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Internet of Things in Business Transformation E-Book

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The objective of this book is to teach what IoT is, how it works, and how it can be successfully utilized in business. This book helps to develop and implement a powerful IoT strategy for business transformation as well as project execution. Digital change, business creation/change and upgrades in the ways and manners in which we work, live, and engage with our clients and customers, are all enveloped by the Internet of Things which is now named "Industry 5.0" or "Industrial Internet of Things." The sheer number of IoT(a billion+), demonstrates the advent of an advanced business society led by sustainable robotics and business intelligence. This book will be an indispensable asset in helping businesses to understand the new technology and thrive.

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CONTENTS

Cover

Title Page

Copyright

Preface

1 Applications of IIoT-Based Systems in Detection Leakage in Pipeline Custody Transfer of Hydrocarbon Products

1.1 Introduction

1.2 Industrial Internet of Things

1.3 Pipeline Leaks

1.4 Conclusion

References

2 Heart Rate Monitoring System

2.1 Introduction

2.2 Project Objectives

2.3 System Architecture

2.4 Conclusion

References

3 An Efficient Clustering Technique for Wireless Body Area Networks Based on Dragonfly Optimization

3.1 Introduction

3.2 Literature Review

3.3 Clustering Technique

3.4 Implementation Steps

3.5 Result and Simulations

3.6 Conclusion

References

4 Transformational Technology for Business Transformation in the New Era

4.1 Introduction

4.2 Digital Transformation and its Big Trend

4.3 Transformational Technology

4.4 How to Get “Digital Transformation” in a Right Way for Any Business

4.5 Relevance of IoT in Digital Transformation

4.6 Conclusion

References

5 Future of Artificial Intelligence: Will People be at More of an Advantage Than They Are Today?

5.1 Introduction

5.2 The State of Artificial Intelligence (AI)

5.3 How Do Customers Interact with AI Nowadays?

5.4 AI as Digital Assistants

5.5 AI and Privacy, Data Security

References

6 Classifier for DDoS Attack Detection in Software Defined Networks

6.1 Introduction

6.2 Related Work

6.3 DDoS Attacks Overview

6.4 Types of DDoS Attacks

6.5 DDoS Detection Techniques in SDN

6.6 Detection Using ML Techniques

6.7 Proposed Work Using SVM Classifier

6.8 Data Set Used

6.9 Proposed Methodology

6.10 Existing & Proposed Results

6.11 Conclusion & Future Work

References

7 IoT-Based Optimized and Secured Ecosystem for Energy Internet: The State-of-the-Art

7.1 Introduction

7.2 Distinguishing Features Between Home Automation and Smart Home

7.3 Energy Generation Shift Towards Renewable Sources

7.4 Robust Energy Management with IoT Technology

7.5 Solution from IoT Technology for Secured Transactions

7.6 Role of IoT in Smart Grid

7.7 Bottleneck Areas of Existing System

7.8 Fusion of Energy Internet with IoT and Blockchain

7.9 Challenges for Safe and Secured Ecosystem in Energy Internet

References

8 A Novel Framework for Intelligent Spaces

8.1 Introduction

8.2 Intelligent Space

8.3 Product Identification

8.4 Position Measurements

8.5 Proposed Framework

8.6 Conclusions

References

9 Defense and Isolation in the Internet of Things

9.1 Introduction

9.2 IoT Security Overview

9.3 Security Frameworks for IoT

9.4 Privacy in IoT Networks

9.5 Summary and Conclusions

References

10 Realization of Business Intelligence using Machine Learning

10.1 Introduction

10.2 Business Intelligence and Machine Learning Technology

10.3 Literature Study

10.4 Business Analytics and Machine Learning

10.5 IoT in Machine Learning

10.6 Conclusion

References

11 Current Trends and Future Scope for the Internet of Things

11.1 Introduction

11.2 IoT in Healthcare

11.3 IoT in Agriculture

11.4 IoT in Industries

11.5 IoT-Based Smart Cities

11.6 IoT in Robotics

11.7 Conclusion and Future Scope

References

12 Challenges for Agile Autonomous Team in Business Organization

12.1 Introduction

12.2 Literature Review

12.3 Types of Autonomy

12.4 Challenges for Autonomous Team

12.5 Suggestions for Training

12.6 Conclusion

References

13 Role of Big Data Analytics in Business Transformation

13.1 Introduction to Technology-Enabled Business Transformation

13.2 Introduction to Big Data, Big Data Analytics & Business Intelligence

13.3 Big Data Analytics and its Role in Business Transformation

13.4 Successful Real World Cases Leveraging BDA for Business Transformation

13.5 BDA Capability Building Challenges

13.6 Conclusion

References

14 Internet of Thing Trends

14.1 Architecture of IoT

14.2 Dependency of Healthcare on IoT

14.3 High Demand of Smart Homes

14.4 Environmental Monitoring

14.5 Waste Management

14.6 Smart Parking

14.7 Routing System for Inner-City Bus Riders

14.8 Self-Ruling Driving

References

15 Internet of Things: Augmenting Business Growth

15.1 Introduction

15.2 Role of IoT in the Business Growth

15.3 Short Comes or Hurdles of IoT

References

Index

End User License Agreement

List of Tables

Chapter 3

Table 3.1 Simulation parameters.

Table 3.2 Dragonfly Algorithm.

Chapter 4

Table 4.1 Various objectives of organizations for transformation given by 460 ex...

Chapter 9

Table 9.1 IoT world forum reference model.

Table 9.2 Security mechanisms to mitigate the threats in the IoT networks.

Table 9.3 Bluetooth smart device protocol stack.

Chapter 10

Table 10.1 Interdisciplinary domains associated with research in IoT.

Chapter 13

Table 13.1 Summary of Case Studies [16].

List of Illustrations

Chapter 1

Figure 1.1 Pipeline mapping—US.

Figure 1.2 Percentage break-up of reasons causing pipeline leaks.

Figure 1.3 Vapor Sampling method of pipeline leak detection.

Figure 1.4 Acoustic method of leak detection.

Figure 1.5 Digital signal processing based pipeline leakage detection.

Figure 1.6 Industrial Internet of Things-based architecture for pipeline leak de...

Figure 1.7 Use of support vector machine for pipeline leak detection.

Figure 1.8 Evaluation of random forest classifier for leakage detection.

Chapter 2

Figure 2.1 Hardware components.

Figure 2.2 Pulse sensor connection.

Figure 2.3 The LinkIt One Board.

Figure 2.4 Pulse Sensor kit

Figure 2.5 Sign-up flowchart.

Figure 2.6 Basic flow.

Figure 2.7 Sign-in page.

Figure 2.8 Choose an account.

Figure 2.9 Options.

Figure 2.10 List of patients.

Figure 2.11 Patient details page.

Chapter 3

Figure 3.1 Base station is not in access of some WBANs.

Figure 3.2 Inter-WBAN clustering.

Figure 3.3 Flow chart of proposed scheme.

Figure 3.4 Primitive corrective patterns between dragonfly.

Figure 3.5 Transmission range versus Cluster Heads for Nodes 50–200.

Figure 3.6 Transmission range versus Cluster Heads for Nodes 250 and 300.

Chapter 4

Figure 4.1 CEO Top Business Priorities for 2018 and 2019 Source: Gartner.

Chapter 6

Figure 6.1 Architecture of SDN.

Figure 6.2 DDoS attacks types.

Chapter 7

Figure 7.1 Home automation & smart home.

Figure 7.2 Internet-of-Things & Web-of-Things.

Figure 7.3 Devices communicating through cloud servers.

Figure 7.4 Process of secured transaction using blockchain.

Figure 7.5 De-centralized banking system using blockchain.

Figure 7.6 Traditional network and software-defined network.

Figure 7.7 Energy transaction through blockchain.

Figure 7.8 Smart car performing secured transactions through blockchain.

Chapter 8

Figure 8.1 Transmitter and receiver.

Figure 8.2 Framework for Intelligent Spaces.

Chapter 9

Figure 9.1 An overview of IoT and IP security protocols.

Chapter 10

Figure 10.1 Applications of Machine Learning.

Figure 10.2 Basic features of Business Intelligence.

Figure 10.3 Business analytics applications of Machine Learning.

Figure 10.4 Scope of business analytics.

Figure 10.5 Sample data source of sales transaction for business analytics.

Figure 10.6 Decision action cycle in Business Intelligence.

Figure 10.7 Decision types at different level.

Chapter 13

Figure 13.1 Digital transformation & growth rate (Forbes.com, 2019).

Figure 13.2 Dimensions of big data.

Figure 13.3 Big data analytics value.

Figure 13.4 Relationship between big data analytics & business intelligence.

Figure 13.5 Strategic value of BDA.

Figure 13.6 Strategic role of BDA.

Figure 13.7 A conceptual framework for BDA adoption in business.

Figure 13.8 BDA capability building challenges.

Chapter 14

The 4 Stage IoT Solutions Architecture

Chapter 15

Figure 15.1 3-layer architecture of IoT [4].

Figure 15.2 Layer IoT architecture [4].

Figure 15.3 Layer architecture of IoT [5].

Guide

Cover

Table of Contents

Series Page

Title Page

Copyright

Preface

Begin Reading

Index

End User License Agreement

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Scrivener Publishing

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Beverly MA, 01915-6106

Publishers at Scrivener

Martin Scrivener ([email protected])

Phillip Carmical ([email protected])

Internet of Things in Business Transformation

Developing an Engineering and Business Strategy for Industry 5.0

Edited by

Parul Gandhi

Surbhi Bhatia

Abhishek Kumar

Mohammad Alojail

Pramod Singh Rathore

This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

© 2021 Scrivener Publishing LLC

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-71112-4

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Cover design by Russell Richardson

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

Printed in the USA

10 9 8 7 6 5 4 3 2 1

Preface

The Internet of Things (IoT) has seen phenomenal growth over the last few years. Not only is this emerging IoT paradigm enhancing lives, it also has the potential to impact economic growth. And because IoT enables new insights into the business world, it continues to be very helpful in transforming business strategies and operations. However, before its full potential can be realized, it is necessary to confront several challenges by addressing the issues posed by the IoT and provide technological solutions to them. To help in this mission, many of the powerful features of the IOT and how they can be used to build strategies for a successful business are discussed in this book; and, in order to generate insights into various domains, IOT tools and techniques are also discussed in detail.

This book is written based on many years of teaching and research experience. Our goal is to provide researchers and students a complete package covering the fundamentals of the IOT and how it helps in various domains, its common practice in business and industry as well as all research aspects of IoT applications. Therefore, when choosing a format for a book that would be accessible to both researchers and university students, an attempt was made to simplify the content and emphasize real-life examples to make the information more easily understood.

After reading the entire book, the reader will come away with a thorough understanding of the rapid development of IoT-based systems and their impact on several scientific and engineering domains, including healthcare, smart homes, agriculture, robotics, industries, integration of leak detection in pipeline custody transfer of hydrocarbon products, and many others. Current trends and different architecture domains are explained systematically to motivate those in academia and industry to become familiar with the power of IoT. Also included are the heterogeneous used to enhance IoT security and an explanation of the framework of intelligent spaces needed for IoT-based optimized and secure ecosystem for the energy internet, handled by pervasive computing environment. The chapters thoroughly explain the transformation of business and ways of addressing its current needs, including how machine learning approaches play a greater role in achieving business intelligence in large commercial organizations, the role of big data analytics with the concept of automation, a roadmap for businesses to leverage big data analytics for creating business value implementing smartness in a smart environment where people are living and making an effort to develop it. Also, an analysis is conducted to examine how human and artificial intelligence might evolve together in future years and how it will impact humans with the help of business intelligence. Finally, this book portrays the difficulties experienced in business development consisting of a self-governing autonomous group setup with resources from both the IT and business advancement side of the organization.

On behalf of the entire editorial board, our heartfelt appreciation goes out to all the authors for considering us to publish their valuable work. Their overwhelming response has been a real factor in keeping us motivated and moving forward with this book, and therefore merits our sincere acknowledgement. The quality and diversity of their contributions have made the book more impactful, and their trust, patience and kind cooperation throughout the various stages of production played a vital role in its success. We also wish to thank the people at Scrivener Publishing for their guidance and support in bringing this edited collection to completion.

The Editors

November 2020

1Applications of IIoT-Based Systems in Detection Leakage in Pipeline Custody Transfer of Hydrocarbon Products

Pragyadiya Das

National Institute of Technology, Trichy, India

Abstract

Custody transfer via pipelines has always been prone to losses due to leakages. Moreover, due to the large lengths of pipelines, leak detection is a tedious task. There are various methods that employ mechanical, mathematical and signal processing based approaches to detect leaks and their location. With the advent of Industrial Internet of Things and Machine Learning, the method of leak detection of pipelines using various machine learning methods have been analyzed and implemented in this chapter.

Keywords: Custody transfer, pipelines, leak detection, industrial Internet of Things, machine learning, ensemble learning

1.1 Introduction

The world of Oil and Gas has been moving at an alarming rate. The world is getting energy hungry [1] and the need for oil (or natural gas) is not going to go down anytime soon [2].

With this increased need for fuel, there is a constant need for the fuel (gasoline etc.) to be transported from one place to another. This gives rise to a need for a medium of transfer that would effectively transport the fuel in a secure and accountable manner.

Figure 1.1 Pipeline mapping—US.

As per data released in India’s official website (https://community.data.gov.in/), the total length of Natural Gas pipelines went from 10,246 to 17,753 km in the period of 31st March 2010 to 31st March 2017. That is a growth of 57% [3]. This is a significant growth and shows the need and increasing utility of pipeline in the energy scenario of an energy hungry nation like India [4].

Similarly, USA has a motor gasoline consumption of about 8,682 thousand barrels per day [4], and has a very significant crude and product pipeline, Figure 1.1 [5] explains this fact.

Looking at pipelines a major method of custody transfer of Gasoline, Diesel and other energy related fuels, it is important that there is proper monitoring of these structures to prevent any kind of adulteration, or more importantly, leakage causing financial losses.

With the advent of Wireless Sensor Networks (WSN) and Internet of Things (IoT), the monitoring of long distance pipelines have now become a task that can be achieved.

1.2 Industrial Internet of Things

The concept of “Internet of Things” has its core concepts set out as the interconnection of devices that have the capability to talk to themselves and “act” or take “decisions” based on each other’s statuses.

Usage of Industrial grade sensors to monitor industrial processes in real time and later achieving their interconnection is called Industrial Internet of Things.

Modern day refineries and pipelines consist of numerous numbers of sensors. The data that is generated is huge. This poses as a great opportunity to drive data analytics and Industrial Internet of things in this sector [6].

IIoT is the utilization of smart sensors (or actuators) to enhance manufacturing and industrial processes. The idea behind IIoT is intelligent machines are made better humans at capturing and analyzing data in real time, in addition they are also made better at prompting information that can be used to make decisions in lesser time and more effectively [7].

1.3 Pipeline Leaks

Pipelines are undoubtedly the most safe and reliable mode of custody transfer of fuel. Major reasons contributing to pipeline failure are depicted in Figure 1.2 [8].

The below detail explains the varied kind of reasons that are faced while analyzing pipeline leaks.

Figure 1.2 Percentage break-up of reasons causing pipeline leaks.

1.3.1 Various Techniques used to Detect Pipeline Leak

The methods that are usually used are divided into two classes,

Hardware techniques.

Software techniques.

We shall discuss in brief about three of the most common non-analytic and hardware-based technique used in pipeline leak detection. They are as follows:

a) Vapor Sampling Method:

This is the most common method used for detection of pipeline leaks. This is an augmented system that has gas detection and gas ppm measurement systems in it. In this method, a gas detection/measurement unit runs along the line of the pipeline. A representation of the same is below in Figure 1.3.

b) Acoustic Signal Processing [9]:

In this method, the occurrence of the leak is treated as a fault in the wall of the pipe. The pressure difference profile inside a pipeline is usually from a higher-pressure potential to lower-pressure potential, this can be treated as an incident beam of light traveling from one end to the other end of the pipeline. A leak on the pipeline causes a disturbance in the pressure profile that can be seen a translucent substance in the path of the incident beam. Now, based on the time needed for the reflected beam to reach the pumping station, the exact position of leak is determined. A representation of the same is given in Figure 1.4.

Since, the speed of sound wave propagation is in a single-phase, rigid pipe is found using the application of law of conservation of mass and comes as,

Here, ν is the wave propagation speed in m/s, ρ is the density of fluid in kg/m3, Ee is the Young’s modulus of the piping material in N/m2, K is the bulk modulus of the liquid and ϕ is a factor of restraint based on the Poisson ratio.

c) Fiber Optic Method [10]:

In this method, a helical strain of fibre optic cables runs along the length of the pipeline. The leak that is generated on the pipeline is seen as spikes in the strain profile of the fiber optic cable that is running along the pipeline. This strain profile is calculated using Brillouin Optical Time-Domain Analysis (BOTDA) [11]. BOTDA can be calculated using traditional mathematics and data analysis methods [12].

Figure 1.3 Vapor Sampling method of pipeline leak detection.

Figure 1.4 Acoustic method of leak detection.

We shall discuss in brief about three of the most common analytic and software-based technique used in pipeline leak detection. They are as follows:

a) Negative Pressure Wave method [13]:

In this method, the drop in line pressure and speed profile is used to detect the leak, however, in this case, the process is heavily dependent on software techniques rather than hardware. The pressure wave is captured using sensors positioned in the upstream and downstream of the pipeline. The captured wave is then put through the extended Kalman Filters (EKF), used for non-linear systems to estimate the number of states required to model the pipeline system [14]. Extended Kalman filtering technique is used to estimate the state vectors that contain information about the segment of the pipeline, this combined with the virtual leakage rate gives an expression [15] that accurately derives the point of leak on the pipeline, provided that the initial conditions of the virtual leakage rate and that of the pipeline are same [16]. In addition, techniques like Haar Cascading are also done to do waveguide transformation/decomposition and analysis for detection of leakages [17].

b) Digital Signal Processing:

This method uses process data such as pressure profile, flow profile, strain, entropy in flow, etc. to identify and analysis various features which help in identification and detection of leaks in pipeline systems.

Features such as energy of the signal, entropy of the signal, zero crossings cut and energy distribution in decomposed wavelet is analyzed [18].

Of these, zero crossings cut is the most significant, as it identifies the events where a high value (defined as 1.5 times the mean of the signal amplitude (positives only)) occurs immediately after a 0 or negative value). These features show us spikes in the flow data and which are then run through a Fast Fourier transform (FFT) for signal decomposition and transformation. This transformed signal wavelet or flow data is then run through a Neural Network, that classifies suitable data as leakages.

A representation of the process is given in Figure 1.5.

c) Dynamic Modeling Approach:

This is a method that is gaining popularity with the advancement of our understanding of the usage of classical and modern statistical techniques. We have already discussed the used of states of the fluid flow in the method that used EKF for feature extraction. Using the concepts of Fluid mechanics, the flow can be modelled in terms of partial differential equations, which in turn can be transformed into state space equations, these state space equations are used to determine the behavior (wave profile, pressure profile, flow profile etc.) of fluid and any disturbance can then be analyzed to detect leakages. In addition, computational fluid dynamics (CFD) is being used to model and detect leakages in pipes.

Figure 1.5 Digital signal processing based pipeline leakage detection.

1.3.2 Use of IIoT to Detect Pipeline Leak

Use of IIoT is gaining popularity with the advent of usage of more connected devices being used in industries day in and day out.

A typical IIoT based unit shall have all devices interconnected with each other. These devices are again connected to a network that terminates at a gateway. The gateway has a two way terminal connecting the analytics block with itself. The analytics block again has a two way terminal connecting it to the rules and controls unit.

In our case the pipeline is the system that has its upstream and downstream connected to each other. The sensors at the upstream, downstream and along the length of the pipeline are interconnected among each other and are also in turn connected to an edge gateway. This gateway connects the system to analytics and transform unit that is used to generate leads for the process. This connection is through an access network, which is typically a network that can handle high volume of uploads and downloads. After this the leads are sent to control unit which based on the leads generate controls or rules for the system, this is again through a network called the service network and has to be able to handle high volume of uploads and downloads.

A representation figure describing the usage of IIoT architecture in detection of leakage in pipeline custody transfer, is given in Figure 1.6,

Figure 1.6 Industrial Internet of Things-based architecture for pipeline leak detection.

1.3.3 Use of Machine Learning Algorithms to Detect Pipeline Leak

Machine Learning and Data science has gained a lot of popularity in the last decade. In the earlier sections of the chapter, we demonstrated the usage of Neural Networks for detection of leaks in a pipeline, that itself was a pre cursor of introduction of machine learning in the field of leakage detection of pipeline transfer of hydrocarbon.

Several machine learning algorithms have started being used for detection of leaks. Advanced techniques such as Neural Networks [19] and Support Vector Machines [20, 21] have already been used and proved to have given excellent results. In addition, more advanced methods employing deep learning and convolutional neural networks [22] are also being explored, in fact application of Variational Autoencoders have already been tested and used.

We shall now discuss the technique of implementation of each of these methods (Neural Network and Support vector machine based) in brief, in addition, we shall also try to implement a novel strategy to use ensemble learning algorithms to detect leakages in pipelines [23].

a) Neural Networks-Based Strategy for Detection of Pipeline Leak Detection

The overall architecture of the system is already designed in the section where leakage detection using digital signal processing is explained, in the section a representative system architecture. Here, we only analyze the details of the neural network from a very computational point of view. In the paper 3-layer neural network is used with a sigmoid activation function. In the method, the error is decreased by backpropagation.

b) Support Vector Machines-Based Strategy for Detection of Pipeline Leak Detection

We have already seen the use of negative pressure wave method for leak detection in earlier section. We see that in negative pressure wave method, various computational methods are used to detect the leakage from the huge dataset that contains the leakage information as well as the noise from the pipeline, pipe fittings and environment. Use of these computational methods makes the model very expensive from a computational point of view. Therefore the use of Support vector machines in conjunction with negative pressure wave architecture proposed.

Figure 1.7 Use of support vector machine for pipeline leak detection.

A support is used to detect extreme cases—for our case the extreme cases are Leak or No Leak case. This is depicted in Figure 1.7.

The data class is separated by hyper planes that divide both the classes clearly. The hyper planes are also called support vectors.

Negative pressure wave method has pressure information from two different scenarios, one is when there is no leak and the pressure profile is usual and the other is when there is a leakage and the pressure profile has disturbances, support vector machines are used for correctly classifying these data and generating leads for detection of leak.

A representative figure for showing the usage of Support Vector machines for negative pressure wave method is as follows,

1.3.4 Design and Analysis of Ensemble Learning-Based Approach for Pipeline Leak Detection

Ensemble learning is basically the method of taking advantage of more than one model to get the combined prowess of the models to achieve better accuracy of analysis. Due to the complexity of the data that we aggregate from a pipeline system, we may require the usage of multiple classification models to come to the conclusion. While there are various ensemble learning models, we choose random forest classifiers. Random forest classifier is one where we break the actual dataset into various bootstrapped samples and bind them with particular features that we want to classify them on. Then we fit these datasets into trees considering selected features only. Then the result that we get from these trees is averaged to get the final result. A representative figure to explain is given in Figure 1.8.

Figure 1.8 Evaluation of random forest classifier for leakage detection.

1.4 Conclusion

It is seen from the literature and analysis given in the chapter that there is a lot of work that is being done in the field of combining connected devices with the power of analytics and machine intelligence to make a system that has minimal to zero human interference.

Implementing algorithms to get desired output is a requirement is something that can only be explored through continuous experimentation and testing.

This chapter aims at making the foundation strong for a beginner in the field of Pipeline engineering, Industrial Internet of Things and Machine learning.

References

1. “In an energy-hungry world, natural gas gaining the most”, Amy Harder. Axio.com, June 2019.

2. Pydata 2018 Video (Youtube), Hot Water Leak Detection Using Variational Autoencoder Model—Jay Kim.

3. Na, L. and Yanyan, Z., Application of Wavelet Packet and Support Vector Machine to Leak Detection in Pipeline. 2008 ISECS International Colloquium on Computing, Communication, Control, and Management, 2008.

4. Ibitoye, Olakunle & Shafiq, Omair & Matrawy, Ashraf. (2019). A Convolutional Neural Network Based Solution for Pipeline Leak Detection.

5. Pipeline Stats in India, https://community.data.gov.in/length-of-natural-gaspipelines-in-india-from-2010-to-2017/.

6. Pipeline—data as per plot, https://www.indexmundi.com/energy/?product=gasoline&graph=consumption&display=rank.

7. Where are pipelines located?, https://pipeline101.org/Where-Are-Pipelines-Located.

8. Lim, K., Wong, L., Chiu, W.K., Kodikara, J., Distributed fiber optic sensors for monitoring pressure and stiffness changes in out-of-round pipes. Struct. Control Hlth., 23, 2, 303–314, 2015.

9. Gamboa-Medina, M.M., Ribeiro Reis, L.F., Capobianco Guido, R., Feature extraction in pressure signals for leak detection in water networks. Procedia Eng., 70, 688–697, 2014.

10. US Oil and Gas Pipeline Stats, https://www.bts.gov/content/us-oil-and-gaspipeline-mileage.

11. Definition of IIoT, https://internetofthingsagenda.techtarget.com/definition/Industrial-Internet-of-Things-IIoT.

12. Bolotina, I., Borikov, V., Ivanova, V., Mertins, K., Uchaikin, S., Application of phased antenna arrays for pipeline leak detection. J. Petrol. Sci. Eng., 161, 497–505, 2018.

13. Adnan, N.F. et al., Leak detection in gas pipeline by acoustic and signal processing—A review. IOP Conf. Ser.: Mater. Sci. Eng., 100, 012013, 2015.

14. Wang, L., Guo, N., Jin, C., Yu, C., Tam, H., Lu, C., BOTDA system using artificial neural network. 2017 Opto-Electronics and Communications Conference (OECC) and Photonics Global Conference (PGC), Singapore, pp. 1–1, 2017.

15. Shibata, A., Konishi, M., Abe, Y., Hasegawa, R., Watanabe, M., Kamijo, H., Neuro based classification of gas leakage sounds in pipeline. 2009 International Conference on Networking, Sensing and Control, 2009.

16. Feng, W.-Q., Yin, J.-H., Borana, L., Qin, J.-Q., Wu, P.-C., Yang, J.-L., A network theory for BOTDA measurement of deformations of geotechnical structures and error analysis. Measurement, 146, 618–627, 2019.

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18. Chen, Y., Kuo, T., Kao, W., Tsai, J., Chen, W., Fan, K., An improved method of soil-gas sampling for pipeline leak detection: Flow model analysis and laboratory test. J. Nat. Gas Sci. Eng., 42, 226–231, 2017.

19. Chen, H., Ye, H., Chen, L.V., Su, H., Application of support vector machine learning to leak detection and location in pipelines. Proceedings of the 21stIEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510), Como, Vol. 3, pp. 2273–2277, 2004.

20. Thorley, A.R.D., Fluid Transients in Pipeline Systems, D&L George Limited, pp. 126–129, 1991.

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2Heart Rate Monitoring System

Ramapriya Ranganath*, Parag Jain†, Akarsh Kolekar‡, Sneha Baliga§, A. Srinivas¶ and M. Rajasekar**

Microsoft, Intel, Delloite, PESU, Dayand Sagar, PESU, India

Abstract

Internet of Things (IoT) is a new and evolving concept that provides connectivity to the Internet via sensing devices and embedded systems to achieve intelligent identification and management in a heterogeneous connectivity environment without human–human or human–computer interactions. Current medical developments have essentially moved the patient monitoring devices typically found in a critical care room such as ECG, pulse oximeter, blood pressure, temperature, etc., into a discharged patient’s home, with the nurse’s station being a computing device connected to a broadband communication link. The primary limiting factor is the cost of this collection of devices. Mobile Healthcare, or mHealth, is defined as “mobile computing, medical sensor, and communications technologies for health care”. Our aim is to design a prototype of a wearable comprising of medical sensors, in this case, a pulse sensor, which transmits data to LinkIt One, a proto-typing board for IoT devices, which then sends real time data to a database, from where data is retrieved and used to plot a dynamic graph on an app (Android and iOS). mHealth is required to prevent medication errors from occurring and to increase efficiency and accuracy of existing medical health systems.

Keywords: Internet of Things, LinkIT One, mobile computing, medical sensor, Mobile Healthcare

2.1 Introduction

Internet of Things (IoT) is the inter-communication of various sensors and embedded systems, without human to human interaction and human to sensor interaction, via the internet and its ability to transfer data over a network, which all work together in order to provide a feasible and much desired output. IoT is autonomous and independent of human interaction. It is the need of the hour. IoT in healthcare, is not only desired but is very necessary. Internet of things makes it possible to capture and analyze data sensed from the human body. IoT makes it possible to reach people anywhere and at any time. Using IoT, we can look out for people who stay in remote locations and provide them with high class medical treatment and constant monitoring [1].

A major problem nowadays is the monitoring of patients. This includes both the discharged patients as well as admitted patients in a hospital. The elderly discharged patients are dependent on others. Some patients do not return to medical facilities for post-discharge testing, assessment and evaluation due to non-availability of their dependents. The proposed system intends to make communication of the patient with doctors and other family members much quicker and simpler. The patient can be monitored and prioritized by the doctor based on how critical their condition is. In the golden hour, each second matters. It is a life or death situation and the most critical patient is prioritized. Even for patients admitted, within the hospital there is prioritization. Using such IoT devices, data can be collected, processed and developed for a large sector of people which include the elderly and those with cardiovascular issues. A doctor can make the required administrations with full knowledge of the patient’s medical history and with continuous flow of real time data regarding the patient’s present condition.

Implementation of IoT in healthcare is used to process one’s data effectively and diagnose the patient’s condition. The data is presented to the doctor in a clear setup. The patient registers on the app via Google authentication. Post-login, the doctor will be able to view the profiles of his subscribed patients through MQTT protocol. New patients have to register [2].

The doctor gets a notification once his subscribed patient data reaches below or beyond medical thresholds. This is useful as the doctor in the current scenario treats the patients in a round robin scenario, there is no priority process established yet. The patient admitted in a particular hospital would want the best doctor to treat them, but this is really hard as the doctor can treat only a small set of patients on a daily basis. Using our mechanism we show the doctor to treat the patient on a priority cycle [3]. As we collect data from a patient dynamically, we can update the list with the highest priority patient. This will ensure that the patient with the most requirements is treated first as during the golden hour they need to be given higher priority. The transmission of the data from the board to the database is done by running node on the board. The chunked data is continuously transmitted to the server and retrieved in the form of a graph on the doctor’s app. On the app, a dynamic plot of the person’s pulse is obtained. There are many advantages to using the LinkIt One development kit. It has an in-built Li-Ion battery. So, constant power supply to the board is not an issue. This does allow the user to move around freely, as other boards would not allow this. It also has an in-built Wi-Fi module. The person can thus, move about freely [4].

Signals sent from the sensor to the LinkIt One are used to calculate the Inter Beat Interval (IBI), which is in turn used to calculate the Beats Per Minute (BPM) of the patient. The system supports the continuous flow of data from the patient, processed by the LinkIT ONE to be accessed by the doctor who is given accessibility to the patient’s information. If the data, crosses a given threshold (upper and lower) the doctor is alerted and immediate attention is given to secure the patient. Here, data is processed and displayed in highly efficient manner which is doctor-friendly and patient-friendly.

Distinct advantages of the proposed system are cost-effectiveness and personalization for chronic patients. Doctors can monitor the health of their patients on their smartphones after the patient gets discharged from the hospital.

A solution involving the Internet of Things has been provided. It includes designing a wearable which would transmit crucial medical sensor data such as pulse, etc. to a remote server, from which data could be accessed by authorized medical professionals on the app, and appointments could be made accordingly between the medical professional and the patient [5].

2.2 Project Objectives

The following are the objectives of the project:

Configure existing devices/sensors to transmit data in a wireless manner to a server.

Create a database to maintain the signal data.

Design a cross platform app which can display critical medical parameters received from devices/sensors with minimum latency.

Set up a pulse monitor on the app to display pulse.

Implement emergency SMS service, when critical parameter threshold of patient is crossed.

Patient parameter list for critical care is as follows:

Pulse

Temperature

Blood Pressure

Respiratory Rate

Oxygen Saturation

Pulse sensor was chosen as it measured the most important parameters from the human body and thus ideal to be used in a wearable. The system is now modularized to incorporate new sensors [5].

2.3 System Architecture

Hardware Components:

The main hardware components used are:

Pulse Sensor—Pulse Sensor heart rate sensor for Arduino and Arduino compatible boards. It adds amplification and noise cancellation circuitry to the hardware. It’s noticeably faster and easier to get reliable pulse readings. Pulse Sensor works with either a 3 V or 5 V Arduino. A Color-Coded Cable, with a standard male header connector. As we know that the ear lobe and the thumb are the most sensitive areas in the human body, we attach the sensor using the ear clip to the ear lope or using the Velcro we attach it to the thumb of the user. There is a small camera placed in the sensor along with an infrared sensor. Infrared sensors work on the principle of reflected light waves. Infrared light reflected from objects. The reflected light is detected and then the BPM (Beats per Minute) is calculated [6].

Figure 2.1 Hardware components.

Figure 2.2 Pulse sensor connection.

LinkIt One—It is a high performance-development board. It provides similar pin-out features to Arduino boards, making it easy to connect various sensors, peripherals, and Arduino shields. LinkIt One is an all-in-one prototyping board for IoT/wearable devices. The advantage of using this board is that it has inbuilt GSM, GPRS, Wi-Fi, GPS, Bluetooth features. It also has a Lithium ION battery which will ensure the board can be used without being connected to a socket always. This is a very important feature for us as this will not restrict the user’s movement. The users are free to move around with this board unlike other boards (

Figure 2.3

).

Proposed System: The proposed automatic, IoT system is used to monitor the patient’s heart rate. It is also used to display the same in the form of an ECG (electrocardiogram). The system has the parts:

Sensing Sub-System

Data Transfer Sub-System

Data Display Sub-System

1. Sensing Sub-System

This comprises of the pulse sensor and the LinkIT One. The Pulse sensor is worn on the tip of the thumb or on the tip of the ear lobe (using and ear clip) as these are sensitive parts. It sends pulse signals to the LinkIt One. The LinkIt One performs the programmed operations on the signal and calculates the Inter-Beat Interval (IBI) and hence the beats per minute (BPM). The connections for the Sensing sub-stem are (Figure 2.2): the pulse sensor, ear clip and Velcro (Figure 2.4).

Figure 2.3 The LinkIt One Board.

Figure 2.4 Pulse Sensor kit

2. Data Transfer Sub-System

In this sub-system, using the Wi-Fi module, provided on the LinkIt One, NodeJS runs on the LinkIt One. As the data is collected from the users, this encrypted data is chunked and transmitted to the server. The data is obtained via a periodic fetch. The LinkIT One uses the high performance Wi-Fi MT5931 which is said to provide the most convenient connectivity functions. It is of small size and low power consumption and the quality of the data transmission is very good. We use a MSSQL database to store the data. The advantage of the above is that it can be used in areas with weak Wi-Fi [7].

3. Data Display Sub-System

This sub-system consists of a cross platform app (Android and iOS) which is used to present the data to the doctor. The data is stored on the database which is retrieved onto the app and the data is plotted onto a dynamic plot and is represented as a graph in the doctor phone. If the patient’s parameters go below or beyond the medical parameters then an immediate message is transferred to the doctor, ambulance and patients relatives. We wanted this to be user friendly and hence we designed a cross platform app. Here to secure the data of a particular patient we use MQTT protocol. MQTT is a connectivity protocol. It is an extremely lightweight publish/subscribe messaging transport. It is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium. Here, it has been used in sensors communicating to a server, request connections with healthcare providers. It is also ideal for mobile applications because of its small size, low power usage, minimized data packets, and efficient distribution of information to one or many receivers.

In the app we have implemented Google OAuth. The confidentiality of each patient is maintained and only the doctor treating the given patient can access his/her details. The app has a framework as follows (Figure 2.5).

The basic flow is as follows (Figure 2.6).

The doctor can sign-in using their Gmail account as depicted (Figures 2.7 and 2.8).

The doctor can view and access his available options (Figure 2.9).

Post login there is a patient-list view page. Here, all the patients the doctor treat are given and can be easily accessed. The list will be provided as follows (Figure 2.10).

The doctor can also input the patient’s ID in a ‘Search ID’ search bar for quicker access of the data. He can also upload a patient’s data the same way. He can select ‘View Details’ to view the details of the given patient as follows (Figure 2.11).

Figure 2.5 Sign-up flowchart.

Figure 2.6 Basic flow.

Figure 2.7 Sign-in page.

The data is pulled from the database to the app and is displayed in the form of an electrocardiogram (ECG). We interface the GSM Module with the LinkIT One, so that upon a threshold being crossed (if the heart-rate is above 100 beats per minute or if it is lower than 60 beats per minute), an emergency SMS is sent to those who matter to the patient, like doctors and close relatives. The SMS indicates the ward number of the patient if he/she is in the hospital, else it provides the patient’s house address. If the threshold is crossed the graph turns red indicating danger.

Figure 2.8 Choose an account.

Figure 2.9 Options.

Figure 2.10 List of patients.

Figure 2.11 Patient details page.

2.4 Conclusion

A prototype of a system was designed and constructed which takes in signals from a medical sensor, such as a pulse sensor, performs operations on it, calculates the beats per minute, and data is transmitted to a database, from which data is retrieved and displayed on a dedicated website for medical professionals. A cross platform app is also created for the same.

We believe that this is a step forward in the field of remote patient monitoring, as patient data, i.e., critical medical parameters such as pulse, blood pressure, oxygen saturation, etc. can be monitored by medical professionals from the convenience of their offices, or even homes, and post-hospitalization care will become more accessible and efficient. Patients who live far away from medical care centers need not travel large distances, just to undergo check-up. This is a cost effective and efficient solution.

References

1. Hu, F., Xie, D., Shen, S., On the Application of the Internet of Things in the Field of Medical and Health Care. 2013 IEEE International Conference on Green Computing and Communications and IEEE, 2013.

2. Jimenez, F. and Torres, R., Building an IoT-aware healthcare monitoring system. 2015 34th International Conference of the Chilean Computer Science Society (SCCC), 2015.

3. Chiuchisan, I., Costin, H.-N., Geman, O., Adopting the Internet of Things Technologies in Health Care Systems. 2014 International Conference and Exposition on Electrical and Power Engineering (EPE 2014), Iasi, Romania, 16–18 October, 2014.

4. Luo, J., Tang, K., Chen, Y., Luo, J., Remote Monitoring Information System and Its Applications Based on the Internet of Things. 2009 International Conference on Future BioMedical Information Engineering, 2009.

5. Istepanian, R.S.H., Sungoor, A., Faisal, A., Philip, N., Internet of M-Health Things, m-IOT. Imperial College London ... Taccini, 2005.

6. Amendola, S., Lodato, R., Manzari, S., Occhiuzzi, C., Marrocco, G., RFID Technology for IoT-Based Personal Healthcare in Smart Spaces. IEEE Internet Things J., 1, 2, 144–152, 2014.

7. Stankovic, Q., Cao, Doan, T., Fang, L., He, Z., Kiran, R., Lin, S., Son, S., Stoleru, R., Wood, A., Wireless Sensor Networks for In-Home Healthcare: Potential and Challenges. 2015 34th International Conference of the Chilean Computer Science Society (SCCC).

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3An Efficient Clustering Technique for Wireless Body Area Networks Based on Dragonfly Optimization

Bilal Mehmood and Farhan Aadil*

Computer Science Department, COMSATS University Islamabad, Attock Campus, Attock, Pakistan

Abstract

Wireless body area network (WBAN) is a network of tiny health monitoring sensors, implanted or placed on the human body to collect and communicate human physiological data. WBAN used to have a connection with Medical-Server to monitor patient’s health. It is capable to protect critical patient’s lives, due to its ability to continuous remote monitoring. Inter-WBAN system provides a dynamic environment for patients to move around freely. While moving, some of the WBANs (patients) may or may not be in the range of Remoter Base Station (RBS). Here we need an efficient approach for inter-WBAN communication. In the proposed clustered-based routing technique network overhead is reduced and cluster lifetime is increased. The cluster head act as a gateway in between cluster members and external network. Moreover, for the selection of optimized cluster heads, we used evolutionary algorithms to select the most optimized solution, within the set of solutions. In the clustering approach, network efficiency is dependent on the cluster’s lifetime. The proposed technique forms efficient clusters with an increased lifetime along with remarkable network and energy efficiency.

Keywords: Wireless body area network (WBAN), energy-efficient clustering, inter-WBAN routing

3.1 Introduction

WBANs are a network of small size, lightweight, low power, wearable/implantable sensors. These sensors monitor human’s physiological activities like Patient’s Heartbeat, blood pressure, Electrocardiogram (ECG), EMG, etc. WBANs allow connectivity in between heterogeneous body sensor to a portable hub devise that provide a connection to the external internet. There are a variety of applications of the WBANs. Military can use it to monitor the physical location, physical condition, and vital signs of a field person. In the medical perspective, we can keep track patient’s physiological condition, to provide medical facilities [1]. On a single body, multiple sensors can be placed and these nodes used to form a single WBAN. Each WBAN has a centralized entity called Personal Server (PS). It gathers data from other sensors and acts as a gateway. PS has a connection to RBS directly or with multiple hops. Communication of two types, intra-WBAN and inter-WBAN occurs in the WBANs. Intra-WBAN is a communication within the sensors of a single WBAN. On the other hand, Inter-WBAN is communication among multiple WBANs. Information collected by the sensors is transmitted to the remote Medical-Server, which is situated in the hospital. Inter-WBAN communication provides dynamic access when patients are doing their normal routine work (during movement in home, office, market or playground). In this case, sensor residing on the human body may or may not be in the range of RBS. So cooperation of multiple WBANs is required for hop-to-hop communion, to reach the RBS. RBS is responsible for further transmission to Medical-Server via the internet. WBANs are capable of protecting human lives by detecting patient’s critical conditions at its early stages. Many human lives are dependent on the performance of the WBAN. Routing strategy is the key to network efficiency. There are different routing mechanisms of inter-BAN and intra-BAN communication. Each WBAN needs to be connected to the external network with the help of a gateway.

This gateway can be a Cellular device, a computer system, or a router which is capable of establishing a connection between inter-WBAN nodes and external internet. The problem arrives when WBAN do not have access to gateway device due to some reason. It is a common experience in a crowded area, like in stadium of international games or any kind of international event, where a huge amount of people access the same network and share their photos and videos. Due to congestion, degradation in the performance of network occurs. Although nowadays cellular networks provide a highly efficient network, it is not enough in some cases, that’s why a separate public safety radio system is used by police, firefighters and emergency medical technicians. It operates in separate portions of the 800 MHz band, which consists of a spectrum at 806–824 MHz paired with spectrum at 851–869 MHz. Another scenario is the battlefield where there is no Access Point available in the vicinity of every soldier as shown in Figure 3.1.

Figure 3.1 Base station is not in access of some WBANs.

Inter-WBAN communication can be useful in both of the cases. As WBAN consists of low power energy nodes, we required an efficient energy consumption routing technique. Clustering is one of the best solutions for efficient routing, where a cluster head is responsible for the transmission of data of multiple WBANs. Network efficiency is dependent on the cluster’s lifetime. In this paper, we proposed an optimization technique of clusters formation using Evolutionary Algorithms. Each cluster head (CH) is a gateway in between cluster members (PSs) of multiple WBANs and the external network. CHs are selected on the bases of fitness.

3.2 Literature Review

As the patient’s lives are dependent on the data traveling from both inter and intra-BANs network, it needs to be secured. Researchers proposed different techniques, some techniques form clusters among the sensors nodes on a single body, the reason to make these clusters is to efficiently utilize the energy of the nodes in tier 1 transmission. On the other hand, cluster formation in inter-BAN nodes of different WBANs is for efficient hop-to-hop routing for tier 2 transmission. A multi- hop routing protocol is proposed by Sriyanijnana et al., it performs well in the perspective of energy consumption, Packet Delivery Ratio (PDR) and network lifetime [2]. A number of fixed nodes are deployed in the network. A cost function is calculated for or the purpose of selection of Forwarding-node. The defined cost function is based on distance from the coordinator nodes, transmission range, residual energy, and on velocity vector of receiver. With clusters a dual sink approach used by DSCB [3], this clustering mechanism use two sinks. They also used the cost function for the purpose of selection of forwarding nodes. Forwarder node is selected by measuring distance of nodes from sink, its residual energy and transmission power. This clustering mechanism provides better performance in the prospective of network scalability, energy and an end to end delay.