neural network that uses unsupervised

learning to produce discretized representation of training data they call map. They use

Dijkstra’s algorithm to form the shortest path from network base station to every node

in network.

WSN composes of units with limited capabilities such as lower levels energy

bandwidth and storage capacities. An efficient routing algorithm must reflect these

shortcomings. Some commonly used routing protocols are as follows (Y. Wang et

al., 2016): 1)Flooding: In this type of routing a message to an uncertain location is

broadcast to all the nodes. The receivers of the message again broadcast the message to


all its neighboring nodes. The process is repeated until all nodes receive the message.

This type of routing while having a high latency, is suitable to system with low power

requirements.

2)Data centric routing: In this type of routing algorithm the sender of the sensed

data broadcast the metadata of a message (mostly the node id and the timestamp) as

an advertisement all nodes that do not have the data respond with a request. Finally,

the sender node only responds to these nodes. Another flavor of this protocol is direct

diffusion routing here the nodes interesting in a topic set their gradient to zero. Data is

send to these nodes only. Gradient increases on every resend.

3)Table driven routing: the routes of delivery of a message is stored in a table

are stored in a table. The sensor nodes fill the tables before they are actually used.

This approach in a wireless sensor network is very useful where there is low latency

requirement.

4) On demand routing: Routing paths for a message is decided only when needed.

This approach is the opposite of table driven routing which focuses on low energy

capacities instead of low latency. This method is better suited therefore is popularly

used in many wireless sensor network implementations.

5) Hierarchical routing: In this method similar sensor nodes are grouped together.

This group of similar sensor nodes are called a cluster. Each cluster has a cluster head,

the cluster head has advanced computational abilities and energy resources. A sensor

node will only send data to the cluster head, saving the bandwidth of its peers. Only a

cluster head handles the routing for the entire cluster. This approach efficiently handles

the problem of latency, bandwidth, and power constrictions.


2.3.4 Communication with base station

There are two model through which communication between the cluster heads and the

base station takes place (Cayirpunar et al., 2015) 1) Push model: The sensor nodes only

transmit data when they are ready. The base station passively listens over a sensory

network. 2) Pull model: In this approach the base station queries for data in a timely

manner. The sensor nodes respond to the query.

2.4 Data Fusion

Multi sensor data fusion is acumination of data from several sources to form a unified

model image. Multisensory fusion is used to provide the computing systems human like

perception although, achieving exact emulation of a human brain is still not possible.

Human beings can perceive objects attributes like depth, texture, colour, temperature

etc. simultaneously as a single model. Without physical touch or contact humans can

guess the correct dimensions of an object. Multi sensor fusion is currently used most

popularly in image and spectral fusion to generate perceptive imaging guidance systems

popularly used in robotic and navigation systems (Tong et al., 2016).

Joint directors of laboratories (JDL) defines data fusion as a “multilevel, multifa-

ceted process handling for automatic detection, association, correlation, estimation and

combination of data and information from several sources”. The data can be extracted

from a single or multiple sources (De Vin, Holm & Ng, 2010).

2.4.1 Advantages of data fusion

1) Robustness and reliability: fused data coming from multiple sources tend be redund-

ant which enables it to work in case of partial failure. One sensor can produce additional

dimension to sensor perception.


2) Increased confidence factor: Multiple sensors provide extended spatial and

temporal dimension to sensor data. This adds to increased confidence factor. It also

leads to data measured by one sensor be confirmed by measurements of other sensors

(Tong et al., 2016).

3) Reduced uncertainty and ambiguity

4) Reduces interference: By increasing the dimensionality of the measurement units,

the resultant system becomes less vulnerable against external interference.

5) Improved perception: The resultant value of a fused property has a better resolu-

tion than measurement taken by a single sensor.

2.4.2 Motivation of sensor fusion

The physical single sensor environment suffers from certain shortcomings. 1) Sensor

failure: the breakdown of a single sensor causes the complete loss of perception for a

physical object.(Mönks et al., 2016)

2) Spatial coverage: one sensor even a cluster of sensor node covers limited spatial

coverage. For e.g. temperature read from a sensor represents the unit measured in a

single read it may not be very accurate, it fails to render the average temperature of a

boiler.

3) Temporal coverage: Some sensors limit the maximum frequency of measurement,

as they need a particular time frame to finish.

4) Imprecision: Data measured from a single sensor over a period of time may not

be precise. A group of sensors measuring the data gives a more accurate reading 2.2 on

page 54.

5) Uncertainty: Uncertainty arises when sensors cannot measure all relevant at-

tributes with its perception range leading to missing and inconsistent values 2.2 on

page 54.


2.4.3 Issues with Data fusion

1) Imperfect, inconsistent and spurious data.

2)Correlation of data: Bias of data due to external uncontrollable data.

3)Data dimensionality: Compression loss of lower level sensors after data fusion

takes place.

2.5 Sensor fusion methodologies

2.5.1 PNN Classifier

Probabilistic neural network is a classifier based on the Bayesian classification theory

and estimation of probability density function. PNN is predominantly a classifier that

maps the multiple inputs to a number of classification. PNN classifier can be used to

process multiple inputs to generate decision ranges.

A probabilistic neural network consists of several probability density function

estimators for each of its classes. The classifier takes decision after calculating the

probability density function of each class using the training examples. The multi-

classifier decision is mathematically expressed as

pkfk > pjfj (2.1)

Here pk is the priori probability of occurrence from class k and the probability

density function for class k is represented by fk. This is called a neural network as it

maps in to the two-layer network.


Architecture of PNN

PNN consist of four layer 1)Input layer, 2)Pattern layer, 3) Summation layer, 4) decision

layer shown as in figure 2.3 on page 54

The input layer shows the multiple inputs to the classifier. The number of nodes in

the pattern layer is equivalent to number of classes in training instances. The input layer

is completely connected to the pattern layer. It does not do any further computation only

distributes input to neurons to pattern layer. The pattern layer is partially connected

to summation layer. The summation layer adds the input neurons from the pattern

corresponding to training instances selected.

How does PNN work

The input layer is propagated to the pattern layer. Once the pattern similar to input is

selected the output node are computed. In equation 2.2 n is the number of features

of input, sigma represents smoothing coefficient, xij is the training instance that cor-

responds to category c. PNN summation layer computes the probability of pattern x

being classified in class c by averaging the output of neurons belonging to the same

class.(Mahendran & Dhanasekaran, 2015)

1

∏c

=1

(2n)n

2 σnexp[−

(x − xij)T (x − xij)2σ2

(2.2)

Here X is the input instance

pi(x) =1

(2π)n

2 σn

1

Ni

(Ni)

∑(i=1)

exp[−(x − xij)T (x − xij)

2σ2] (2.3)

In equation 2.3, Ni denotes the total number of samples in classifier c the training

class will contain probabilities of losses associated with making incorrect decision.

This priori information will be used by the decision unit to calculate the output from


summation neurons. (Ben Ali, Saidi, Mouelhi, Chebel-Morello & Fnaiech, 2015)

2.5.2 Kalman filter

Kalman filter is an algorithm that uses temporal series of measurement containing noise.

It produces estimates of unknown units over a period of time that tend to be more precise

than those based on measurement in a single run. It also measures joint probability

distribution for a variable over a timeframe. Kalman filter inputs data from multiple

sensors to a matrix of internal states consisting of parameters of interest which relates

to linear dependencies between system states and corresponding inputs (Cheng, 2016).

Ð→x k + 1 = A.Ð→x k +B.Ð→u k +w (2.4)

Equation 2.4 shows the Kalman filter algorithm here vector xk shows the state of

system at a point of time represented by k. Vector uk is input to the system at time k.

Matrix B defines the relation between input vector uk and system state vector xk+1, w

is a random variable that represents noise. (Cheng, 2016)

2.6 Clusters and data aggregation

In a cloud based topology as shown in (Wu et al., 2015a) (Y. Zhang et al., 2012),

(Mitton et al., 2012) have a cluster architecture. Instead of communicating with the

nodes directly all the nodes on a cloud based network topology each of the node directly

communicates to the cluster head.A sensory cloud can have more than one cluster

head. The cluster head then assembles and aggregates the data this is known as data

aggregation. Cluster and data aggregation are very useful for machine learning because

1 Due to data aggregation, pool of data from all the sensors are available for interpola-

tion and extracting trend patterns from data.


2 Machine learning algorithms can be employed to select the most optimal(lower cost

for all nodes to interact) cluster head. Selecting an efficient cluster head will in

turn prolong life of the network (Schaefer, 2014), (Yang et al., 2014), (Soliman,

2014).

Few methodologies for clustering includes 1)using decision trees for selecting cluster-

heads (Craven & Shavlik, 2014). 2)Neural networks for large scale clustering (Rojas,

2013). 3)Using a self organizing map for data aggregation (Barbancho et al., 2007).

2.6.1 Event detection and processing

: Event detection is the corner stone of every sensory network.In context of cloud based

manufacturing there has be trustworthy event scheduling and detection . Machines in

the cloud based architecture must be able to detect and log events with minimum human

intervention (Khan, Khan, Zaheer & Khan, n.d.) (Wu et al., 2015a). Event processing

can be done through 1) Bayesian algorithms : This includes decentralization of a sensor

network zone (Liu, Yang, Ma & Cheng, 2015). 2) neural network (Yu, Wang & Meng,

2005) and K nearest neighbour (Jayaraman, Zaslavsky & Delsing, 2010).

2.6.2 Localization of sensor objects

: this involves determining geographical location of nodes and its components.In order

make the cloud based topology self aware, each node needs to know (or be aware) of

other components in its locality. For most large scale organizations this is done through

global positioning system. But in case of manufacturing firms which are indoors this

may not be a viable choice. Relative location is sufficient for certain purposes (Ahmed,

Shi & Shang, 2005) ,absolute localization involves the use of anchors to localize a node

in its absolute location (Ahmed et al., 2005). In this context certain terms used is:

1 Unknown node: This represents a node whose location cannot be determined.


2 beacon node: This node can recognize its placement either manually or through

a positioning hardware. This node serves as a beacon for recognizing other

unknown nodes.

3 Received signal strength indicators: This is a representation of distance to measure

transmission performance (of nodes)

2.7 Semantic Sensor N/W Ontology

Semantic layer is a thin information processing standard that extends W3C consortium,

these standards represent common protocol of information exchange between web

entities.

According to (Hao & Helo, n.d.), (Wu et al., 2015b), (Yang et al., 2014), (X. V. Wang,

Wang, Mohammed & Givehchi, 2016)a cloud manufacturing network consist of several

loosely coupled entities that are autonomous and self aware. In addition, machines in

a cloud manufacturing framework must be able to understand each other (talk with

each other, interpret contextual data relating to its functionality) (Brown, McMahon

& Sims Williams, 1995), (Young, Gunendran, Cutting-Decelle & Gruninger, 2007).

Semantics sensor network ontology is a set of tools for knowledge acquisition, structural

representation of logic and reasoning that are tailored for sensor based framework

(Lemaignan, Siadat, Dantan & Semenenko, 2006).

Sensory network ontology is based on OWL that uses XML like structure (elements

and attributes) with standardized meaning that is used to define nodes, their structure,

their relationships, and their functionality . The OWL an extension to RDF schema set

(Resource description framework) RDF is a data model for describing things and their

relationship (Papazoglou, 2012).

A basic structure describing a simple relationship between a printer(machine) and


its sub part is shown in 2.4 on page 55. The figure simply states that the print head

having the name ’CR322A’ is the sub part of the printer.

Figure 2.5 on page 56 depicts a complete representation of semantic sensory network

as adapted from (Pu, Wang, Du, Zhang & Chen, 2016). This shows diagrammatic repres-

entation of the ontology repository. This illustrates a sensor that measures temperature

and further provides information about measurement range (attributes of which consist

minimum and maximum temperature range, unit of measurementCelsius), measurement-

capability (having attributes accuracy rangemin, max values and interval), and system

information (giving platform representation, and address of physical instance).

As SOAP protocol,XML and REST web services are corner stone of cloud based

manufacturing as discussed by (Young et al., 2007), (Lemaignan et al., 2006). A

thin ontology layer that sits on top of its web service should be relatively easy to

define(Young et al., 2007).

By referring the literature described in this section. We will attempt to conduct

the study further to answer the following research questions. RQ1 as a result of the

advantages cloud manufacturing Shown in table 2.1 on page 33 can machines achieve

(at least partially) human-like perception (from the context of judging the depth, texture,

color and similar attributes of an object).

RQ2 Can these machines take autonomous decision based on the achieved sensory

perception


Figure 2.1: Feedback control process in autonomic system


Figure 2.2: Aspects of data fusion

Figure 2.3: PNN Classifier


Figure 2.4: Semantic sensor network ontology representation


Figure 2.5: Semantic sensor network ontology representation

Chapter 3

Method

3.1 Introduction

An integral part of designing a diverse system architecture is usage of a functional model

that clearly captures the guidelines of operation, system architecture, processing and

control functions within the data fusion process. Design science research methodology

has been found to successfully capture the salient features and diverse automatic,manual

approaches that are employed across different computing projects.Design science is a

solution oriented approach that is more suitable to agile projects requiring observational

studies and having a patterned evolution approach. In order to fully attain the fused state

a certain amount of reverse engineering is required as if the fusion is unsuccessful.The

data can relapse into its original state. The data fusion process is tightly coupled with

resource management this is an important feature that needs to be fulfilled in a data

fusion system.

Data science methodology follows the engineering cycle which is a series of recur-

ring steps that leads to a better functioning system (Keith, Demirkan & Goul, 2013).

The recursive steps in design science includes

1 Problem investigation.

61

Chapter 3. Method 62

2 Treatment design.

3 Treatment implementation.

5 Implementation evaluation.

3.2 Problem investigation

Figure 3.1: Data fusion architecture

as seen in figure 3.1, data fusion is fundamentally used to provide specific assessment

of the observed environment , this information can be further used to automate the

system or be used by the end user to plan and control the resources.

3.2.1 Problem investigation

A generalized approach to problem investigation for data fusion project is shown in 3.2

on the next page. This approach suits problem area like sensor fusion where a complete


Figure 3.2: Problem investigation approach

set of system goals is difficult to understand at the beginning of the design process

(Keith et al., 2013). In a poorly defined and understood environment such the design

science engineering approach will incorporate better solutions through data, or model

available in the system (Keith et al., 2013).

Layered design : This approach applies the concept of ’divide and conquer’ to the

problem area, specially when the kind of possible errors likely to be encountered by the

system in the future is uncertain. This concept merely states how the architecture needs

to be sub divided and the control needs to integrated to various sub working nodes. This

is shown in figure 3.3 on the following page.

This approach permits a cost effective method to achieve the rquired level of

performance. This is the common method used for designing and developing the multi-

sensor fusion approach and will be used in this paper. The higher level processes will be

handled by more computationally advanced nodes therefore, the users of this approach


Figure 3.3: Layered Approach architecture

can add any number of lower level nodes when required.

Paradigm based design: multi-sensor data fusion itself follows a top down approach,

while the control structure follows the top- down approach. This is a important principal

that will be used to design this system as it can be noted that sensor data from the lower

nodes are passed on to higher level nodes for fusing. This means that the data is flowing

from bottom towards top controlling nodes. Similarly, lower level sensors are not able

to compute data therefore these functions are done by higher level nodes.


Figure 3.4: Layered Approach architecture

3.2.2 Treatment design

The design for a sensor fusion system involves.Figure 3.5 on the next page shows the

design process. The design process includes

1 Data fusion algorithm design

2 Development and validation of the proposed algorithm.

Data fusion algorithm defines the processing of the fusion activity. This includes

1 Data association

2 Data alignment

3 Estimation of state(classification)

The concept of data association means how the sensor data is represented before and

after fusion this concept is also mentioned in 1.5 on page 28. Data association includes

1 Hypothesis generation:Indentify the sensors used and data generated to be fused

(Aher & Lobo, 2013)


Figure 3.5: Multisensor system design process adapted from

(Mönks et al., 2016)

2 Hypothesis evaluation: Calculate the metrics for fusion and how they will be fused

(Aher & Lobo, 2013).

3 Estimation of state(classification): this is the final step of this fusion process where

the fusion and classification of data is completed.

3.2.3 Treatment implementation and evaluation

In this study the implementation of the proposed design is done through a simulation

of the entire process. The dataset includes 100 sensor samples and 312 samples of the

fused matrix which is then used to show the simulation of fusion on a three dimensional

spectrum (Yann-Al Le borgne, 2014) and (Illinois, 2014).

Proposed system design

Here a block diagram shows the fusion system design. Taking in consideration the

layered approach mentioned in 3.2.1 on page 59 figure 3.6 on page 64 shows the


proposed system network. Figure 3.6 on the next page shows three wireless sensor

network having sensor clusters. Each cluster has a cluster head node. The cluster head

is responsible for training the data from sensors received within its range. Sensors can

interact only with the cluster head. Each sensor head is equipped with a switch for

communicating with the base station and other cluster heads .

The base station is responsible for the final classification and storing of data.


CH

CO COCO

CO

CH

CH

CH

Polymer FBG Sensor

Proximity Sensor

CONIR

Sensor

Network Edge Switch

Core switch

RouterBase Station

Wireless Access Point CLOUD BASED MANUFACTURING

PFBG SENSOR 1

PFBG SENSOR 2

PFBG SENSOR 3

NIR SENSORS

CH = Cluster Head

Proximity Sensors

Optical fiber network

UTP Cable Line

Chapter 4

Analysis

4.1 Introduction

In order to avoid the tedious process of disassembly during recycling thermoplastic

components are chopped into smaller shards which are sorted manually and if lucrative

these shards are sorted by hand with the aid of a convertor. The operators select shards

based on appearance, texture, color etc. manually. This process has to be extremely

precise as even a little margin of error can cause the price of the resultant plastic to

be extremely low. In the manual process an operator often selects one type of plastic.

Therefore, the automated classifier has to take in account both the factors texture

identification as well as the cadence. The conveyor belt of the recycling plant has

different blends of plastic resins passing through it. For a classifier system to achieve

100 percent accuracy and be profitable it needs to able to distinguish both the physical

attributes as well learn the underlying pattern of the data. Visually many thermoplastic

components such as ABS, polycarbonate, polyamide are similar therefore using the

data only from visual sources is not sufficient for proper classification of thermoplastics.

The NIR spectra shows the absorption of light. Regardless of the actual color of plastic

resin, samples of the same type always have the same peak patterns (Emberton, Chittka,

69

Chapter 4. Analysis 70

Cavallaro & Menghua, 2016a). These property makes it possible to distinguish different

plastic types.

Unfortunately, plastics with blackish gradient have stronger absorption peaks than

others (Emberton, Chittka, Cavallaro & Menghua, 2016b) This makes information

generated only from the spectra incomplete to successfully classify and categorize a

plastic shard. Therefore, data from different devices is required to successfully classify

a sample in a recycling plant given the huge variance in the sample size (Elsayed,

Rischbeck & Schmidhalter, 2015).

4.2 Supervised learning classifier

The classifier uses the feature vector, which is provided by the multiple sensors in the

recycling plant (Miguez, Georgiopoulos & Kaylani, 2010). These sensors provide a

more in depth attribute categorization to technically classify thermoplastic resin. For

the classifier to achieve its purpose the feature values for the thermoplastics in the same

category should be similar, and a greater variance for plastics of different categories

are expected. This means that the classifier complexity increases with the length of

our feature fused vector. In our algorithm sensory pretreatment is conducted for two

purposes. Firstly, it serves as a learning aid for the algorithm. This means that once

a shard is properly classified its properties are stored in the fused feature matrix, the

future classification of shards with same properties are instantaneous. Secondly the

smoothing (or the training vector) acts as a means to correctly abolish noisy data.

The principal component analysis method or (PCA) is used for both data smoothing

(training the data) and spectral recognition (classifying the data in a PCA hyperspectral

system) (Casa, Castaldi, Pascucci, Palombo & Pignatti, 2013). Once the fused feature

matrix is identified from the sensory system. A supervised learning classifier based

on probabilistic neural network is used to classify the plastics into their appropriate


categories ( here the decision fusion occurs) (Nie et al., 2016). A example of working

of a PCA hyperspectral system is shown in 4.1 on the following page

Figure 4.1: Data in PCA after feature fusion

In our case the sensory data pre-treatment is done mainly for two reasons. Firstly,

from data in the visible spectra it is interesting to utilize prior knowledge in order

to decide which are the discriminant features. Secondly, the information provided

by the hyperspectral system requires an adequate pretreatment for reducing the data

dimensionality, that it is huge in this kind of sensor, and for revealing differences

between spectra. The Principal Component Analysis method (PCA) has been used as

a hyper spectral component. Figure 4.2 shows the overall functioning of the classifier

shows for this pre-treatment.

4.3 Feature Fusion

As seen in the algorithm shown in 4.3 on page 72, the feature fusion algorithm takes three

inputs namely the temperature, tensile yields, and metallic content in a thermoplastic

shard. This information is captured by the individual sensors. The three inputs are

passed on to the FLC or the fuzzy logic controller. The first data fusion occurs between

the pre trained data and the new data captured by the sensor of the same type. This

help in eliminating unwanted noise from the fusion process this is also mentioned in


Figure 4.2: Classifier structure

equation 1.1 on page 21 the difference between d’ (the trained data) and d (new data)

is represented here. Consequently, fusion of data from different sources takes place

and the fused feature matrix as shown in 1.5 on page 28 The content in the fused

matrix is then compared to its previous entries and if there is no change in the data the

current run is disregarded from the matrix entirely (Jamalabadi, Nasrollahi, Alizadeh,

Nadjar Araabi & Nili Ahamadabadi, 2016). If the fusion is successful, the fused data is

accepted by the base station and is stored on a cloud database in real-time. This data is

instantaneously available to the hyperspectral scanners. These scanners with the help

of dimension data and in some cases (image representation) combine feature matrix to

the spectral image (decision fusion) and use this data to successfully classify a plastic

component (Scherer, 2012).

The process flow of feature fusion is shown through a business process modeling

notation (BPMN) diagram in 4.4 on page 73. Here there are two processes corresponding

to two different sensors are shown. One sensor collects temperature data and the second

sensor extracts tensile yield. The system checks sensor battery levels for calculating the

confidence factor every 10 minutes prior to sensor fusion. The data extracted is then


passed on to the base station where the fusion process actually takes place. Here the

base station will evaluate the confidence factor, fuse the data with its training factors

as mentioned in 4.3 on page 72. The fuzzy logic controller located in the base station

fuses the data from different sources to generate and maintain the feature fusion matrix

(Iounousse et al., 2015).

4.4 Findings

Findings are categorized according to the fusion type.

4.4.1 Feature fusion

Feature level fusion can be accomplished by fusing two feature sets obtained from

multiple sensor sources as shown in figure 1.5 on page 28. To further explain the

simulation process we will assume that A and B represent two different sensor feature

vectors. Let A =[a1,a2,a3,..., an] and B = [b1,b2,b3,...,bn] represent data extracted from

multiple sensor sources. The fusion of these two sets gives a new resultant feature

vector that is multiplication of the first two . The working of Kalman FLC( fuzzy logic

controller) is shown in figure 4.5 on page 74. Here there are two states as represented in

1.1. The state is the representative of variable d and the covariance is the representation

of state d’. On encountering a new entry of data from multiple sensors, firstly the values

of the new vector is predicted and measured based on the training values. The new

state is then validated against a specific range of values this is shown in step ’validate

measurement’ in figure 4.5 on page 74.If the new values are not in the specific range

then the vector is reverted back to its original state. Otherwise, the validated values are

merged with the co-variance vector (d’). The new probabilities for future measurement

prediction and validation are calculated and stored. After this step feature fusion is

actually completed and the two vectors of d and d’ are true representation of their


real-time values.

As a proof of concept the fusion algorithm was run on mat-lab simulator for 100

samples. The data is extracted from (Illinois, 2014) and (Yann-Al Le borgne, 2014)

. The data from these files was extracted to MATLAB data file. The results of the

simulation are as follows. In figure 4.6 on page 75 smoothing of surface temperature and

tensile strength takes place in accordance with first step in 4.3 on page 72. Figure 4.7

on page 76 show the difference between states d and d’ mentioned in equation 1.1 on

page 21 and 2.2 on page 48. Finally, Fusion of data from different sensors takes place

as shown in 4.8 on page 76. In feature fusion shown in figure 4.8 on page 76, both

smoothing and actual fusion of multi-sensor data takes place. Figure 4.9 on page 77

shows the difference in training and actual data after multi data fusion.

Here the data in the MATLAB model data file is plotted on a graph. Data from

individual sensors are first fused with their normalized forms, this function is known

as data smoothing. Here the assumption is that all values are in the proper range

configuration. Here 90 percent of the values is assumed to be in its normalized state or

90 percent of the data used is for training.

4.4.2 Decision fusion

For simulating the decision fusion sum of absolute difference SAD is used. Sum of

absolute difference is fundamentally the similarity between two image blocks. Here the

center of gravity,color,texture is used to classify objects into classes. SAD works by

taking difference in each pixel of the original block and comparing it with each pixel of

the processed block. In figure 4.1 on page 67 this pixel processing and fusion is done

by components NPC1 and NPC2. The only difference being that we use the principal

component analysis or PCA for classifying an image in to a class and not fusing image

data.


Next the texture and contour of the pixel is generated as shown in figure 4.11 on

page 79.

In order to show the simulation of hyper spectral system used for decision fusion

a triangulated image is plotted on a graph as shown in figure 4.10 on page 78. Then

the pixel information of our image and the target image is generated for comparison

as seen in figure 4.12 on page 80. Finally, the image is classified into a appropriate

class see figure 4.14 on page 81. For our thermoplastic classification the original image

represents the normalized image (training data) this could be derived as individual

image captured by CCD camera sensor or an array of images or even a fused image.

The thermoplastic shards captured by the CCD camera sensor can be scanned in similar

manner against the normalized set of images to identify its class. Here figure vrefCOG

shows a three dimensional representation of the scanned object.

4.4.3 Final PNN Classification

Once the feature fusion matrix the final feature vector and the class of the plastic is de-

cided this information is feed into PNN classfier to merge both these information(either

one feature vector or hyper spectral data by itself will not suffice as only density and

color information will not be enough to classify the final category ).

Input Layer: Feature Fused matrix(temprature, tensile properties), Hyperspectral

class data (this includes all information by the CCD camera sensor and the

hyperspectrum like colour, texture, density, depth(COG)) etc.

Output layer Final identified category of the plastic.

Summation Layer This layer will use the fuzzy logic controller to fuse the inputs

recursively till successfully classified and training data is normalized


Figure 4.3: Data Fusion Algorithm


Get Tensile

Yield

Get Temp

Fuse Data

Base Station

Figure 4.4: Data Fusion Algorithm

Chap

ter4.A

naly

sis78

Fig

ure

4.5

:D

ifference

betw

eenn

orm

alizedan

dactu

alvalu

eso

fm

erged

data

adap

ted

from

(Tian

etal.,

2016)


Figure 4.6: Smoothing of tensile and temperature data


Figure 4.7: Difference in state d and d’ of multi-sensor data

Figure 4.8: Fusion of tensile and temperature data


Figure 4.9: Difference between normalized and actual values of merged data


Figure 4.10: Plotting of original image


Figure 4.11: Texture and contour generation


Figure 4.12: Pixel-wise comparison of source and target


Figure 4.13: Center of gravity and density of the target

Figure 4.14: Pixel-wise comparison of source and target

Chapter 5

Discussion

The norm of covariance curve as shown by figure 4.9 on page 77, we can clearly see

that the norms of the fused features is considerably lower than those of normalized

version of both the sensors. This shows that the fused data is relatively free of noise.

This can also be seen from figures 5.1 on the following page , 5.2 on page 84 and 5.3 on

page 85. The example shown above gives a better predictive accuracy(less uncertainty).

this shows that the algorithm has worked relatively well.

A study conducted by (Declercq, Nagaoka, Yamazaki & Saijo, 2015) specifies

that PCA based spectrum are highly sensitive to light reflectively and gradients of the

scanned objects. According to the authors ,the hyper spectrum performs poorly for

objects of a blackish - gray gradient because lesser light is reflected back, thus causing

poor visibility of the scanned object. This will have a clear repercussion on the objects

scanned by the spectral system.

A major drawback of this study is that it was conducted in a simulated environment

with a small data set. A plastic recycling plant is huge with infinite amount of data being

generated daily. The use of algorithm on actual data will only show the true accuracy of

the methodology.

86

Chapter 5. Discussion 87

Figure 5.1: Temperature data before smoothing

5.0.1 Factors that could effect the accuracy of the algorithm

Out of sequence data The input to a data fusion system is normally discrete pieces

of algorithm having a unique timestamp, deciding its time of origin. However in

real life scenario, heterogeneous sensors working at different rates(speed) cand

lead to data being out of sequence. Such out of sequence data could badly effect

the working of our algorithm, as both training/ smoothing of data and the current

data will be in an inconsistent state leading to disastrous outcomes.

Conflicting data In our case study scenario, we have used NIR and Polymer FBG

sensors. A notable point is that both these sensor produce temperature data

(although in this study it is assumed that only NIR captures temperature data ).

A real life situation could have many sensors reflecting many different values.


Figure 5.2: Temperature data after smoothing

This could lead to data inconsistency. An appropriate measure of accuracy of the

algorithm needs the data to be in a consistent state.

Disparate data In order for a fully automated classifier the sensors should work in

conjunction. However, in a recycling plant there could be sensors whose data are

completely incompatible. Data from completely disparate sources will cause the

fusion mechanism in this study to fail. It needs to ensured beforehand that the

data to be fused is compatible.


Figure 5.3: Tensile yield before and after smoothing

Chapter 6

Conclusion and future study

This study presents a concept of multi sensor data fusion in a WSN based infrastructure.

The preliminary study of the WSN environment currently used in manufacturing/ re-

cycling system showed us that there are multiple arrays of sensors already deployed

around the machines. Data sensed by these multiple sensors are being used to monitor

the state of machines. In this study we have attempted to gather the data from these

sensors and fuse them into a meaningful form to automate the classification and sort-

ing of plastics into their appropriate categories and pass them along for next step of

processing. Recycling thermoplastic is a technological problem that is not completely

solved yet.One of the complexity in deploying an automated solution is that various

plastic contents though visually similar have largely varying characteristics which is not

perceivable by machines unless the data already present in different sensors are used in

a way that helps the classifiers more adaptive. This study shows a simulation through

the algorithm that fuses data from CCD spetrum, NIR sensor and inductive sensor.

A brief description of the classification method used in this study is as follows. PNN

methodology is used for the classification, in which set of predetermined inputs or (data

from sensors) are normalized and then fused using the Kalman filter algorithm. Feature

fusion represents the fusion of lower level sensors for a vector of feature attributes. A

90

Chapter 6. Conclusion and future study 91

higher level sensor used is the PCA or the hyper spectral sensing in which the plastic

color and texture is identified to determine the preliminary class of the plastic. This

information is again fused with the feature fused matrix of lower level values. The

Kalman filter fusion process and the hyperspectral image classifcation is shown through

a simulation in MATLAB.

To conclude although the algorithm successfully works on a small scale simulated

data, a full scale implementation of the actual environment is essential. In this study the

elements of noisy data, redundant, incompatible and spurious data are not considered but

these factor could have serious repercussions on accurate functioning of the algorithm.

6.0.1 Future studies

Future work will include enhancing the automated fusion through machine learning

and web based modules.A sensor ontology language can also significantly improve the

extraction of data through a web based module.

Semantic sensor network

Sensor semantics deal with extending the former OWL ontology for modeling sensor

devices and their capabilities for interacting better with their systems and processes

(Compton et al., 2012). The ontology is based on observation the pattern of sensor

interaction with its peers or higher level systems as seen in figure 6.1 on the next page

The sensor ontology can be sub divided in to a number of perspectives based on

their interaction with sub-systems. These are as follows

1 Sensor perspective:what a sensor measures, how it senses and what is sensed.

(Compton et al., 2012)

2 System perspective: sensors interactions with their systems. Focus is on a system of

sensors (Compton et al., 2012).


Figure 6.1: Pattern in Semantic sensor network taken from


3 Data perspective: what data is being sensed also the meta-data generated (Compton

et al., 2012).

4 Feature or property perspective with focus on features or attributes being sensed and

the correlation of these properties


Figure 6.2: Overview of the Semantic Sensor Network ontology classes and properties

taken from


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