Further Advances in Internet of Things in Biomedical and Cyber Physical Systems: 193 (Intelligent Systems Reference Library, 193) [1st ed. 2021] 3030578348, 9783030578343

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Intelligent Systems Reference Library 193

Valentina E. Balas Vijender Kumar Solanki Raghvendra Kumar   Editors

Further Advances in Internet of Things in Biomedical and Cyber Physical Systems

Intelligent Systems Reference Library Volume 193

Series Editors Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland Lakhmi C. Jain, Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology, Sydney, NSW, Australia; KES International, Shoreham-by-Sea, UK; Liverpool Hope University, Liverpool, UK

The aim of this series is to publish a Reference Library, including novel advances and developments in all aspects of Intelligent Systems in an easily accessible and well structured form. The series includes reference works, handbooks, compendia, textbooks, well-structured monographs, dictionaries, and encyclopedias. It contains well integrated knowledge and current information in the field of Intelligent Systems. The series covers the theory, applications, and design methods of Intelligent Systems. Virtually all disciplines such as engineering, computer science, avionics, business, e-commerce, environment, healthcare, physics and life science are included. The list of topics spans all the areas of modern intelligent systems such as: Ambient intelligence, Computational intelligence, Social intelligence, Computational neuroscience, Artificial life, Virtual society, Cognitive systems, DNA and immunity-based systems, e-Learning and teaching, Human-centred computing and Machine ethics, Intelligent control, Intelligent data analysis, Knowledge-based paradigms, Knowledge management, Intelligent agents, Intelligent decision making, Intelligent network security, Interactive entertainment, Learning paradigms, Recommender systems, Robotics and Mechatronics including human-machine teaming, Self-organizing and adaptive systems, Soft computing including Neural systems, Fuzzy systems, Evolutionary computing and the Fusion of these paradigms, Perception and Vision, Web intelligence and Multimedia. Indexed by SCOPUS, DBLP, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

More information about this series at http://www.springer.com/series/8578

Valentina E. Balas Vijender Kumar Solanki Raghvendra Kumar •

•

Editors

Further Advances in Internet of Things in Biomedical and Cyber Physical Systems

123

Editors Valentina E. Balas Department of Automatics and Applied Software Aurel Vlaicu University of Arad Arad, Romania

Vijender Kumar Solanki Department of Computer Science and Engineering CMR Institute of Technology (Autonomous) Hyderabad, Telangana, India

Raghvendra Kumar Department of Computer Science and Engineering GIET University Gunupur, Odisha, India

ISSN 1868-4394 ISSN 1868-4408 (electronic) Intelligent Systems Reference Library ISBN 978-3-030-57834-3 ISBN 978-3-030-57835-0 (eBook) https://doi.org/10.1007/978-3-030-57835-0 © Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The main objective of this book publication is to explore the concepts of Internet of Things, biomedical and cyber physical systems along with the recent research and development. It also includes various real-time applications and case studies in the field of engineering and technologies used. As populations grow and resources become scarcer, the efficient usage of these limited goods becomes more important. The content of the book is divided into four different sections.

Section I: Distributed Sensor Networks Chapter 1 discussed cluster formed by this low-energy node will terminate prematurely and waste entire network resource. ILEACH is measured one of the finest of them. To improve service life, sensor nodes with high residual energy and short distance from the base station (BS) are chosen as cluster head (CH) nodes. Then intelligently manage these nodes to create clusters to maximize the lifetime of the WSN and minimize the average energy consumption. The TDMA protocol is used for intra-cluster communication. In this article, we propose a reform of the ILEACH protocol by acquaint with cluster communication, in which the cluster heads are organized in a hierarchical structure, additional optimizing the life of the WSN. The simulation outcomes illustrate that the improved algorithm in this respect outperforms the LEACH protocol. Chapter 2 proposed framework consolidates wearable sensors to quantify physiological and natural parameters. A passage is acquainted with giving information preparing, a neighborhood web server, and a cloud association. A wearable sensor on a laborer and natural sensor on a wanderer that can transmit the information to the client by means of a door for example server, gives offer notice and cautioning component for the clients. Live health examination taken for laborers who work in an underground like tunnels, shafts, etc., it has an Individual database of laborers and contrasts it, and current essential tangible qualities separate to workplace information. Live update will screen from the control room, and it can

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direct the specialist if any medical problem occurs and furthermore can maintain a strategic distance from the undesirable passing. Chapter 3 discussed cloud computing has the implementation of traditional IT for high implementation time. The security is the main problem of the big data analytics for implementing governance and risk management. The factors for affecting the cloud computing have identified for the hybrid cloud computing. Chapter 4 focuses on DoS attacks in cognitive radio networks (CRNs). The presence of malicious users is threat for enhancing the effective spectrum utilization, and this threat may be an active or passive. In an active attack, malicious user will deliberately upset the primary user framework. A passive attack relates to the circumstance in which a malicious attack endeavors to translate source data without infusing any data or attempting to alter the data, i.e., it will tune in to the transmission without cooperating with other users. The network consists of two users such as primary users and secondary users where the main impact occurs on primary users. The network performance parameters such as packet delivery ratio, packet loss ratio, bandwidth usage and end to end delay are analyzed in CRN to detect DoS attacks. Chapter 5 indicated that the alternating deposition direction strategy allows achieving thin walls with more regular height. The roughness of the side surface of the thin walls is about 0.23 mm. The microstructure of ER70S6 thin walls changes from region by region: The upper region consists of lamellar structures; the middle region features granular structures; and the lower region shows mixed lamellar and equiaxed structures. The hardness also varies according to these three regions. The upper region shows the highest average value of hardness (
191 HV), followed by the lower region (
178 HV) and the middle region (
163 HV). Finally, the ER70S6 walls built by WAAM exhibit the anisotropy in terms of tensile strengths in the horizontal and vertical directions. Chapter 6 analyzed the factors that impact the precisional control process of the DC motor such as disturbance, the temperature effect on coil resistance and the temperature effects on magnetic fluxes. From that, we suggest a method of the actuator fault estimation to apply for the process of eliminating fault which will be performed in the future. First, a nonlinear mathematical model of the DC motor under the action of temperature is constructed to control the system. Second, building the inequalities based on the reconstruction of unknown input observer (UIO) with considering disturbance is constructed to estimate the actuator faults based on Lyapunov’s stability condition and a linear matrix inequality (LMI) optimization algorithm in order to obtain the control signal error asymptotically stable. Finally, the numerical simulation process is done to show the obtained result of the proposed method. Chapter 7 analyzed the problems occurred for maintaining big data processing and maintaining it. Hadoop Distribution File System is developed to produce the solutions for the big data challenges with the concept of acquire, organize, analyze and decide using analytic application.

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Section II: Intelligent System Design and Applications Chapter 8 discusses an objective of this application to make it interactive and make facilities to the user for solving daily problems. Citizens can use this system to search for information and send necessary documents within the electronic government for the Ministry of Commerce in client side for solving their problems. Also, it is used to complete their information in the database system by using full name and ration card number of family to enter the system. Citizen can follow this case and can find the result for this request from the system. Chapter 9 proposed a new approach by combining automatic syntactic features with pre-trained word embedding in deep learning method—Bidirectional Long Short-Term Memory (BiLSTM) for Vietnamese Named Entity Recognition. The proposed system has achieved good results for the Vietnamese NER problem on the VLSP 2016 dataset. Chapter 10 proposed a new design of convolutional neural networks (CNNs) and principal component analysis (PCA). The proposed system is estimated using five datasets, (Mhearth), (Sensors-Activity-Recognition-Dataset-SHOAIB), (REALDISP), (REALWORLD) and (Activity Recognition Dataset), where the accuracy equals 99.8%, 99.44%, 99.85%, 96.90%, 98.68%, Sequentially. Chapter 11 discussed the overall and detailed designs of the robot are presented. Also, some main research results relevant to the implementation and pilot applications of the robot are shown. It was demonstrated that the robot prototype was effectively implemented and tested with the use of TIG/MIG/MAG welding methods. The use of the robot in a welding cell reduces the production cost, improves the product quality and optimizes the manpower used in the welding process. Chapter 12 proposed a modified reversible parallel and serial adder/subtraction circuit using dual key gate (DKG) and SG. The performance of parallel adder/subtractor circuit design using dual key gate and serial adder/subtractor using dual key gate with SG is simulated and synthesized using Xilinx. The performance of this circuit is compared with existing design using Feynman gate and toffoli gate based on complexity, low power and garbage input/outputs. Chapter 13 designed by adopting the elbow inlet for generating the swirl flow. The temperature difference of steel pipes for the original furnace was 55.4°C. However, the temperature difference of a bundle of steel pipes for the new model by using downward inlet angle of 30 degrees was 13°C. Moreover, temperature difference by modified model had more uniform than those of by Prieler et al. of 50°C. Effect of Reynolds number, the horizontal and the vertical steel spacing on the temperature difference among the steel pipes were also examined. The transient simulations were performed to investigate steel pipe temperature during annealing process. In short, the results revealed that a new designed inlet configuration with this kind of geometry of an annealing furnace could be applied to minimize the temperature difference among steel pipes in steel annealing process.

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Chapter 14 designed a real-time facial expression classification the same system using Deep Convolutional Neural Networks (DCNN) that could be used for security checks in public places. The design of the system is conducted in five stages. Initially, facial expression images for the seven categories are captured, preprocessed, and loaded into the system, so that there are ten images for each of the category, namely angry, disgust, sad, fear, neutral, happy and surprise. The network architecture is then defined by repeating convolution layer, batch normalization layer, rectified linear layer (ReLU), max pooling layer three times. A fully connected layer is then defined for training followed by a SoftMax layer. Chapter 15 aimed to find the interdependency between cost and time problems in construction projects and determine which problem has major impact on the other problem to find its solution. The results show PSO is very fast in finding the interdependency between the problem and the method in searching which is very smooth as it finds the solution in the third iteration and that the effect on the problem one on two is slightly different from the two on one which means that both require solution and it’s the same applied on the problem two and three which indicate that the construction phase is very critical and require great attention.

Section III: IoT Applications in Biomedical Engineering Chapter 16 discussed a novel strategy for automated water irrigation together with a platform for pest detection, which can be used to control the water level and water the crops in agricultural lands, accordingly, is presented in this paper. The water pump is triggered depending on the water level in the soil. In addition, we have implemented a new algorithm in this method to identify the pests in the plants. It will take reasonable measures to eradicate it, depending on the nature of infection. The proposed algorithm employed is built on the extraction of appropriate features from the plant leaves, and those features are utilized for classification. A comparison of the proposed algorithm with current algorithms like k-NN and decision tree was set up to yield admirable results. Chapter 17 created a small medical system within the context of the Health Telematics software that enables specialist doctors to use telescope tracking, long-range assistance and telecommunication from mobile health providers. The system enables critical biosignals and photographs of patients to be submitted to the hospital from site. Data are transmitted over a GSM network or Wi-Fi connection. Owing to the need to share and record data during telemedicine sessions, we have aligned the consulting network with a digital database that can store and handle the “ambulance” program captured data. Chapter 18 proposed a greenhouse automation system based on Arduino for the monitoring of temperature, humidity and moisture of the soil. Arduino can obtain data on the environmental conditions of the greenhouse from various sensors and transfer the data to the ESP8266 module. Consequently, it is possible to change the state of greenhouse control devices like fans, lamp heater and water pump in obedience to the necessary conditions of the crops. These parameters are modified

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by the type of plant to maximize their growth, the Aloe Vera plant was used in this project. For the architecture of the Internet of Things was used Blynk coming from the embedded board and the communication link with the Blynk Server was through the Wi-Fi protocol. Results indicate that the system allows the control and monitoring in real time of the greenhouse correctly. As a future improvement, it is intended with the data obtained, to search for the best optimal conditions for plant growth through artificial intelligence. Chapter 19 analyzed and discussed the impact of artificial intelligence on the HRM process. Application of AI tools for screening of candidates, engagements of employees and their career enhancement have been explained thoroughly. Some challenges in HR practices based on data science are extensive HR phenomenon, and there are many restraints due to small datasets. In this chapter, we have analyzed the gaps between the realism of artificial intelligence in managing human resources and expectations. Suggestions have also been discussed for the progress to be made. The methods which have sustainable HR and talent acquisition, training through the utilization of technology, have also been discussed in the present chapter. Practical examples to understand the collaboration of working with AI are considered as well. Chapter 20 identified the emergency call headed back to the location and to monitor the congestion system in order to provide efficient facilities. This journal also sets out a method that uses a ZigBee component and Internet of Things (IoT) to transmit the treatment request from the ambulance to the nearby hospitals, while ambulance attaining the road junction, the smart traffic system which in turn changes the traffic signal cycle. This system can be implemented throughout the city thereby reducing the delay. Chapter 21 proposed a method, which will ensure road safety, women safety as well elder people safety. Overall, the major threats and difficulties faced by people will be prevented or solved using this application. Chapter 22 deals with the basic introduction to the concept of Internet of Things (IoT), which is a concept that enables a device to connect to the Internet or other devices, hence forming a giant ecosystem. The IoT platform acts as the brain of the system, while the devices linked together via IoT function as limbs.

Section IV: Cyber Physical System Framework and Applications Chapter 23 introduced multiple linear models fuzzy objectives as both the objective functions and variables coefficients (time) fuzzy numbers Trigonometric function was used to convert the fuzzy numbers to the normal formula and then construct the mathematical model and solve it using the goal programming method. Chapter 24 presented an effective technical solution for integrating and controlling a heavy robot of which all joints are driven by hydraulic actuators. The robot is designed to support workers for transferring hot and heavy workpieces between a heating furnace and a hydraulic press machine of a hot press forging shop floor. The control system of the robot is integrated mainly based on the

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industrial PLC units, which also plays a role as a central control unit for controlling all the components of the entire forging station. The control algorithm for the system integration is validated through functional tests and experiments that are carried out with a real forging shop floor at The Mechanical Company Ltd. No. 83, Yen Bai Province, Vietnam. Chapter 25 used simple IR system to compute the percentage of grams of plagiarized texts, done in two methodologies: (i) percent of plagiarism in suspicious document (a file in dataset) and (ii) percent of plagiarism in a file in dataset (to suspicious file). And then the Precision, Recall, F-measure and Error rate are estimated. In case of execution time, the proposed method is four times faster than winnowing algorithm. Chapter 26 proposed method uses Named Entity Recognition technique with a recurrent neural network model in combination with conditional random field model to extract asset features, thereby building a regression model to evaluate the price of assets based on the attribute set. The method works relatively well with a dataset of mobile phone descriptions with high accuracy. Chapter 27 discussed CrowdBC, a blockchain-based decentralized framework for crowd sourcing, within which a requester’s task are getting to be solved by a crowd of workers without trusting on any third party, users’ privacy is often guaranteed, and only low transaction fees are required. In particular, we introduce the architecture of our proposed framework, supported which we provide a concrete scheme. We further implement a software model on Ethereum public test network with real-world dataset. Experiment results show the feasibility, usability and scalability of our proposed crowd sourcing system. Chapter 28 gives a empirically investigation, what effect chatbots have on subjective well-being of the students and how subjective well-being can be measured in terms of this research context. The major findings of the study are that students’ subjective well-being has been measured into three dimensions. The first dimension of subjective well-being is life satisfaction which represented that overall students feel satisfied after conversing with the chatbot. The second dimension is positive affect which is also increasing after conversing with the chatbots. The third dimension is negative affect which is decreasing among the students after conversing with the chatbots. So, it is recommended to the college and university to implement chatbots in their premises for the wellbeing of their students. We are sincerely thankful to Almighty to supporting and standing in all times with us, whether its good or tough times and given ways to conceded us. Starting from the call for chapters till the finalization of chapters, all the editors have given their contributions amicably, which itself a positive sign of significant team works. The editors are sincerely thankful to all the members of Springer (India) Private Limited, especially Prof. (Dr.) Lakhmi C. Jain, S. Tigner and Aninda Bose for the providing constructive inputs and allowing opportunity to edit this important book. We are equally thankful to a reviewer who hails from different places in and around the globe shared their support and stand firm toward quality chapter submission. The rate of acceptance we have kept as low as 16% to ensure the quality of work submitted by author. The aim of this book is to support the computational studies at

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the research and post-graduation level with open problem-solving technique, and we are confident that it will bridge the gap for them by supporting novel solution to support in their problem solving. At the end, editors have taken utmost care while finalizing the chapter to the book, but we are open to receive your constructive feedback, which will enable us to carry out necessary points in our forthcoming books. Arad, Romania Hyderabad, India Gunupur, India

Valentina E. Balas Vijender Kumar Solanki Raghvendra Kumar

About This Book

The edited book covering the further advances in the fields of Internet of Things, biomedical engineering, and cyberphysical system with recent applications. It is covering the various real time, offline applications and case studies in the fields of recent technologies and case studies of Internet of Things, biomedical engineering, and cyberphysical system with recent technology trends. In the twenty-first century, the automation and management of data are vital, in that the role of Internet of Things proving the potential support. The book is consisting of excellent work of researchers and academician who are working in the domain of emerging technologies, e.g., Internet of Things, biomedical engineering, and cyberphysical system. The chapters covering the major achievements by solving and suggesting many unsolved problems, which am sure to be going to prove a strong support in industries toward automation goal using of Internet of Things, biomedical engineering, and cyberphysical system.

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Key Features

1. This book will provide in-depth knowledge about enhancements of Internet of Things in related fields. 2. Technical approach in solving real-time/offline Internet of Things applications in biomedical and cyberphysical system. 3. Practical solutions through case studies in Internet of Things, biomedical and cybersecurities. 4. Companies may get different ways to monitor biomedical data from various medical sensors and modify their processes accordingly to prevent unauthorized leakage of the data. 5. The interdisciplinary tools & cases of Internet of Things, biomedical and cyberphysical system.

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Contents

Part I 1

2

Distributed Sensor Networks

Energy Efficient Multi-hop Routing Techniques for Cluster Head Selection in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . G. Hemanth Kumar, G. P. Ramesh, and C. Ravindra Murthy 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Intelligent Wearable Sensor Band for Underground Working People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Karthikeyan, G. Sethuram Rao, M. S. Kowshik, P. Mohan G. Vishal, R. Juliet, P. Swetha, and T. Veronica 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Hardware Components . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Rover Implementation . . . . . . . . . . . . . . . . . 2.2.2 Specialist Implementation . . . . . . . . . . . . . . 2.2.3 Edge Gateway . . . . . . . . . . . . . . . . . . . . . . 2.3 Programming Components . . . . . . . . . . . . . . . . . . . . 2.3.1 Wearable Communication . . . . . . . . . . . . . . 2.3.2 Programming Implementation . . . . . . . . . . . 2.3.3 Cloud Implementation . . . . . . . . . . . . . . . . . 2.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Sensors’ Presentation . . . . . . . . . . . . . . . . . 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Hybrid Cloud Computing Model for Big Data Analytics in Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Sheela Daniel, S. Raja, P. Ebby Darney, and Y. Harold Robinson 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bio-Inspired Search Optimization for Intrusion Detection System in Cognitive Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . M. S. Vinmathi, M. S. Josephine, and V. Jeyabalaraja 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Impact of Security Attacks on Cognitive Network Work . . . 4.3 Algorithm for Search Optimization . . . . . . . . . . . . . . . . . . . 4.3.1 Elephant Search Algorithm (ESA) . . . . . . . . . . . . . 4.4 Implementation and Results . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Additive Manufacturing of Thin-Wall Steel Parts by Gas Metal Arc Welding Robot: The Surface Roughness, Microstructures and Mechanical Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Van Thao Le, Dinh Si Mai, Van Chau Tran, and Tat Khoa Doan 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Materials and Experimental Methods . . . . . . . . . . . . . . . . . 5.3 Experimental Results and Discussion . . . . . . . . . . . . . . . . . 5.3.1 Effects of Depositing Strategies on the Shape of Thin Walls . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Surface Roughness Analysis . . . . . . . . . . . . . . . . . 5.3.3 Microstructure Analysis . . . . . . . . . . . . . . . . . . . . 5.3.4 Mechanical Properties . . . . . . . . . . . . . . . . . . . . . . 5.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . An Application of Observer Reconstruction to Estimate Actuator Fault for DC Motor Nonlinear System Under Effects of the Temperature and Disturbance . . . . . . . . . . . . . . . . . . . . . Tan Van Nguyen and Xuan Vinh Ha 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 DC Motor Model Formulation . . . . . . . . . . . . . . . . . . . . . . 6.3 UIO Design for Nonlinear System . . . . . . . . . . . . . . . . . . . 6.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Simulation Without Disturbance d ðtÞ ¼ 0 . . . . . . . . 6.4.2 Simulation with Disturbance d ðtÞ ¼ 0:025 randomð2; tÞ . . . . . . . . . . . . . . . . . . . . . . . .

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6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Enhanced Hadoop Distribution File System for Providing Solution to Big Data Challenges . . . . . . . . . . . . . . . . . . . . A. Essakimuthu, R. Karthik Ganesh, R. Santhana Krishnan, and Y. Harold Robinson 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Solution for Big Data Challenges . . . . . . . . . . . . . . . 7.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . 7.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Intelligent System Design and Applications

Electronic Public Distribution System in Electronic Government . . . . . . . . . . . . . . . . . . . . . . . Israa M. Hayder, Dalshad J. Hussein, Hussain A. Younis, and Hameed Abdul-Kareem Younis 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 E-Government . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Materials and Methods . . . . . . . . . . . . . . . . . . . . 8.4.1 Application Servers (Tomcat Apache) . . . 8.4.2 Proposed E-Government Schemes . . . . . . 8.5 Result and Discussion . . . . . . . . . . . . . . . . . . . . . 8.5.1 E-Governments in the Middle East . . . . . 8.5.2 Futures This Project . . . . . . . . . . . . . . . . 8.5.3 Running the Project . . . . . . . . . . . . . . . . 8.5.4 Main Page . . . . . . . . . . . . . . . . . . . . . . . 8.5.5 User Page . . . . . . . . . . . . . . . . . . . . . . . 8.5.6 Main Site for Use . . . . . . . . . . . . . . . . . . 8.5.7 Staff Page . . . . . . . . . . . . . . . . . . . . . . . 8.5.8 Admin Main Page . . . . . . . . . . . . . . . . . 8.5.9 Searching Operation . . . . . . . . . . . . . . . . 8.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Combining Syntax Features and Word Embeddings in Bidirectional LSTM for Vietnamese Named Entity Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Bui Thanh Hung 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 9.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

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Proposed Method . . . . . . . . . . . . . . . . 9.3.1 Word Embeddings . . . . . . . . . 9.3.2 Bidirectional Long Short Term Memory (Bi-LSTM) . . . . . . . . 9.4 Experiments . . . . . . . . . . . . . . . . . . . . 9.4.1 VLSP Dataset . . . . . . . . . . . . . 9.4.2 Experiment Results . . . . . . . . . 9.5 Conclusion . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . .

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10 Human Activity Recognition by Deep Convolution Neural Networks and Principal Component Analysis . . . . . . . . . . Amir A. Aljarrah and Ali H. Ali 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Deep Learning Architectures . . . . . . . . . . . . . . . . . . 10.4.1 Convolutional Neural Network (CNN) . . . . . 10.4.2 Convolutional Layer (CL) . . . . . . . . . . . . . . 10.4.3 Batch Normalization Layer . . . . . . . . . . . . . 10.4.4 Rectified Linear Unit (ReLU) Layer . . . . . . . 10.4.5 Pooling Layer . . . . . . . . . . . . . . . . . . . . . . . 10.4.6 Fully-Connected Layer . . . . . . . . . . . . . . . . 10.4.7 Softmax Layer . . . . . . . . . . . . . . . . . . . . . . 10.4.8 Classification Layer . . . . . . . . . . . . . . . . . . 10.5 Architecture of the Method . . . . . . . . . . . . . . . . . . . 10.5.1 Data Acquisition . . . . . . . . . . . . . . . . . . . . . 10.5.2 Normalization of Data . . . . . . . . . . . . . . . . . 10.5.3 Principle Component Analysis . . . . . . . . . . . 10.6 Experiments and Results . . . . . . . . . . . . . . . . . . . . . 10.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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11 Development of a New 6 DOFs Welding Robotic System for a Specialized Application . . . . . . . . . . . . . . . . . . . . . . . . Truong Trong Toai, Duc-Hoang Chu, and Chu Anh My 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.1 TIG Welding Method . . . . . . . . . . . . . . . . . . 11.1.2 MIG/MAG Welding Method . . . . . . . . . . . . . 11.1.3 Plasma Arc Welding (PAW) . . . . . . . . . . . . . 11.1.4 Submerged-Arc Welding (SAW) . . . . . . . . . . 11.1.5 Laser Beam Welding (LBW) . . . . . . . . . . . . . 11.1.6 Industrial Robots . . . . . . . . . . . . . . . . . . . . . 11.2 Design of a 6DOFs Welding Robot . . . . . . . . . . . . . . 11.2.1 Functional Requirements . . . . . . . . . . . . . . . . 11.2.2 The Overall Structure of the Welding System .

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11.2.3 The Welding Arm . . . . . . . . . . . . 11.2.4 The Workspace of the Robot . . . . . 11.2.5 The Control Software of the Robot 11.3 Implementation and Testing for the Robot . 11.3.1 Implementation of the Robot . . . . . 11.3.2 Accuracy Analysis . . . . . . . . . . . . 11.3.3 Testing and Application . . . . . . . . 11.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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12 Low Power Reversible Parallel and Serial Binary Adder/Subtractor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Bhuvaneswary, S. Prabu, S. Karthikeyan, R. Kathirvel, and T. Saraswathi 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Realization of Basic Reversible Logic . . . . . . . . . 12.3 Design of Reversible Parallel Adder/subtractor . . . 12.4 Design of Reversible Serial Adder/Subtractor . . . . 12.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.6 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 12.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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13 Computational Fluid Dynamics Analysis of Reduction of Temperature Difference for a Bundle of Steel Pipes Inside Annealing Furnace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lam Hai Dinh, Tan Van Nguyen, and Tu Thien Ngo 13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 Annealing Furnace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.1 The Original Annealing Furnace . . . . . . . . . . . . . 13.2.2 The New Designed Annealing Furnace . . . . . . . . 13.3 Mathematical Model and Boundary Conditions . . . . . . . . . 13.4 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4.1 The Flow Field and Steel Pipes Temperature of the Basic Annealing Furnace . . . . . . . . . . . . . . 13.4.2 The Flow Field and Steel Pipes Temperature of the Modified Annealing Furnace . . . . . . . . . . . 13.4.3 Effect of Reynolds Number at the Inlet, the Horizontal and Vertical Steel Spacing on the Steel Temperature Difference Among Steel Pipes . . . . . 13.5 Transient Simulation of Steel Pipes Bundle Temperature During Annealing Process . . . . . . . . . . . . . . . . . . . . . . . . 13.6 Total Deformation of Steel Pipes During Annealing Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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14 A DCNN Based Real-Time Authentication System Using Facial Emotions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Praveen Edward James, Mun Hou Kit, and T. Anthony Snow Ritta 14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2 Design Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.1 Image Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.2 Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.3 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.4 Convolution Layer . . . . . . . . . . . . . . . . . . . . . . . . . 14.3 Algorithmic Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.4.1 Experiment 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.4.2 Experiment 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Interdependence of Construction Projects Problems Using PSO . Abtehaj Hussein, Rouwaida Hussein Ali, Hafeth I. Naji, and Naji Muter 15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part III

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IoT Applications in Biomedical Engineering

16 Automated Irrigation System with Pest Detection Using IoT with OTSU Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. V. Kalpana, T. Chandrasekar, S. Rukmani Devi, and T. C. Jermin Jeaunita 16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2 Background and Related Work . . . . . . . . . . . . . . . . . . 16.3 Proposed Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.5 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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17 IoT Based Telemedicine System . . . . . . . . . . . . . . . . . . . . . . . . . . L. K. Hema, Rajat Kumar Dwibedi, R. Karthikeyan, and V. Vanitha 17.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2 Biomedical Automation in Internet-of-Things . . . . . . . . . . . . 17.3 Existing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.4 Proposed System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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17.4.1 Sensing, Processing, Communication Plane . . . . 17.4.2 Data Concentration and Cloud Computing . . . . . 17.4.3 Analytics, Pre and Post Processing by Physician 17.5 Data Acquisition and Transmission in Ambulance Using IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.6 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . 17.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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18 Tracking Greenhouses Farming Based on Internet of Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sebastián Gutiérrez, Rafael Rocha, David Rendón, Juan Carlos Bernabé, Luis Aguilera, and Vijender Kumar Solanki 18.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2 Aloe Vera Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2.1 Soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2.2 Irrigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2.3 Indoor Cultivation . . . . . . . . . . . . . . . . . . . . . . . 18.3 System Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.4 Hardware of the System . . . . . . . . . . . . . . . . . . . . . . . . . 18.4.1 Temperature and Humidity Sensor . . . . . . . . . . . . 18.4.2 Light Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.4.3 Water Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.4.4 Actuator and Power Supply . . . . . . . . . . . . . . . . . 18.5 Data Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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19 Impact of Internet of Things and Artificial Intelligence on Human Resource Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sonal Pathak and Vijender Kumar Solanki 19.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.1.1 Employees as Resource . . . . . . . . . . . . . . . . . . . . . 19.1.2 Management of Human Resource . . . . . . . . . . . . . 19.2 Traditional Methods of Human Resource Management Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.3 Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.3.1 Importance of Artificial Intelligence in Transitioning Human Resource Management Practices . . . . . . . . 19.3.2 How Artificial Intelligence is Reinventing Human Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.4 Comparison Between Traditional HRM and AI-Based HRM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.5 Implications of Artificial Intelligence in Human Resources Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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19.6

Limitations of Artificial Intelligence Based HR Tools 19.6.1 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . 19.6.2 Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . 19.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.8 Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

20 IoT Based Intelligent Ambulance Monitoring and Traffic Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Jijin Godwin, B. V. Santhosh Krishna, R. Rajeshwari, P. Sushmitha, and M. Yamini 20.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.2 Proposed System . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.2.1 Block Diagram . . . . . . . . . . . . . . . . . . . . . . 20.2.2 Hardware Components . . . . . . . . . . . . . . . . 20.3 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.3.1 Preemption Strategy . . . . . . . . . . . . . . . . . . 20.3.2 Path Selection Strategy . . . . . . . . . . . . . . . . 20.4 Result and Discussion . . . . . . . . . . . . . . . . . . . . . . . 20.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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21 IoT Based Emergency Alert System . . . . . . . . . . . . . . . . . . . . K. Chinnusamy, D. Nandhini, A. S. Subhashri, M. Hemavathy, V. Baskar, M. Kavimani, and M. Vignesh 21.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.1 Wireless Alert Through Internet of Things (IoT) 21.2.2 PIC 16F877A . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.3 Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3.1 Zigbee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4.1 Geo Fencing . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.5 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 21.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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22 An Industrial Internet of Things Approach for Pharmaceutical Industry Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deepak Kumar Sharma, Gurmehak Kaur, and Mohita Sharma 22.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.1.1 Introduction to IoT . . . . . . . . . . . . . . . . . . . . . . . 22.1.2 Introduction to Pharmaceutical Industry . . . . . . . . 22.2 Background and Research on IoT . . . . . . . . . . . . . . . . . .

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22.3

Technologies Used with IoT . . . . . . . . . . . . . . . . . . . . 22.3.1 Short Range IoT Network . . . . . . . . . . . . . . . . 22.3.2 Medium Range IoT Network . . . . . . . . . . . . . . 22.3.3 Long Range IoT Network . . . . . . . . . . . . . . . . 22.4 Slow Growth in the Pharma Industry . . . . . . . . . . . . . . 22.5 IoT in Pharma Industry . . . . . . . . . . . . . . . . . . . . . . . . 22.6 Advantages of IoT in the Pharmaceutical Industry . . . . 22.6.1 IoT Solves the Previously Discussed Problems in the Following Ways . . . . . . . . . . . . . . . . . . 22.6.2 Uses and Advantages of IoT in the Pharma Industry . . . . . . . . . . . . . . . . . . 22.7 Architecture of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.8 Challenges and Drawbacks of IoT . . . . . . . . . . . . . . . . 22.8.1 R&D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.8.2 Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . 22.8.3 Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . 22.9 Applications of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.10 Future of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.11 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Cyber Physical System Framework and Applications

23 Solving the Problem of Fuzzy Transportation Using Linear Programming and Goal Programming . . . . . . . . . . . . . . . . . Hasanain Hamid Ahmed 23.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.2 Concept of the Transport Problem . . . . . . . . . . . . . . . . 23.3 Methods of Solution of Transport Model . . . . . . . . . . . 23.4 Concept of the Goal Programming . . . . . . . . . . . . . . . . 23.5 Methods of Solving Goal Programming . . . . . . . . . . . . 23.6 Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.6.1 Functions Affiliation to the Fuzzy Group . . . . . 23.6.2 Linear Affiliation Function . . . . . . . . . . . . . . . 23.7 Robust Ranking Method . . . . . . . . . . . . . . . . . . . . . . . 23.8 Mathematical Model of the Problem of Multi-Objective Fuzzy Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . 23.9 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.9.1 Building the Mathematical Model of the Fuzzy Transport Problem . . . . . . . . . . . . . . . . . . . . . 23.10 Explanation the Results . . . . . . . . . . . . . . . . . . . . . . . . 23.11 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contents

24 Effective Solution to Integrate and Control a Heavy Robot Driven by Hydraulic Actuators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chu Anh-My 24.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.2 Description of the Robotic Cell . . . . . . . . . . . . . . . . . . . . . 24.3 The Control System Design . . . . . . . . . . . . . . . . . . . . . . . . 24.4 Validation and Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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25 Design and Implementation of Arabic Plagiarism Detection System . . . . . . . . . . . . . . . . . . . . . . . . . . Zahraa Jasim Jaber and Ahmed H. Aliwy 25.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 25.2 Literature Survey . . . . . . . . . . . . . . . . . . . . 25.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.4 Outline of the Proposed System . . . . . . . . . . 25.5 Implementation and Results . . . . . . . . . . . . . 25.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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26 Application of Artificial Intelligence to Asset Pricing by Vietnamese Text Declaration . . . . . . . . . . . . . . . . . . Tran Ngoc Thang, Dao Minh Hoang, Tran Thi Hue, Vijender Kumar Solanki, and Nguyen Thi Ngoc Anh 26.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.2 Theoretical Preliminary . . . . . . . . . . . . . . . . . . . . 26.2.1 Named Entity Recognition . . . . . . . . . . . 26.2.2 Artificial Neural Network . . . . . . . . . . . . 26.2.3 Conditional Random Field . . . . . . . . . . . 26.2.4 Decision Tree . . . . . . . . . . . . . . . . . . . . . 26.3 Solution Overview . . . . . . . . . . . . . . . . . . . . . . . 26.3.1 Phase 1 . . . . . . . . . . . . . . . . . . . . . . . . . 26.3.2 Phase 2 . . . . . . . . . . . . . . . . . . . . . . . . . 26.4 Computational Experiments . . . . . . . . . . . . . . . . . 26.4.1 Data Description . . . . . . . . . . . . . . . . . . . 26.4.2 Implementation and Evaluation of Model . 26.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contents

27 A Survey on Decentralized Crowdsourcing Using Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . M. Preetha, K. Elavarasi, A. Mani, E. Pavithra, P. Sudharshna, and S. Rakshini 27.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.1.1 Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.1.2 Working of Blockchain . . . . . . . . . . . . . . . . . . 27.1.3 Smart Contract . . . . . . . . . . . . . . . . . . . . . . . . 27.1.4 Crowdsourcing . . . . . . . . . . . . . . . . . . . . . . . . 27.1.5 Consensus Algorithms . . . . . . . . . . . . . . . . . . 27.2 Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.3 Real Time Examples . . . . . . . . . . . . . . . . . . . . . . . . . . 27.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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28 A Study of Student’s Subjective Well-Being Through Chatbot in Higher Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shivani Agarwal and Nguyen Thi Dieu Linh 28.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.1.1 Background About Chatbot . . . . . . . . . . . . . . . . . 28.1.2 Application of Chatbot in Business . . . . . . . . . . . 28.1.3 Scope of the Study . . . . . . . . . . . . . . . . . . . . . . . 28.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.3.1 Participants and Procedure . . . . . . . . . . . . . . . . . 28.3.2 Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.3.3 Objectives of the Study . . . . . . . . . . . . . . . . . . . . 28.3.4 Hypothesis of the Study: . . . . . . . . . . . . . . . . . . . 28.4 Result and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.6 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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About the Editors

Valentina E. Balas, Ph.D. is currently Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a Ph.D. in applied electronics and telecommunications from Polytechnic University of Timisoara. Dr. Balas is author of more than 270 research papers in refereed journals and international conferences. Her research interests are in intelligent systems, fuzzy control, soft computing, smart sensors, information fusion, modeling, and simulation. She is the Editor-in-Chief to International Journal of Advanced Intelligence Paradigms (IJAIP) and to International Journal of Computational Systems Engineering (IJCSysE), member in Editorial Board member of several national and international journals and is evaluator expert for national and international projects. She served as General Chair of the International Workshop Soft Computing and Applications in seven editions 2005–2016 held in Romania and Hungary. Dr. Balas participated in many international conferences as Organizer, Session Chair, and member in International Program Committee. Now she is working in a national project with EU funding support: BioCell-NanoART = Novel Bio-inspired Cellular Nano-Architectures—For Digital Integrated Circuits, 2M Euro from National Authority for Scientific Research and Innovation. She is a member of EUSFLAT, ACM and a Senior Member IEEE, member in TC—Fuzzy Systems (IEEE CIS), member in TC—Emergent Technologies (IEEE CIS), member in TC—Soft Computing (IEEE SMCS). Dr. Balas was Vice-president (Awards) of IFSA International Fuzzy Systems Association Council (2013–2015) and is a Joint Secretary of the Governing Council of Forum for Interdisciplinary Mathematics (FIM), A Multidisciplinary Academic Body, India and recipient of the “Tudor Tanasescu” Prize from the Romanian Academy for contributions in the field of soft computing methods (2019). Vijender Kumar Solanki, Ph.D. is an Associate Professor in Computer Science & Engineering, CMR Institute of Technology (Autonomous), Hyderabad, TS, India. He has more than 14 years of academic experience in network security, IoT, Big Data, Smart City and IT. Prior to his current role, he was associated with xxix

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About the Editors

Apeejay Institute of Technology, Greater Noida, UP, KSRCE (Autonomous) Institution, Tamilnadu, India and Institute of Technology & Science, Ghaziabad, UP, India. He is member of ACM and Senior Member IEEE. He has attended an orientation program at UGC-Academic Staff College, University of Kerala, Thiruvananthapuram, Kerala & Refresher course at Indian Institute of Information Technology, Allahabad, UP, India. He has authored or co-authored more than 60 research articles that are published in various journals, books and conference proceedings. He has edited or co-edited 14 books and Conference Proceedings in the area of soft computing. He received Ph.D. in Computer Science and Engineering from Anna University, Chennai, India in 2017 and ME, MCA from Maharishi Dayanand University, Rohtak, Haryana, India in 2007 and 2004, respectively and a bachelor's degree in Science from JLN Government College, Faridabad Haryana, India in 2001. He is the Book Series Editor of Internet of Everything (IoE): Security and Privacy Paradigm, CRC Press, Taylor & Francis Group, USA; Artificial Intelligence (AI): Elementary to Advanced Practices Series, CRC Press, Taylor & Francis Group, USA; IT, Management & Operations Research Practices, CRC Press, Taylor & Francis Group, USA and Computational Intelligence and Management Science Paradigm, (Focus Series) CRC Press, Taylor & Francis Group, USA. He is Editor-in-Chief in International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) ISSN 2581-3242 ; International Journal of Hyperconnectivity and the Internet of Things (IJHIoT), ISSN 2473-4365, IGI-Global, USA, Co-Editor Ingenieria Solidaria Journal ISSN (2357-6014), Associate Editor in International Journal of Information Retrieval Research (IJIRR), IGI-GLOBAL, USA, ISSN: 2155-6377 | E-ISSN: 2155-6385 . He has been guest editor with IGI-Global, USA, InderScience & Many more publishers. He can be contacted at [email protected] Dr. Raghvendra Kumar is working as Associate Professor in Computer Science and Engineering Department at GIET University, India. He received B. Tech, M.Tech., and Ph.D. in computer science and engineering, India, and Postdoc Fellow from Institute of Information Technology, Virtual Reality and Multimedia, Vietnam. He serves as Series Editor Internet of Everything (IOE): Security and Privacy Paradigm, Green Engineering and Technology: Concepts and Applications, published by CRC press, Taylor & Francis Group, USA, and Bio-Medical Engineering: Techniques and Applications, published by Apple Academic Press, CRC Press, Taylor & Francis Group, USA. He also serves as acquisition editor for Computer Science by Apple Academic Press, CRC Press, Taylor & Francis Group, USA. He has published number of research papers in international journal (SCI/SCIE/ESCI/Scopus) and conferences including IEEE and Springer as well as serve as organizing chair (RICE-2019, 2020), volume editor (RICE-2018), keynote speaker, session chair, co-chair, publicity chair, publication chair, advisory board, technical program committee members in many international and national conferences and serve as guest editors in many special issues from reputed journals

About the Editors

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(Indexed By: Scopus, ESCI, SCI). He also published 13 chapters in edited book published by IGI Global, Springer, and Elsevier. His research areas are computer networks, data mining, cloud computing, and secure multiparty computations, theory of computer science and design of algorithms. He authored and edited 23 computer science books in fields of Internet of Things, data mining, biomedical engineering, big data, robotics, and IGI Global Publication, USA, IOS Press Netherland, Springer, Elsevier, CRC Press, USA.

Part I

Distributed Sensor Networks

Chapter 1

Energy Efficient Multi-hop Routing Techniques for Cluster Head Selection in Wireless Sensor Networks G. Hemanth Kumar, G. P. Ramesh, and C. Ravindra Murthy

Abstract Sensors usually operate on battery power, which limits energy consumption. Energy efficient optimization algorithms allow nodes to use smartly without wasting battery consumption. Hierarchical routing protocol is the finest recognized protocol for improving power consumption in wireless sensor network. The LEACH protocol not take into account the remaining energy of the node when selecting the cluster header, it is possible to select a node with a slightly lower energy as the cluster header. In this way, the cluster formed by this low-energy node will terminate prematurely and waste entire network resource. ILEACH is measured one of the finest of them. To improve service life, sensor nodes with high residual energy and short distance from the base station (BS) are chosen as cluster head (CH) nodes. Then intelligently manage these nodes to create clusters to maximize the lifetime of the WSN and minimize the average energy consumption. The TDMA protocol is used for intra-cluster communication. In this article, we propose a reform of the ILEACH protocol by acquaint with cluster communication, in which the cluster heads are organized in a hierarchical structure, additional optimizing the life of the WSN. The simulation outcomes illustrate that the improved algorithm in this respect outperforms the LEACH protocol. Keywords LEACH · ILEACH · OHILEACH · TDMA

G. Hemanth Kumar (B) · C. Ravindra Murthy Department of E.I.E, Sree Vidyanikethan Engineering College, Tirupati, Andhrapradesh, India e-mail: [email protected] G. P. Ramesh Department of ECE, St. Peter’s Institute of Higher Education and Research, Avadi, Chennai, Tamilnadu, India e-mail: [email protected] © Springer Nature Switzerland AG 2021 V. E. Balas et al. (eds.), Further Advances in Internet of Things in Biomedical and Cyber Physical Systems, Intelligent Systems Reference Library 193, https://doi.org/10.1007/978-3-030-57835-0_1

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1.1 Introduction Wireless Sensor Network (WSN) is a group of a variety of sensor nodes with limited thorough, reckoning, and Communication abilities these sensors are positioned extensively with one or more base stations. WSN offers a wide range of applications such as pressure, humidity, temperature, military scouting disaster management, and forest inspection etc. [1]. State-of-the-art setups, sensor nodes with limited battery power are used randomly. The choice of routing methods is an important issue for the efficient provision of collected data from source to destination. The routing techniques are used in these category of networks must ensure minimum energy depletion, as sensor battery replacement is often impossible. The choice of routing methods is a significant issue for Distribute sensing data efficiently from source to destination. The routing approaches used in these categories of networks often make it impossible to replace the sensor battery, so they need to guarantee minimal energy consumption. Depending on the application and network architecture, for WSN has proposed and developed numerous energy-efficient routing protocols [2]. However, the rechargeable batteries used in the WSN will not be recharged, if it is recharged, so an improvement is required: Therefore, to improve performance, you need to improve the lifetime of the network and this can be analysed with the available node energy as data is transferred from the source to the target. Based on the organization of sensor networks, routing can be categorized as flat, location-based routing and hierarchical routing [3]. Clustering algorithms offer an energy-efficient technique to exploit the lifespan of WSNs by splitting the sensor nodes into clusters, which would then have the option of internally choosing a cluster header. Every cluster header would then gather packets from all nodes in the cluster and send information to the central station. The selection of cluster heads is one of the best-known cluster algorithms i.e. LEACH (Low Energy Adaptive Clustering Hierarchy). Each sensor node is assigned the probability of becoming a cluster head [4]. Hierarchical routing offers enhanced energy effectiveness and ascend ability due to its structure. This type of protocol is separated into network groups, and some nodes are selected as superior nodes based on specific standards. These superior nodes are known as cluster heads (CHS), acquire, integrate, and suppress the data acquired from neighbouring nodes, and eventually compile information to the Base station. The cluster head provides extra facilities for additional nodes and therefore uses more power than other nodes in the cluster. Cluster rotation is mutual technique to reduce rotational energy inside the cluster. The rest HR protocol was anticipated by Zelman et al. [5] well-known as LEACH. In this document, we suggest the improved LEACH (ILEACH) protocol, which selects cluster heads based on various thresholds. The probability of the new cluster head selection is calculated the number of initial energy and adjacent nodes. On a rotation basis, a headset member receives and sends data from neighbouring nodes Total results to the base station. Data Acquisition For a given number of sensor nodes, the number of control and management nodes can be adjusted to systematically reduce the number of system nodes energy consumption, which increases the lifetime of the network [6].

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1.2 Related Work The author discussed Comparison of performance between particle swarm optimization and genetic algorithms by a new cost function, the determination of which is to minimize distance within a cluster and optimize network power consumption [7]. Improved clustering routing algorithm built on LEACH and PEGASIS based load balancing in WSN. In clustering, the node removes the node from the cluster head, removes the CH to the base station, and combines clusters with a minimum of complex distance. This strategy balances the grid energy efficiently [8]. With a single transition, a trust mechanism that identifies and eliminates malicious nodes established among sending and neighbouring nodes, hybrid games with unceasing monitoring and forwarding strategies, which can efficiently decrease error rates when detecting packet loss on unreliable wireless channels [9]. To suppress attacks by attackers, the author develop security mechanisms based on monitoring nodes, which are heterogeneous energy nodes that have only monitoring but no forwarding capabilities [10]. In the structure of a cluster network, sensor nodes are alienated into clusters. The Cluster Head is not merely answerable for supervision of sensor member nodes, but also controls the aggregation and transfer of data among clusters. The oldest standard WSN clustering protocol is LEACH, which dynamically creates clusters to optimize network power consumption [11]. M-GEAR is a location-based protocol that divides the sensor field into four logical areas based on their position in the sensing field. In this algorithm, if the distance from the sensor node to the BS exceeds a predetermined threshold and the connection between the sensor node and the BS is based on the cluster, the centre of the sensitive field has a gateway sensor node [12]. The HEED algorithm is a common generic routing protocol. In particular, the HEED algorithm synthesizes the mixture of the residual energy and the secondary parameters of the node for the periodic selection of the cluster head channel. In spite of the fact that this algorithm speeds up clustering and reduces intercluster communication costs, the inter-cluster competition eliminates the possibility of connecting some sensor nodes to the cluster [13–16] .

1.3 Methodology Assume an opaque sensor network of uniform, energy-constrained nodes the data should be reported to the aggregation node. In LEACH, integrated with TDMA-based MAC protocol Clusters and simple “routing” protocols. LEACH divides nodes into clusters and divides a dedicated node, cluster head, in each cluster is in control for making and retaining TDMA schedules and other nodes of the cluster are participating nodes [14]. All participating nodes are assigned TDMA time slots that can be used to exchange data between the participant and the cluster head. With the exclusion of their time intervals, participants can spend their time in a state of sleep. The cluster head equals the acquired data of its participants and transfers it to the sink

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node or other participant nodes for further processing. Each node decides whether it will become the cluster header independently of the other nodes, so there is no signalling traffic associated with the choice of cluster header. The protocol is based on loops, that is, all nodes decide whether to be cluster headers at the same time, and then the non-cluster header nodes must be associated with the cluster header. The non-cluster header selects the cluster header based on the received signal level. A cluster-divided network is a time variable, and the protocol assumes global time synchronization. Once cluster has been formed, each cluster head selects a random CDMA code for its cluster. The content that it sends, and what it needs to use by member nodes. The protocol is structured in cycles and each cycle is divided into an installation phase and a stable state phase (Fig. 1.1). The installation phase starts from the node to the cluster head. In the next stage of advertising, the head of the cluster informs its neighbours with an Advertising packet. The heads of the cluster use the CSMA protocol to fight for media without additional conditions for unknown terminal problems. The non-cluster head node selects the ad package with the strongest received signal level. In the next cluster setup step, participants use the CSMA protocol again to notify their cluster heads. After TDMA scheduling randomly selects CDMA codes and broadcasts information to plan the sub-step. Then, the steady state TDMA mode begins. Due to conflicts in advertising or joining data packages, the protocol has no guarantee that non-clustered nodes are part of a cluster. However, this can ensure that the nodes belong to the maximum one cluster. Open the cluster head during the whole round and you need to switch member nodes Start with the setup phase,

Fig. 1.1 Organization of LEACH cycles

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sometimes in the steady state phase, depending on their location to schedule TDMA cluster. According to the protocol defined, LEACH will not be able to cover large topographical areas of square miles, because clusters of heads two miles from the shell can Not enough energy to reach the receiver. This may be the limit if you can schedule a cluster header to be sent using other cluster headers. During the cluster setup stage, the node will randomly generate a number from 0 to 1 and including 0 and 1. Suppose the generated random number is less than the threshold T(n), the node in the round will become the head of the cluster. The calculation of T(n) is based on the subsequent formula:

T(n) =

⎧ ⎪ ⎨ ⎪ ⎩

p    , N ∈ G 1 − P r mod 1p 0,

otherwise

In the above equation, p is the percentage of Cluster nodes take into account the total number of nodes, i.e. The probability that a node will become the head of a cluster; r refers to current rounds (cycles), N is the number of nodes; G is a set of nodes that not come to be cluster head in 1/p cycle. The node selected as the cluster header is then sent to its neighbouring node as cluster header information, and the remaining nodes select the cluster to join and notify the corresponding cluster header in accordance with the strength of the broadcast signal it receives. The cluster head then creates the TDMA, creates a time slot for each node in the cluster, and sends them to them in a broadcast form. Thus, each node can send data in its own time interval, while in another time interval the node goes into sleep mode, which saves energy. During a stable data transfer phrase, member nodes (non-clustered nodes) in the cluster will transmit the tracked data to the corresponding cluster heads for a given time interval. The transmitted phrase can be divided into several frames; each frame is determined by the number of nodes in the cluster. The data that each node sends in its own time interval is only part of the frame. At the end of each round, the heads of the clusters and groups will be re-elected, which requires a certain amount of energy. To reduce the load on the system, the duration of each round of stabilization is much longer than the cluster creation time 6. For a cluster header, it remains in a communication state, so that it can receive the acquired data from nodes in its cluster every time. Once all the data has been received from the participating nodes, the cluster head will process data such as data fusion to reduce redundant data. Finally, the cluster head transmits the merged data to its own cluster head, for non-clustered nodes they transmit data at their own time interval, and at other times they turn off the wireless module to save power.

1.4 Results See Figs. 1.2 and 1.3.

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Fig. 1.2 Round number versus average energy

Fig. 1.3 Round number versus dead nodes

1.5 Conclusion OHILEACH is a generic routing algorithm for cluster routing protocols that has several advantages. Selecting the cluster header causes premature disconnection of the node, resulting in loss of network attributes. When choosing a cluster head, consider node energy stops so that low energy nodes do not become cluster heads. We propose an ILEACH protocol reform that is accustomed to cluster communication. Cluster communication is organized in a hierarchy to further optimize the life of the

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WSN. In this research, we experiential that the OHILEACH protocol attains the best energy organization intervals, such as the number of rounds compared to the average energy and the number of rounds compared to the dead node, resulting in the longest lifetime.

References 1. Pino-Povedano, S., Arroyo-Valles, R., Cid-Sueiro, J.: Selective forwarding for energy-efficient target tracking in sensor networks. Signal Process. 94, 557–569 (2014) 2. Pantazis, N.A., Nikolidakis, S.A., Vergados, D.D.: Energy efficient routing protocols in wireless sensor networks: a survey. IEEE Commun. Surv. Tuts. 15(2), 551–591 (2013) 3. Karaki. A.L., Kamal, A.E. Routing techniques in wireless sensor networks: a survey. IEEE Wirel. Commun. 6–28. 4. Fu, C., Jiang, Z., Wei, W.E.I., Wei, A.: An energy balanced algorithm of LEACH protocol in WSN. Int. J. Comput. Sci. 10(1), 354–359 (2013) 5. Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: ‘Energy efficient communication protocol for wireless microsensor networks. In: Proceedings of 33rd Annual Hawaii International Conference on System Science, vol. 2, p. 10 (2000). 6. Bingcai, C., Huazhuo, Y., Mingchuan, Y., et al.: A intercluster multi-hop routing protocol improved based on LEACH protocol. Chin. J. Sens. Actuat. 27(3), 373–377 (2014) 7. Latiff, N.M.A., Tsimenidis, C.C., Sharif, B.S.: Performance comparison of optimization algorithms for clustering in wireless sensor networks. In: Proceedings of the IEEE International Conference on Mobile Adhoc and Sensor Systems, Pisa, Italy, pp. 1–4 (2007) 8. Zhang Z., Zhang, X.: Research of improved clustering routing algorithm based on load balance in wireless sensor networks. In: Proceedings of the IET International Communication Conference on Wireless Mobile and Computing, Shanghai, China, pp. 661–664 (2009) 9. Liao, H., Ding, S.: Mixed and continuous strategy monitor-forward game based selective forwarding solution in WSN. Int. J. Distrib. Sens. Netw. 2015, 35978 (2015) 10. Hu, Y., Wu, Y.M., Wang, H.S.: Detection of insider selective forwarding attack based on monitor node and trust mechanism in WSN. Wirel. Sens. Netw. 6, 237–248 (2014) 11. Heinzelman, W.R., Chandrakasan, A.P., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Maui, HI, USA, 7 January 2000, pp. 1–10 12. Nadeem, Q., Rasheed, M.B., Javaid, N., Khan, Z.A., Maqsood, Y., Din, A.: M-GEAR: Gateway-based energy-aware multi-hop routing protocol for WSNs. In: Proceedings of the 2013 Eighth International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA), Compiegne, France, 28–30 October 2013, pp. 164–169 13. Lin, C.H., Tsai, M.J.: A comment on “HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks.” IEEE Trans. Mob. Comput. 5, 1471–1472 (2006) 14. Sipon Miah, M., Koo, I.: Performance analysis of ILEACH and LEACH protocols for wireless sensor networks. J. lnf. Commun. Converg. Eng. 10(4), 384–389 (2012) 15. Kumar, G.H., Ramesh, G.P., Avadi, C.: Novel gateway free device to device communication technique for IoT to enable direct communication between homogeneous devices. Int. J. Pure Appl. Math. 118(16), 565–578 (2018) 16. Ramesh, G.P., Aravind, C.V., Rajparthiban, R., Soysa, N.: Body area network through wireless technology. Int. J. Comput. Sci. Eng. Commun. 2(1), 129–134 (2014)

Chapter 2

Intelligent Wearable Sensor Band for Underground Working People S. Karthikeyan, G. Sethuram Rao, M. S. Kowshik, P. Mohan Raj, G. Vishal, R. Juliet, P. Swetha, and T. Veronica

Abstract This paper presents an Intelligent wearable sensor band for underground working people. The security and soundness of laborers are significant for underground individuals. The proposed framework consolidates wearable sensors to quantify physiological and natural parameters. A passage is acquainted with giving information preparing, a neighborhood web server, and a cloud association. A wearable sensor on a laborer and natural sensor on a wanderer that can transmit the information to the client by means of a door for example server, gives offer notice and cautioning component for the clients. Live health examination taken for laborers who work in an underground like Tunnels, Shafts, etc., it has an Individual database of laborers and contrasts it and current essential tangible qualities separate to workplace information. Live update, will screen from the control room and it can direct the specialist if any medical problem occurs and furthermore can maintain a strategic distance from the undesirable passing. Keywords Sensor · Cloud · Natural parameters · Tunnels · Security

2.1 Introduction The Wearable Sensor Network (WSN) is being prospected in numerous applications like home security, savvy spaces, ecological checking, combat zone reconnaissance, target following and also consists of various little, low-fueled, vitality gained sensor hubs with detecting, preparing the information, and remote correspondence [1]. The Performance of the remote sensor arranges (WSN) in ecological observing and human services applications has been improved as of late. Therefore, the client can comprehend the continuous physiological and ecological information from neighborhood internet browser or portable applications anyplace and S. Karthikeyan (B) · G. S. Rao · M. S. Kowshik · P. M. Raj · G. Vishal · R. Juliet · P. Swetha · T. Veronica Department of Electronics and Communication Engineering, Velammal Institute of Technology, Panchetti, Chennai, India e-mail: [email protected] © Springer Nature Switzerland AG 2021 V. E. Balas et al. (eds.), Further Advances in Internet of Things in Biomedical and Cyber Physical Systems, Intelligent Systems Reference Library 193, https://doi.org/10.1007/978-3-030-57835-0_2

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whenever [2–4]. Wearable body territory arrange (WBAN) is one of the WSN that is commonly utilized in social insurance situations to see the physiological signs which may build the way of life, and as needs be wellbeing, for instance, a wearable band, which computes the heartbeat rate and hemoglobin level present inside the blood by utilizing a human services observing framework [5]. Aside from medicinal services applications, WBANs have likewise been familiar with screen situations. For instance, the work screens inside the meanderer can ascertain the temperature, gas (like dangerous gases and non-poisonous inside the environment) and weight esteems for security applications [6–9]. Indoor ecological monitoring is controlled by the can configuration of a wearable sensor. A sufficient opportunity for both ecological and physiological surveillance would not be provided there [10]. For eg, the research unceasingly screens the world and the subject’s intensity for persistent respiratory disease. Security is amazingly significant for the business work environment, particularly for laborers continually exchanging working situations among indoor and outside. In outside conditions, pressure and other dangerous substance are destructive to human wellbeing [11]. To hinder laborers from being presented to any unsafe and risky circumstances, some physiological parameters of laborers ought to try and be checked; Temperature and pulse rate are the key parameters considered in current measurement works dependent on WBAN [12]. The primary usually observed parameters are among different wearable ecological testing applications, temperature and weight. During this article, we propose a wearable sensor coordinating system that is appropriate for the financial work setting with related protection and health applications [13]. The framework design is appeared in Fig. 2.1. The wearable sensor arrange comprises of various wearable sensors that are equipped for speaking with the cloud server. The Environmental sensors observing including surrounding temperature, weight, elevation and gas content; the Wearable sensor is for physiological signs checking including oxygen level and heartbeat rate. Two portable technologies are used in our work, including Wi-Fi for short-term transmission of information and Routers for long-run transmission of information [14]. It empowers the short-go information to be transmitted at longer separations and associated with the on the web. Wearable sensors are designed to converse with each other on various subjects for effective functionality, if appropriate. In addition, an input is executed to store, save and transfer data to the cloud system. Checked information are frequently shown from a web server situated inside the passage and a web webpage inside the cloud server. If a crisis state is unlikely to be observed, the system will give customers alerts to their Smart Phones, such as Smartphone or Desktop. The remainder of this paper is written as follows: in Sects. 2.2 and 2.3, the usage of hardware and programming is provided, Sect. 2.4 provides some test results; in Sect. 2.5, the end and potential works are finally condensed.

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Fig. 2.1 The architecture of the proposed WBAN

2.2 Hardware Components 2.2.1 Rover Implementation The Rover description shows up in Fig. 2.2a. It fuses a force supply, one Camera, RaspberryPi3+ Module and 4 ecological sensors. A Rover is appended with MCP3008 IC, L293D Motor Driver and 2 DC motors operated by battery charging and voltage regulation regulate the voltage of the battery at steady voltage (6 V). The Raspberry Pi3+ Model with meanderer, which is utilized for the long-run correspondence at 300 Mbps and it has most extreme band of 5 GHz for WLAN. The long-range information must be conveyed from the Planet to the remote entrance. The computer obtains the Worker’s data attached to the wrist. Temperature (LM35), pressure (BMP180), and gas (such as MQ2 and CO2 ) are four ecological sensors selected. Such sensors are chosen because of their exceptional, good precision and low power consumption. Figure 2.2 Gas sensor—MQ2 and CO2 .

2.2.2 Specialist Implementation Figure 2.2b presents the graph of the Worker. It includes a force supply, and hub Microcontroller (MCU), and two physiological sensors. A low-power supply to control the battery voltage at 12 V for the circuit. Internal heat level sensors (LM35)

14 Fig. 2.2 a. Gas sensor b. Raspberry Pi 3B+ in Rover setup c. over all integration part with Raspberry pi 3B+

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Fig. 2.3 Both physiological and environmental

and rate sensors are associated with the MCU by adaptable wires. Both the wellness parameters are transmitted to the server through remote transmission (WBAN).

2.2.3 Edge Gateway Passage computer architecture is referred to as Fig. 2.3. It consists of 1 Raspberry Pi Model 3, remote modules, Fig. 2.2b MCU node with SPO2 sensor, and supply device for power. The Raspberry Pi operates on the Raspbian platform and is a functional platform for open-source Linux. This embraces a few distinct dialects that include Java, Node.js, Python, C, and C++. The low power consumption, which needs just 2 A and 5.5 V power source, That a conveyable force bank can legally regulate. A Raspberry Pi connects with the Pi to get the wanderer’s remote knowledge.

2.3 Programming Components 2.3.1 Wearable Communication Wearable connectivity helps to include important health warning notices to some or more of the on-site staff. Therefore, they can respond to emergency situations as

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early as possible without getting gateway alerts. Through rover will relay two forms of RF packets: packets class 1 and packets class 2. Class 1 packet is general details regarding the world that are transmitted when no dangerous substance is detected. This message’s targeting destination is also the portal. Class 2 packet is distributed after discovery of dangerous conditions. This packet is additionally distributed because of the connection to certain connected apps. The sensor node continues tracking environmental data in continuous monitoring mode, without reaching low-power mode. After initialization a rover can reach idle mode and listen to the RF channel for incoming messages. If data is provided a data separation feature can verify if the data may be a alert message from other Safe Nodes with unsafe environmental data. If it is dangerous data, the consumer will be alerted through the application of a Smart Device. The program algorithm will then miss the notification and switch to idle mode. In case no information is collected, the meanderer must within a period gage and record inline sensor details. On the off probability that any unsafe situations may be found, a Classification 2 packet will be configured and the RF package will be distributed to some or all others including the entrance. The Class 1 packet is formed and sent to the portal on the off probability that a hurtful situation is found.

2.3.2 Programming Implementation The design incorporates five parts: (1) WSN Director of Management (WDM), (2) Data Process Provider (DPM), (3) Database Manager (DBM), (4) Regional Web Server, and (5) Internet. For eg, Raspberry pi 3+ and Node MCU, a software written in Python is used to communicate with the numerous remote modules. Right now, program peruses information from the wanderer and laborer which pass the information to server. The information put away in the neighborhood database can be recovered later on for additional examination. A community server operating platform that depends on Node.js, HTML, CSS, and JavaScript is built to slowly display sensor details. Door details can be transferred to the cloud through Wi-Fi, Ethernet, and organizing cells.

2.3.3 Cloud Implementation The cloud service is supported in the US technology expert company Global Ocean. The server operates on Ubuntu 16.04.5 with 2 GB of RAM and 25 GB of plate capacity. In terms of Node.js and Node-RED, a cloud-put together platform is built, like the portal web execution. In turn a Mosquito expert is added and configured on the cloud platform. As an enhancement which can communicate within the platform and cloud with the MQTT professional. In addition, MySQL database is implemented on the server for storing of knowledge. To ensure the best possible protection of

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information, accreditations are needed if any client wants to access the server data. The server is configured to attempt once regularly to do a scheduled validation of the Ubuntu picture to insure the data is not lost.

2.4 Results and Discussion 2.4.1 Sensors’ Presentation Some continuous evaluation from various sensors worn by one subject is provided in the Fig. 2.4. The qualities demonstrate that the subject is outside and the wearable gadget in wrist. Clearly, when the subject is outdoors, it can be found that the ambient temperature, beat rate and weight are greater than inside passages. Since hotter temperature can assimilate more dampness, the weight is lower when the subject is inside passages. For gas fixation, it is higher in inside passages when contrasted with outside. Internal heat level and pulse information is additionally introduced in the figure. For the exception of when the subject is within passages from just below 35 °C to around 38 °C, the internal heat volume begins to increase. The subject’s pulse perusing is changed at around 100 beats for every moment. The Oxygen level in the blood consistently somewhere in the range of 95 and 99% as would be expected, on the off chance that it goes irregular it will diminish underneath 90%. Fig. 2.4 Software for video streaming 1

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2.5 Conclusion Right now, present a keen wearable sensor band for underground working individuals for wellbeing and security estimations. The framework is set up to live both physiological and ecological information shaping a system from wearable sensors joined to laborers and give the data to the control space for checking. Highlights like sensor hub equipment and programming structure, portal and cloud usage are examined. During this venture, we will stay away from the undesirable demise and furthermore keep away from the basic issues confronting the laborers.

References 1. Selkar, R.G., Thakare, M.: Brain tumor detection and segmentation by using thresholding and watershed algorithm. Int. J. Adv. Inf. Commun. Technol. 1, 321–324 (2014) 2. Alam, M.S., Rahman, M.M., Hossai, M.A.: Automatic human brain tumor detection in MRI image using template-based K means and improved fuzzy C means clustering algorithm. Big Data Cogn. Comput. 3(27) (2019) 3. Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8, 275–283 (2004) 4. Devkotaa, B., Alsadoona, A., Prasada, P.W.C., Singhb, A.K., Elchouemic, A.: Image segmentation for early stage brain tumor detection using mathematical morphological reconstruction. Procedia Comp. Sci. 125, 115–123 (2018) 5. Mathur, N., Meena, Y.K., Mathur, S., Mathur, D.: Detection of Brain Tumor in MRI Image Through Fuzzy-Based Approach in High-Resolution Neuroimaging-Basic Physical Principles and Clinical Applications. Rijeka, Croatia, InTech (2018) 6. Arzoo, M., Prof, A., Rathod, K.: K-means algorithm with different distance metrics in spatial data mining with uses of NetBeans IDE 8.2. Int. Res. J. Eng. Technol. 4, 2363–2368 (2017) 7. Karthik, R., Menaka, R.: A multi-scale approach for detection of ischemic stroke from brain MR images using discrete curvelet transformation. Measurement 100, 223–232 (2017) 8. Wu, Y., Zhu, M., Li, D., Zhang, Y., Wang, Y.: Brain stroke localization by using microwavebased signal classification. In: IEEE International Conference on Electromagnetics in Advanced Applications, pp. 828–831 (2016) 9. Ay, H., Furie, K.L., Singhal, A., Smith, W.S., Sorensen, A.G., Koroshetz, W.J.: An evidencebased causative classification system for acute ischemic stroke. Ann. Neurol. 58(5), 688–697 (2005) 10. Database, Brainweb: http://www.bic.mni.mcgill.ca/brainweb/. Accessed 10 Sept 2018 11. Ramesh, G.P., Aravind, C.V., Rajparthiban, R., Soysa, N.: Body area network through wireless technology. Int. J. Comput. Sci. Eng. Commun. 2(1), 129–134 (2014) 12. Nithya, V., Ramesh, G.P.: Wireless EAR EEG signal analysis with stationary wavelet transform for co channel interference in schizophrenia diagnosis. In: Balas, V., Kumar, R., Srivastava, R. (eds.) Recent Trends and Advances in Artificial Intelligence and Internet of Things. Intelligent Systems Reference Library, vol 172. Springer (2020) 13. Fiebach, J.B., Schellinger, P.D., Geletneky, K., Wilde, P., Meyer, M., Hacke, W., Sartor, K.: MRI in acute subarachnoid haemorrhage; findings with a standardised stroke protocol. Neuroradiology 46(1), 44–48 (2004) 14. Fiebach, J.B., Schellinger, P.D., Jansen, O., Meyer, M., Wilde, P., Bender, J., Hähnel, S.: CT and diffusion-weighted MR imaging in randomized order. Stroke 33(9), 2206–2210 (2002)

Chapter 3

Hybrid Cloud Computing Model for Big Data Analytics in Organization R. Sheela Daniel, S. Raja, P. Ebby Darney, and Y. Harold Robinson

Abstract Hybrid cloud computing model is constructed to produce the infrastructure for the organization. The service oriented architecture is used to support the big data for the business intelligent solutions. The architect decision has provided the Oracle based big data application with Hadoop technique. The cloud computing has the implementation of traditional IT for high implementation of traditional IT for high implementation time. The security is the main problem of the big data analytics for implementing Governance and risk management. The factors for affecting the cloud computing has identified for the hybrid cloud computing. Keywords Private cloud · Public cloud · Hybrid cloud · Security · Organization · Risk management · Oracle · Big data

R. S. Daniel Department of Civil Engineering, SCAD College of Engineering and Technology, Cheranmahadevi, India e-mail: [email protected] S. Raja Department of Mathematics, SCAD College of Engineering and Technology, Cheranmahadevi, India e-mail: [email protected] P. E. Darney Department of Electrical and Electronics Engineering, SCAD College of Engineering and Technology, Cheranmahadevi, India e-mail: [email protected] Y. H. Robinson (B) School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India e-mail: [email protected] © Springer Nature Switzerland AG 2021 V. E. Balas et al. (eds.), Further Advances in Internet of Things in Biomedical and Cyber Physical Systems, Intelligent Systems Reference Library 193, https://doi.org/10.1007/978-3-030-57835-0_3

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3.1 Introduction The cloud computing trend and how cloud computing will become the essential part of the organization’s infrastructure implementation in the future [1]. Hybrid cloud computing vital for organizations has the Hybrid cloud will become essentials for the organizations in future because it will offer a single cloud solution with the mixture of public and private cloud computing services [2]. Organizations will be concentrating more on data and application integration, relating exterior and interior applications with hybrid computing implementations [3]. Cloud computing service provider assistance on cloud consumption has the Organizations will need the help of cloud computing service to liaise with IT department to discuss the purchasing and cloud solution options [4]. Figure 3.1 demonstrates the cloud computing models that the cloud computing has separated into 3 models of public cloud, private cloud and hybrid cloud. It demonstrates the control for three types of services, abstraction and the purpose for producing the efficient modelling. Big data getting developed and established that EMC implements that many companies are utilizing different sources to accomplish superior consideration of their, customers, associates, employees and processes [5]. Figure 3.2 demonstrates the produced oracle solution for the big data related issues that the HDFS have the

Fig. 3.1 Cloud computing models

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Fig. 3.2 Oracle solution for Big data

oracle NoSQL database and the enterprise application for acquire section, Hadoop for organize section that has the oracle big data connectors, The data warehouse and In database analytics has been demonstrated for the analyze and analytic applications for decide implementations [6]. Companies are exploring and processing more than organized and transaction based data which contains videos, social networking, RFID logs, sensor networks, search indexes, environmental conditions and medical scans [7]. Universal methodology has discussed to develop strategies for big data to capture the right metadata which will be analysed, maintained and store quickly [8]. It will provide the organizations to understand the large data to design business intelligence solutions which can later link to build the strong Service Oriented Architecture (SOA) [9]. The variety of digital sources which big data can support and organization can fully utilized different pieces of the detailed information from different sources [10]. Figure 3.3

Fig. 3.3 Architect decision

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Fig. 3.4 Traditional IT with cloud computing

illustrates the Architect decision for providing oracle and big data appliance with Hadoop and it is connected with the oracle and big data to produce the external table for oracle SQL developer with SQL tools. Big data supports different type of sources like media and entertainment, healthcare, life sciences, video surveillance, transportation and logistics [11]. It will help organization’s those trading globally which will provide the organization’s to make their decisions, efficiently and well-timed based on their transactions [12]. There are several differences for the cloud computing and traditional IT Eco system that the service providers have the connection with the data centres and networks for providing the support for hardware and software specifications with high amount of implementation period and customization about the expensive for mobility and adaptability [13]. The cloud eco system has the multi user based environment with the service providers. The on demand and virtualization are the other parameters of cloud based systems and it is demonstrated in Fig. 3.4.

3.2 Methods and Materials The challenges involved in the implementation of big data as organization go further along with it. One of the main challenges involved the lack of staff with big data and analytical skills and second one it’s not limited to local contexts, it’s more global [14]. The big data contains several prospects but dealing with fast progresses enhances challenges, budget and complications [15]. The organization’s thinking about big data as problem or an opportunity, only 30% reflect that big data as problem because only large volume of data is not only the solution for some organizations. On the other hand, 70% of the organization sees it as a prospect to thorough studies of their data which contains new evidences about their consumers, associates, costs, processes and procedures which can be used for business proficiencies. Figure 3.5

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Fig. 3.5 Oracle integrated information architecture

demonstrated the integrated format for information with Oracle for big data cluster with DBMS, NoSQL and HDFS. The security and governance for the management with the organize for Hadoop based MapReduce network. Infrastructure as a Service (IaaS) is the service provide infrastructure as a service, in which company outsource the hardware, networking gears, servers and storage. Companies like Microsoft, Amazon and Telstra and other sel.\lice providers delivers its personal equipment and responsible for administration, monitoring, marinating and running it. Application platform as a Service (ApaaS) is the service provides the whole application as a service; in which company lease the hardware, networking gears, servers, internet and storage. Companies like Google App Engine, Force.com and application space to charge virtualised severs and related facilities and services for developing and running applications. Software as a Service (SaaS) is the service provides the software application as a service, in which company uses the application over the internet. Companies like Google, salesforce.com and other application service providers deliver OneSource and licensed software and applications. Service Oriented Architecture (SOA) is the service which allows companies to maintain and support communication between two different computing services or entities. Communication between two commuting entities and services are independent of each other, like Amazon Flexible Payment and PayPal. Delivery models of cloud computing is about the different delivery models of cloud computing which includes Public, Private and Hybrid Cloud. Figure 3.6 demonstrates the Physical architect for combined analytics that has the oracle big data appliance, oracle big data connectors, Oracle Exadata and Oracle Exalytics.

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Fig. 3.6 Physical architect for combined analytics

3.3 Results and Discussion Advanced analytics is the combination of different procedures and tools categories which includes prognostic analytics, data mining, statistical analysis, complex SQL, data visualization, artificial intelligence, natural language processing and database approaches which maintain analytics. Visualisations are the concept that the big data provides Advance Data Visualisations (ADV) which characterizes millions of data points. ADV supports different sets of data varieties and data structure which is not adjustable into computer screen. Real time Organization can observer and measure different business processes more regularly because of the bid data tools and analysis. Structure and unstructured data: Organizations are handling unstructured and structured data more efficiently than ever before. This provides organizations improved data mining and fraud finding techniques to process under layers of organizational data. Hadoop Distributed File System (HDFS) demonstrates the big data is varied in terms of data types, HDFS will help to convert these different data types into data structure so it can fit into the traditional data base management system. MapReduce will provide distributed parallel processing for different types of data collections which will make analysis easy on big data. Private cloud deployed on company’s own site or data centre belongs to the service provider. The private cloud makes logic to be deployed and contain within the zones of company infrastructure which will provide more security and control over the company’s infrastructure. It will provide access control to companies which will assure certified access to infrastructure and its applications, hardware utilisation and assets management. Vendor hosted or partner allowed the companies to move their applications to the service provider secure data centres. Companies will be saving cost rather than investing and buying the new technology not only equipment but also release pressure on in house resources and it is demonstrates in Fig. 3.7. Public cloud allows organization to utilized infrastructure and applications through the internet. Organization’s end users will be accessing applications, servers,

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Fig. 3.7 Private Cloud

networks and services on demand through web browser on their laptops or workstations. Companies no longer required buying the expensive equipment, when applications and services are accessible through internet on little cost. The main problem moving with public cloud is security and regulatory issues. and it is demonstrated in Fig. 3.8. Hybrid will provide the combination of both private and public cloud to the organizations. It will provide the organizations the choice to switch between private and public on peak processing times, when resources required over the network. The companies will deployed the application which is more important and essential on the private cloud behind the firewall, like less security services and applications can place on public clouds. Hybrid model will allow the companies to maintain and control peak time periods, save costs and security on precarious applications and this model is demonstrated in Fig. 3.9. Cloud-centric deployment has the Organizations will be focusing and exploring different options and prospects to transfer current enterprise workload on application infrastructure or cloud system. To make most out of the cloud model organizations need to plan applications deployment where most of the workload is vastly adjustable and scalable. Upcoming operational models and data centre based on cloud model that in coming years most of the organization will be designing and developing its own cloud computing mode to support its operational model and data centres. Future developments, issue identification and analysis in company A is the enormous big mining giant which has several mines located all over the world. Each site has its

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Fig. 3.8 Public cloud

Fig. 3.9 Hybrid cloud

own business rules and doing things according to their requirements which is not ideal if company want to implement a global and standardise solution. Company A is using Oracle stacks which are using Oracle Warehouse Builder (OWB), Hyperion Performance Suite, Business Intelligence Tool (OBIEE) and Ellipse as ERP system (Also based on Oracle) and oracle databases. The company is expanding quickly in each area which involves different subject areas like assets management, mobile

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assets, procurement, sales, commercial, planning, finance, HR, budgeting and forecasting. The result of that data sets are getting bigger and larger, transactions and operations are expanding enormously every day which need proper analysis, data quality and to make business operations uniform and standardise each site and location. A Company’s corporate affairs and legal business functions and department are getting busier than ever before which needs to look for every media to get the right and proper information at right time. To address the future requirements, Company A needs to expand its data warehouse and business intelligence operations to make most out the business value which comes from data. Company A has different sites in Canada, Chile, Peru, Argentina, South Africa and Australia, each site has its own central offices where they perform all the business operations according to their needs which at some stage need to standardise and make it global to produce a single solution. To address large data sets, RFID Assets, social media for legal and corporate laws, so company will analyse them properly need to address some of the changes in the future. The process is explaining and hovering curtains from big data analytics which can provides benefits to Company A explain in the table in many ways. The unstructured and structure data, social media, news and blogs from anywhere from the internet are demonstrated in the detail. It is also explained about the integration of the data to address these requirements before entering into the data warehouse environment. Company A needs an integrated architecture which will support both traditional data warehouse and big data implementation. For the best result big data needs to be added into the existing data warehouse platform, so analysis can be used for the business intelligence system for better visualisation. Company A is using heavily Oracle products, even the hardware The Solaris boxes which will make implementation and integration of big data into existing data warehouse system easier if Company A will keep the same vendor. The different data sources are captured and stored which later processed into traditional DBMS (OLTP), files, and distributed clustered systems such as NoSQL and Hadoop Distributed File System (HDFS). Some of the architecture from big data appliances to the reporting level that the Company A is already using Oracle stream for warehousing and reporting purposes, the only piece is missing to bring Big data analysis and tool in place for detailed level of analysis. The different data sources are captured and stored on big data appliances, this unstructured information will process into the format by using Hadoop, MapReduce and NoSQL which can make sense for RDBMS system to integrate Data Warehouse system in place. This information later processed for reporting and visualisation purposes. Sandboxes are the copies or segment of huge datasets. For the experimentation purposes Company A needs sandboxes to store segment of massive Company A datasets from different data sources. It will provide the data analyst to play with the data and do the analysis with any tool they want to use to make sense of the data to the business. The data stored on the sandboxes store for certain period of time then discard the data after the analysis. Company A corporate private cloud implements the Private cloud deployed on Company A’s own site or data centre belongs to the service provider. It will give company the option where small number of business users are

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Fig. 3.10 Factors affected for cloud computing

located provide application and data access through clouds rather than spending and investing a small data centre for 50–100 users. Figure 3.10 demonstrates the Factors affected for cloud computing that the neutral has the highest amount of affected factors other than Laws. Moving big data into clouds utilizes the Private cloud implementation will also give Company A the benefit to move Big data scenarios into the cloud in future if it has too. If information is not located inside the company and it is coming from outside the firewall then it can be stored on the cloud for analysis. This information is relatedto tweets, feeds, internet, news, web data and events. Expensive maintaining cost when small number of business users on different site Some of the sites in Peru and Chile have only 30 users which will not going to increase in next 20 years, Company A has got data centres and application centres, which is costing maintaining and licensing cost. Private Cloud needs to be implemented for small number of business users, which will provide low maintenance cost and no licensing cost In future big data analysis from different sites can place on the clouds and used in EDW system for analysis. Resultant ICT impact and business or regulatory requirements has been described that customer data is the precious assets for the organizations which required vigilant governance and protection. Managing big data is not easy and it will require lot of precaution and architectural framework to make sure the quality and security of the underlying data. The data governance issues related to more towards security. Big data provide the detailed level of analysis and sensitive information to trade secret, financial archives and knowledgeable belongings. Make information available centrally will provide informal and treasured target for attackers or hackers which can harm the status of the organizations. Figure 3.11 demonstrates the Internet based cloud computing factors that have the email in spite of internet related cloud computing model.

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Fig. 3.11 Internet based cloud computing factors

The application policy and governance program offer organization best practices when providing business approaches and application schemes. The cloud computing providers have extensive regularity ambiguities and absence of standardisation which needs to address that sort of issues as well. Some of the companies will provide corporate companies to do penetration testing which will highlight where the loop poles are in the implementation. Companies also implement their own security controls deployed on its own sites for cloud computing to look after the security and privacy issues. In relation to private cloud implementation for Company A requirement are given below: • • • • • • • • •

Cloud service provider Mutual agreement on contract Security and privacy controls form Telstra Security and privacy controls within company A for the security Penetration testing required in certain time frames which provides by different companies to find loop holes Applications needs to decide by the infrastructure team to place on private cloud Data ownership Make sure the regulations from the country regularity authority body if exists Redesign and revisited the privacy and security policy framework.

3.4 Conclusion With all these benefits there are some challenges when it comes to big data and cloud computing. Big data contains data ownership, data quality, data governance and lack of staff type of challenges which require proper consideration, investigation and

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deployment as part of big data implementation. On the other hand, in cloud computing also contains security, privacy and data ownership types of obstacles which also need proper analysis and deployment for cloud implementation. One of the main obstacle facing in the implementation of big data and clouds are governance and privacy issues which involves re-designing organization’s policies, impact analysis and mitigation strategies for the effective execution. Massive research, developments and investments are still improving the big data technology by different IT vendors which can predict the long lasting future of big data and cloud technologies in IT and business world.

References 1. Hussain Iqbal, M., Rahim Soomro, T.: Big data analysis: apache storm perspective. Int. J. Comput. Trends Technol. 19(1), 9–14 (2015) 2. Krishnan, R.S., Julie, E.G., Robinson, Y.H., Kumar, R., Son, L.H., Tuan, T.A., Long, H.V.: Modified zone based intrusion detection system for security enhancement in mobile ad-hoc networks. Wirel. Netw. 1–15 (2019) 3. Akuma, S., Iqbal, R., Jayne, C., Doctor, F.: Comparative analysis of relevance feedback methods based on two user studies. Comput. Hum. Behav. 60, 138–146 (2016) 4. Lopez, J., Rios, R., Bao, F., Wang, G.: Evolving privacy: from sensors to the Internet of Things. Future Generat. Comput. Syst. 75, 4657 (2017) 5. Balaji, S., Julie, E.G., Robinson, Y.H., Kumar, R., Thong, P.H., Son, L.H.: Design of a securityaware routing scheme in mobile ad-hoc network using repeated game model. Comput. Stand. Interfaces 66 (2019) 6. Gong, B., Veeravalli, B., Feng, D., Zeng, L., Wei, Q.: CDRM: a cost-effective dynamic replication management scheme for cloud storage cluster. In: 2010 IEEE International Conference on Cluster Computing, September 2010, pp. 188–196 (2010) 7. Li, M., Wang, X., Gao, K., Zhang, S.: A survey on information diffusion in online social networks: models and methods. Information 8, 118 (2017) 8. Mittal, H., Saraswat, M.: An optimum multi-level image thresholding segmenta-tion using non-local means 2d histogram and exponential kbest gravitational search algorithm. Eng. Appl. Artif. Intell. 71, 226–235 (2018) 9. Robinson, Y.H, Jacob, I.J., Julie, E.G., Darney, P.E.: Hadoop MapReduce and dynamic intelligent splitter for efficient and speed transmission of cloud-based video transforming. In: IEEE— 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 400–404. IEEE (2019) 10. Mika, V., Daniel Graziotin, M., Kuutila, M.: The evaluation of sentiment analysis—A review of research topic, venues, and top cited papers. Science Direct 27, 16–32 (2018) 11. Hogenboom, F., Frasincar, F., Kaymak, U., De Jong, F.: An overview of event extraction from text. In: Workshop on Detection, Representation, and Exploitation of Events in the Semantic Web (DeRiVE 2011) at tenth international semantic web conference (ISWC 2011), vol. 779, pp. 48–57 (2011) 12. Thomaz, G.M., Biz, A.A., Bettoni, E.M., Mendes-Filho, L., Buhalis, D.: Content mining framework in social media: A FIFA world cup 2014 case analysis. Inf. Manage. 54(6), 786–801 (2017) 13. Krishnan, R.S., Julie, E.G., Robinson, Y.H., Raja, S., Kumar, R., Thong, P.H., Son, L.H.: Fuzzy logic based smart irrigation system using Internet of Things. J. Clean. Prod. 252(10) 119902 (2020)

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14. Reddick, C.G., Chatfield, A.T., Ojo, A.: A social media text analytics framework for doubleloop learning for citizen-centric public services: a case study of a local government facebook use. Gov. Inf. Q. 34(1), 110–125 (2017) 15. Lakshminarayanan, K., Krishnan, R.S., Julie, E.G., Robinson, H.Y., Kumar, R., Son, L.H., Hung, T.X., Samui, P., Ngo, P.T.T., Bui, D.T.: A new integrated approach based on the iterative super-resolution algorithm and expectation maximization for face hallucination. Appl. Sci. 10, 718 (2020). https://doi.org/10.3390/app10020718

Chapter 4

Bio-Inspired Search Optimization for Intrusion Detection System in Cognitive Wireless Sensor Networks M. S. Vinmathi, M. S. Josephine, and V. Jeyabalaraja

Abstract In modern times, security concerns have been susceptible. Kaspersky intelligence believes which denial of service attacks has a vital effect on the network relative to many other remote security threats. The current work focus on DoS attacks in cognitive radio networks (CRNs). The presence of malicious users is threat for enhancing the effective spectrum utilization and this threat may be an active or passive. In an active attack malicious user will deliberately upset the primary user framework. A passive attack relates to the circumstance in which a malicious attack endeavors to translate source data without infusing any data or attempting to alter the data i.e., it will tune in to the transmission without cooperating with other users. The network consists of two users such as primary users and secondary users where the main impact occurs on primary users. The network performance parameters such as packet delivery ratio, packet loss ratio, bandwidth usage and end to end delay are analyzed in CRN to detect DoS attacks. Keywords Bio-inspired algorithm · Search optimization · Cognitive WSN · Intrusion detection · Elephant search algorithm

M. S. Vinmathi (B) Department of Computer Science Engineering, Dr. M.G.R. Educational and Research Institute (Deemed to Be University), Maduravoyal, Chennai 600095, India e-mail: [email protected] M. S. Josephine Department of Computer Applications, Dr.M.G.R. Educational and Research Institute (Deemed to Be University), Maduravoyal, Chennai 600095, India e-mail: [email protected] V. Jeyabalaraja Department of Computer Science Engineering, Velammal Engineering College, Surapet, Chennai 600066, India e-mail: [email protected] © Springer Nature Switzerland AG 2021 V. E. Balas et al. (eds.), Further Advances in Internet of Things in Biomedical and Cyber Physical Systems, Intelligent Systems Reference Library 193, https://doi.org/10.1007/978-3-030-57835-0_4

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4.1 Introduction The Cognitive Radio (CR) is one of the emerging technologies in wireless communication focused at enhancing the methods by which the radio is utilized. The drastic increase in the number of users, data rates and wide range of applications in wireless communication systems by the users results in scarcity of the spectrum. The spectrum needs to be utilised effectively in order to make use of the available limited resources. The key interest on spectrum utilization is created in cognitive radio networks. The Primary Users (PUs) are the legitimate user frequency band and the Secondary Users (SUs) are the unlicensed users or opportunistic users awaiting to share the spectrum opportunistically when the primary user is not using it. This facilitates the spectrum assignment to the opportunistic users without resulting interference to the licensed users [1] intensity. Figure 4.1 depicts the general architecture of Intrusion Detection system in CRs. Input module receives some of the information like type of signals, TCP information, agreement parameters, number of packets sent, receive and drop, time delay or arrival etc. The monitoring module consists of previous historical data of input module and stores in the routing table to find which kind of attack occurs in the network [2]. The Detection module detects the attacks based on the monitoring information stored. Some of the attacks detected are PUE, jamming, selfish attacks and flooding attacks. Also, it focus on two operations namely finding the cooperative location and to make reliable system against attacks. The output module enumerates the attack alert, specify the attack type, the source of attack and finally the victim of attack [3].

4.2 Impact of Security Attacks on Cognitive Network Work In Fig. 4.2, the vulnerable effects of the security attacks are shown. A definitive objective of deploying CR systems is to address the spectrum under-utilization that is caused by the current settled spectrum usage strategy. By powerfully getting to the spectrum “openings”, the SUs can recover these generally squandered spectrum resources. However, PUE attackers may take the spectrum “openings” from the SUs, prompting consistent bandwidth wastage [4]. On a basic level, the secondary administrations in CR organizes naturally have no security that they will have stable radio resource due to the idea of dynamic spectrum. The presence of these attacks fundamentally expands the connection unreliability of CR systems [5]. In Denial of Service, consider PUE attacks with high assaulting recurrence; at that point the attackers may involve in occupying large number of the spectrum openings. The SUs will have inadequate Bandwidth for their transmissions, and thus, a portion of the SU administrations will be denied. In the worst scenario, the CR network may even discover no channels to set up a typical control channel for conveying the control messages [6].

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Fig. 4.1 General scenario of IDS in cognitive radio networks

Fig. 4.2 Vulnerable impacts of security attacks in CRN

Figure 4.3 depicts the types of intrusion detection with respect to CRNs. They are classified in to two types namely misuse detection and anomaly detection. Both the cases are vulnerable in nature. Many attacks Syn flooding attacks are some examples of misuse and anomaly detection [7, 8]. Fig. 4.3 IDS classification in CRN

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4.3 Algorithm for Search Optimization 4.3.1 Elephant Search Algorithm (ESA) The ESA has a place with the gathering of contemporary met heuristic inquiry enhancement calculations [9]. This calculation impersonates the conduct and qualities of an elephant, and its system depends on double hunt instrument, or the pursuit operators can be isolated into two gatherings [10]. Elephants live in gatherings which are bunch is separated interested in a few tribes below the initiative most seasoned primary gathering. The ESA copies the fundamental attributes and highlights the crowd source of elephants. Elephants are extraordinary, wherever the male elephants want to survive inside segregation and females likes gatherings; the improvement is measured and answerable for the objectives of the investigation. Apparently, the algorithm has three fundamental attributes as compelling inquiry improvement strategies; 1. The hunt procedure iteratively refines the answer for optimal ideal arrangement; 2. Head female elephants lead escalated nearby inquiries at places, wherever superior likelihood of result the most excellent arrangement was normal 3. The male elephant encompass obligations through investigations out of the neighbourhood. Elephants contain a few highlights in addition to attributes which create the motivation procedure through the natural conduct are significant [11, 12]. ESA is depicted live respectively below the authority of the most seasoned, assume that x elephant in tribe cl be able to be portrayed by accompanying arithmetical equations:   E L new,cl,x = E L cl,x + y · E L Best,cl − E L cl,x · z

(4.1)

where are the new and old phase for the elephant x in clan cl. respectively and y m[0, 1] as shown in Eq. 4.1. The Male elephants which are grown-up disregard their family and exist in the detached region. It circumstance be able to be reenacted through isolating administrator to take care of complex streamlining issues. To get better the pursuit capacity people with the most noticeably awful wellness case will actualize the isolating administrator as indicated by the accompanying Eq. 4.2: E L wor,cl = E L Min + (E L Max − E L Min + 1) · Rnd

(4.2)

where E L new,cl,x , E L cl,x are assigned to find the upper and lower bound position of each elephant, represent the worst elephant in clan cl, and Rnd m[0, 1] is assigned for stochastic distribution. At the last point, ESA is created with the portrayal of tribe refreshing and isolating administrator. The Eq. 4.2 is used to find the malicious

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Plain Text

DES Encryption

Key 1

DES Encryption

Key 2

DES Encryption

Key 3

Cipher Text

Fig. 4.4 Data encryption in symmetric level

Table 4.1 Algorithm for encryption Step 1: Ctext = EncryptK3(DecryptK2EncryptK1 (pltext))) DES encrypts with K1, DES decrypt with K2,then DES encrypt with K3 Decryption is the reverse: Step 2: Pltext = Decrypt K1 (EK2 (DecryptK3 (Ctext))) i.e., decrypt with K3, encrypt with K2,and then decrypt with K1

node by monitoring approach [7] which is in turn utilized for developing intrusion detection system. The symmetric data triple data encryption algorithm (Fig. 4.4) is used for authentication during a feature extraction process to transmit data in an encrypted manner. The algorithm consists of a 64-bit plain text key. The cryptographic text is obtained as well as the XOR is really the perfect block of plain text with triple DES operation. There are triple keying options used as per the requirement. Table 4.1 defines the encryption algorithm for security and protected data transmission [13, 14]. In any feature extraction method, this encryption algorithm is used to remove attacks that are considered to be intermediate in the network.

4.4 Implementation and Results A network scenario with an area of 1000 m * 1000 m, consists of maximum of 50 nodes (users) for simulation. Also, we considered 20 Primary Users, 20 Secondary Users and an attacker/intruder for analysing intrusion detection in CRNs. Figure 4.5 describes about the data transmission scenario form source to destination and Fig. 4.6 depicts the intrusion detection by ESA algorithm. Figure 4.7 shows the encrypted keys for each node. The encryption is performed to transmit the data to provide an additional security. The efficiency of IDS is calculated

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Fig. 4.5 Data transmission from source to destination through neighbouring nodes

Fig. 4.6 Intrusion detection scenario

form simulation results using network simulator. The detection rate is evaluated as shown in Eq. 4.3. Dr = Mean dr op /Tn

(4.3)

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Fig. 4.7 Generation of encrypted keys for data transmission

Packet delivery raƟo

where, Dr is the detection rate, Mean drop is the drop of packets in failed sessions and Tn is the sequence of transmission with n rounds. Figure 4.8 shows the packet delivery ratio for 50 nodes. The results shows that the packet loss is high as compared in normal scenario. Figure 4.9 shows the throughput for 50 nodes. The results depicts that there is drastic reduction in throughput during the intrusion detection. 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Number of nodes(50)

100 Sec 200 Sec

Normal

Fig. 4.8 Packet delivery ratio

Time/Sec

AŌer Intrusion

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throughput(Mbps)

100 80 Number of nodes (50)

60

100 Sec

40

200 Sec

20 0 Normal

AŌer Intrusion

Ɵme(sec) Fig. 4.9 Throughput comparison

4.5 Conclusions and Future Work In this paper, we have discussed and implemented bio-inspired search optimization algorithm called elephant search optimization algorithm for finding the intruders in cognitive wireless sensor networks. Also we have discussed on the characteristics of Primary users and secondary users of CRN. The results discussed here shows that the packet delivery ratio and overall throughput has been slashed to 50% in the network. Due to this adverse effect the primary users cannot receive the requested resources and in turn data loss occurs. In future, the simulation study can be increased up to 1000 nodes with live datasets.

References 1. Elangovan, K., Subashini, S.: A survey of security issues in cognitive radio network. ARPN J. Eng. Appl. Sci. 11(17), 10496–10500 (2016) 2. Wang, X., Ji, Y., Zhou, H., Li, J.: Auction based frameworks for secure communications in static and dynamic cognitive radio networks. IEEE Trans. Veh. Technol. 66(3), 2658–2673 (2017) 3. Hemanth Kumar, G., Ramesh, G.P.: Reducing power feasting and extend network life time of IoT devices through localization. Int. J. Adv. Sci. Technol. 28(12), 297–305 (2019) 4. Hemanth Kumar, G., Gireesh, N.: End-to-end communication between IoT devices to maximize energy efficiency through optimization and localization based on the bio inspired algorithms. Int. J. Adv. Sci. Technol. 28(16), 1444–1452 (2019) 5. Rajesh, D.: Improved distributed cooperative spectrum sensing (dcss) for cognitive radio adhoc network. Int. J. MC Square Sci. Res. 7(1), 170–182 (2015) 6. Li, J., Feng, Z., Feng, Z., Zhang, P.: A survey of security issues in cognitive radio networks. China Commun. 132–150 (2015) 7. Balaji, S., Sasilatha, T.: An efficient routing approach for detection of syn flooding attacks in wireless sensor networks. EAI Endorsed Trans. Energy Web Inf. Technol. 5(20), 1–6 (2018) 8. Balaji, S., Sasilatha, T.: Detection of denial of service attacks by domination graph application in wireless sensor networks. Clust. Comput. J. Netw. Soft. Tools Appl. 22(6), 15121–15126 (2019)

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9. Darwish, A.: Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications. Future Comput. Inf. J. 3, 231–246 (2018) 10. Deb, S., Fong, S., Tian, Z., Wong, R.K., Mohammed, S., Fiaidhi, J.: Finding approximate solutions to NPhard optimization and TSP problems using elephant search algorithm. J. Supercomput. (Springer, New York, 2016) 11. Meng, X., Liu, Y., Gao, X., Zhang, H.: A new bio-inspired algorithm: chicken swarm optimization. In: Advances in Swarm Intelligence, Springer, Berlin (pp. 86–94) (2014) 12. Wang, G.G., Deb, S., Coelho, L.D.S.: Elephant herding optimization. In: 3rd International Symposium on Computational and Business Intelligence, IEEE (2015) 13. Elangovan, K., Subashini,S.: Particle bee optimized convolution neural network for managing security using cross-layer design in cognitive radio network. J. Amb. Intel. Hum. Comput. (2018) 14. Elangovan, K., Subashini, S.: Cumulative cooperative spectrum sensing scheme to defend against selfish users. Indian J. Sci. Technol. 9(16), 1–4 (2016)

Chapter 5

Additive Manufacturing of Thin-Wall Steel Parts by Gas Metal Arc Welding Robot: The Surface Roughness, Microstructures and Mechanical Properties Van Thao Le, Dinh Si Mai, Van Chau Tran, and Tat Khoa Doan Abstract The Additive Manufacturing (AM), which employs the arc to fuse the metallic wire (WAAM) is attracted much attention for producing metallic parts. This technique shows a high rate of the material deposition and low production costs when compared to other metallic AM technologies. In the current work, an industrial welding robot has been employed for building ER70S6 steel walls according to the additive manufacturing method. First, two deposition strategies were considered to build the thin walls layer by layer—i.e., the same deposition direction and alternating deposition direction strategies. After that, the microstructural and mechanical characteristics of thin walls built with the most suitable strategy were investigated. The results indicate that the alternating deposition direction strategy allows achieving thin walls with more regular height. The roughness of the side surface of the thin walls is about 0.23 mm. The microstructure of ER70S6 thin walls changes from region by region: the upper region consists of lamellar structures; the middle region features granular structures; and the lower region shows mixed lamellar and equiaxed structures. The hardness also varies according to these three regions. The upper region shows the highest average value of hardness (~191 HV), followed by the lower region (~178 HV), and the middle region (~163 HV). Finally, the ER70S6 walls built by WAAM exhibit the anisotropy in terms of tensile strengths in the horizontal and vertical directions. Keywords Welding robot · Additive manufacturing · Low-carbon steel · Microstructures · Hardness · Tensile strengths

V. T. Le (B) · D. S. Mai · V. C. Tran · T. K. Doan Le Quy Don Technical University, Hanoi, Vietnam e-mail: [email protected] © Springer Nature Switzerland AG 2021 V. E. Balas et al. (eds.), Further Advances in Internet of Things in Biomedical and Cyber Physical Systems, Intelligent Systems Reference Library 193, https://doi.org/10.1007/978-3-030-57835-0_5

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5.1 Introduction Additive Manufacturing (AM) is considered as a new technique for manufacturing components with highly complex geometry without using supplementary resources, for example, cutting tools, fixture systems as in machining [1]. AM technology allows the manufacture of a physical part from its three-dimensional computer aided design (3D CAD) model layer by layer, which is opposite to subtractive manufacturing processes [2]. Thanks to the layer manufacturing principle, AM uses only an amount of materials required to build designed parts and support structures if necessary. Therefore, the waste of materials and environmental impacts could be reduced [3, 4]. Based on the energy source employed, metallic AM is categorized into three groups - i.e., laser-based AM (including powder bed and powder feed systems), arc-based AM (or WAAM), and electron beam-based AM [5]. Among metallic AM technologies, WAAM becomes a potential technology for the manufacture of large components. This technique features a high rate of the material deposition and low costs of investment [6]. WAAM uses an electrical arc made between the electrode and the workpiece to melt the metal wire and forms metal parts layer by layer. In the literature, considerable studies on the microstructures and mechanical characterization of WAAM components from titanium and nicked alloys, as well as aluminum alloys were carried out [7]. However, limited studies investigated on the microstructures, hardness and tensile strengths of steel walls produced by a welding robot according to the WAAM principle. Most of published studies used the stainless steel wire (e.g., 304 [8], 304L [9], 308L [10, 11], 316 and 316L [12, 13]) as the feedstock in WAAM. In the cases of low-carbon steels, in [14], the authors investigated the effect of processing conditions on the evolution of microstructures of mild steel walls produced by a WAAM process, in which a MIG welding torch was controlled by a 3-axis CNC machine. These authors stated that there was any significant difference in terms of microstructures between samples built with different processing parameters. However, in this study, a low level of welding current (50 A) was used to build the walls, and the voltage was varied in a narrow range from 11.7 V to 13.1 V. Such a change in terms of processing parameters might be not sufficient enough to cause a noticeable difference about the microstructure between the printed walls. Haden et al. [15] also used a WAAM printer constructed at Lehigh University to build thin walls from SS304 and ER70S mild steel. These authors focused on investigating the tensile strengths and the wearing property of thin wall materials. In terms of the microstructure and hardness, the authors only observed in the middle region of the walls. Nevertheless, the microstructure in other regions, i.e. the bottom and the upper regions of the wall was not reported. In terms of tensile strengths of mild steel walls, these authors observed that the tensile strengths (YS and UTS) in the horizontal direction were lower than that in the building direction. However, other authors [16] observed an opposite trend, namely the YS and UTS in the horizontal direction were significantly greater than those in the vertical direction.

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In our research, the manufacture of thin-wall mild-steel components by an industrial welding robot is addressed. The effect of two depositing strategies on the geometry of thin walls was first investigated to select the most suitable one. After that, the surface roughness, the microstructural and tensile strengths of the walls were investigated to confirm the compatibility of built components with real applications.

5.2 Materials and Experimental Methods In the experiment, the copper-coated welding wire made of mild steel (ER70S-6) was used as the feedstock in the welding process. The wire’s diameter is equal to 1.2 (mm). The chemical elements of the wire are 0.04% C, 0.92% Si, 0.45% Mn, 0.015% S, 0.011% P, 0.2% Cu, and Fe (balance). Several plates made of SS400 steel were also employed as the substrate. The length, width, and thickness of the plates are 250, 150, and 15 (mm), respectively. The robotic welding system (Panasonic TA-1400) was used to build the thin walls on the substrates according to the AM principle (Fig. 5.1). The welding torch movement is implemented by a 6-axis robot performed. The power source is YD350GR3 of Panasonic. The gas of 99.9% CO2 was used for the shielding during the welding process. The flow rate of the shielding gas was fixed at 16 L/min.

Fig. 5.1 The welding robot TA-1400 of Panasonic

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Fig. 5.2 Two deposition strategies considered for building thin-wall components

In the first stage, two strategies - i.e., the same deposition direction (Fig. 5.2a) and alternating deposition direction (Fig. 5.2b), were used for building thin-wall samples. In the first strategy, the welding deposits were performed in the same direction, whereas the second one deposits two adjacent layers in two opposite directions. In two strategies, after the deposit of a layer, the torch moves to the starting position of the next deposit. A holding time (t dw ) of sixty seconds was also applied between two adjacent layers to cool down the part and transfer the cumulated heat to the ambiance of the experimental room. The current, I = 90 A, the voltage, U = 18 V, and the welding speed, v = 300 mm/min, were used to build thin walls in both strategies. In the second stage of this research, a thin wall was built on a substrate by using the most suitable strategy chosen from the first stage to analyze the surface roughness, microstructures, hardness, and tensile properties. The aforementioned process parameters were used to build the wall. The height, length, and thickness of the wall are about 100, 200, and 4.5 (mm), respectively (Fig. 5.3). To analyze the microstructures of the wall, a specimen MS was cut from the wall in the middle region by using a wire cutting EDM machine (Fig. 5.3). The cross section of the wall is ground, polished, and chemically etched. Then, the microstructures were analyzed by using an optic microscope, AXIO A2M, in different regions of the cross section. A hardness-testing machine, Future-Tech Vicker FV-310, was used to measure the hardness. Three specimens in the vertical direction (vTS-1-2-3) and three specimens in the horizontal direction (hTS-1-2-3) were extracted from the thin wall (Fig. 5.3). All tensile strength tests were implemented on a tensile testing machine, INSTRON 3369, at room temperature.

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Fig. 5.3 Thin-wall part with the positions of cutting the specimen (MS) for observing microstructures, the tensile specimens in vertical (vTS-1-2-3) and horizontal (hTS-1-2-3) directions for observing tensile properties

5.3 Experimental Results and Discussion 5.3.1 Effects of Depositing Strategies on the Shape of Thin Walls As revealed in Fig. 5.2d, e, the alternating deposition direction allows achieving the thin wall with more regular height (Fig. 5.2e) than the first strategy (Fig. 5.2d). This can be explained based on the original shape of a single welding layer (Fig. 5.2c). Normally, when the parameters of the welding process were not adjusted, the height of single welding beads in the starting region is higher than that of the steady region, whereas the ending region is lower than the steady region [17, 18]. In the alternating deposition direction strategy, the arc in the current deposit/layer was struck in the ending region and it was turned off in the starting region of the previous deposit. Hence, the deviation of the height between the starting and the ending regions of the previous layer was effectively compensated in the current layer (Fig. 5.2e). On the other hand, in the same deposition direction strategy, the height of thin walls was significantly decreased from the starting region to the ending region (Fig. 5.2d). From this result, the alternating deposition direction strategy was considered as the most suitable one for building thin wall with more stable shape, and it was used to build the thin wall to investigate the surface roughness, microstructures, and mechanical properties of the wall.

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5.3.2 Surface Roughness Analysis The appearance of the side surfaces of the thin wall (Fig. 5.3) was acquired by a non-contact 3D digitizer (Konica Minolta Range 7). The digitized data (STL file, Fig. 5.4a) was then transformed into the point cloud {pi }, and a plane (P) that fits the point cloud {pi } of the side surface was estimated. The equation of the plane (P) is described by Eq. (5.1): (P) : α X + βY + γ Z + δ = 0

(5.1)

where α, β, γ , and δ are the coefficients of the plane and α 2 + β 2 + γ 2 = 0. The surface roughness (SR) of the side surfaces was calculated by Eq. (5.2) [19]: ⎛ ⎞ Np Np   |α X i + βYi + γ Z i + δ| 1 1 ⎝ ⎠  SR = Di = N p i=1 N p i=1 α2 + β 2 + γ 2

(5.2)

Fig. 5.4 a Appearance of the side surface of the wall (STL file) and b distribution of distance Di from the point pi to the fitted plane (P)

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where N p is the number of the points in the point cloud {pi } and Di is the distance from the point pi = (X i , Y i , Z i ) to the fitted plane (P). The distribution of the distances (Di ) in the computation area was shown in Fig. 5.4b. The surface roughness of the side surfaces of the walls was about 0.23 (mm), which is compared to those reported in the study of Xiong et al. [19].

5.3.3 Microstructure Analysis From the images of microstructures, it was firstly observed that the thin walls present various microstructure types that can be classified in three regions of the walls: the upper, the middle, and the lower regions. The upper region shows lamellar structures composed of ferrite with different types: Widmanstätten, allotriomorphic, and acicular morphologies (Fig. 5.5). In fact, the top layer was rapidly cooled by the air in the experimental room, and this layer was not influenced by the heat of other layers as in the other regions. In addition, the heat was conducted towards the bottom of the wall. Thereby, the lamellar structure was generated in this region. The middle/central region reveals granular structures of ferrite and little portion of pearlite in the boundaries of grains (Fig. 5.6). It was also found that the center of a layer, the zone ➀, features finer and denser grains in comparison to the overlapping zone ➁ (Fig. 5.6). This is because the heat of molten pool that formed the layer (N)

Fig. 5.5 Microstructures in the top region: lamellar structures of ferrite

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Fig. 5.6 Typical microstructures in the middle region composed of equiaxed grains of ferrite and perlite in the boundaries of grains

reheated and partially remelted the previous layer (N − 1), causing coarser grains in the overlapping zones. The lower region exhibits a mix of equiaxed and lamellar structures of ferrite (Fig. 5.7). The lamellar structures also distribute in the equiaxed grains of ferrite. The lower region contacts the large substrate, whereas the middle region contacts warmer layers. Thus, the lower region features a higher cooling rate when compared to the middle region, and the lamellar structures of ferrite were generated in the bottom region [8].

5.3.4 Mechanical Properties Microhardness. The hardness of the built thin-wall material was revealed in Fig. 5.8. The microhardness was measured at five positions in each region, which distribute on the centerline of the cross section of MS. It was found that the upper region of the wall is hardest, while the middle region shows the lowest hardness. The hardness of the upper region, the middle region, and the lower region is 191 ± 4 HV, 163 ± 4 HV, and 178 ± 6 HV, respectively. This observation is in line with the microstructure observation. The hardness of the upper region is greater than the middle and the bottom regions, because the upper region is dominantly composed of Widmanstätten structures (Fig. 5.5). Due to the presence of

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Fig. 5.7 Microstructures in the lower region: mixed lamellar and equiaxed structures

Fig. 5.8 Hardness variation in three regions of the wall

lamellae structures (Fig. 5.7), the lower region has the hardness value that is higher than that in the middle region characterized by granular structures (Fig. 5.6). Tensile strengths. The ultimate tensile strength (UTS) and yield strength (YS) of the wall were presented in Fig. 5.9. The YS and UTS in the vertical direction equal to 362 ± 8 MPa and 479 ± 7 MPa, respectively, are higher than those in the horizontal direction (YS = 320 ± 6 MPa and UTS = 429 ± 8 MPa). The anisotropy in terms of the microstructure might cause this difference of the tensile strengths between two directions.

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Fig. 5.9 Comparison of the tensile strengths in the horizontal direction (hTS) and the vertical (vTS) direction of the wall

5.4 Conclusions In this paper, an industrial welding robot was employed for producing thin wall components according to the AM method. The effect of the deposition strategy on the shape was firstly investigated. Thereafter, the most suitable deposition strategy was used to build a thin wall for investigating the surface roughness, the microstructures, and the mechanical characteristics of built materials. The main outcomes of this research can be summarized as follows: • The alternating deposition direction strategy allows achieving thin-wall components with more shape stability and regular height. • The surface roughness of the thin wall is about 0.23 mm. • The wall features the microstructure that varies from region by region. The upper region is characterized by lamellar structures. The middle region is composed of granular structures of ferrite. The pearlite appears in the boundaries of grains. The lower region reveals a mix of lamellar and equiaxed structures. • The variation of microstructures in different regions leads to the variation of hardness from 163 to 191 HV. • The mechanical properties of thin-wall low-carbon components built by the welding robot (YS: 320–362 MPa and UTS: 429–479 MPa) are also comparable to those of wrought A36 low-carbon steel, which has similar chemical compositions in compared to ER70S-6. Acknowledgements This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 107.99-2019.18.

5 Additive Manufacturing of Thin-Wall Steel Parts by Gas Metal …

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References 1. Chen, L., He, Y., Yang, Y., Niu, S., Ren, H.: The research status and development trend of additive manufacturing technology. Int. J. Adv. Manuf. Technol. 89, 3651–3660 (2017). https:// doi.org/10.1007/s00170-016-9335-4 2. ASTM: F2792 - 10e1 Standard Terminology for Additive Manufacturing Technologies (2010). https://doi.org/10.1520/F2792-10E01 3. Le, V.T., Paris, H., Mandil, G.: Environmental impact assessment of an innovative strategy based on an additive and subtractive manufacturing combination. J. Clean. Prod. 164, 508–523 (2017). https://doi.org/10.1016/j.jclepro.2017.06.204 4. Le, V.T., Paris, H.: A life cycle assessment-based approach for evaluating the influence of total build height and batch size on the environmental performance of electron beam melting. Int. J. Adv. Manuf. Technol. 98, 275–288 (2018). https://doi.org/10.1007/s00170-018-2264-7 5. Frazier, W.E.: Metal additive manufacturing: a review. J. Mater. Eng. Perform. 23, 1917–1928 (2014). https://doi.org/10.1007/s11665-014-0958-z 6. Williams, S.W., Martina, F., Addison, A.C., Ding, J., Pardal, G., Colegrove, P.: Wire + arc additive manufacturing. Mater. Sci. Technol. 32, 641–647 (2016). https://doi.org/10.1179/174 3284715Y.0000000073 7. Wu, B., Pan, Z., Ding, D., Cuiuri, D., Li, H., Xu, J., et al.: A review of the wire arc additive manufacturing of metals: properties, defects and quality improvement. J. Manuf. Process. 35, 127–139 (2018). https://doi.org/10.1016/j.jmapro.2018.08.001 8. Bai, Y., Gao, Q., Chen, X., Yin, H., Fang, L., Zhao, J.: Research on microstructure and properties of 304 stainless steel made by MIG filler additive manufacturing. IOP Conf. Ser. Earth Environ. Sci. 237 (2019). https://doi.org/10.1088/1755-1315/237/3/032096 9. Laghi, V., Palermo, M., Tonelli, L., Gasparini, G., Ceschini, L., Trombetti, T.: Tensile properties and microstructural features of 304L austenitic stainless steel produced by wire-and-arc additive manufacturing. Int. J. Adv. Manuf. Technol. 106, 3693–3705 (2020). https://doi.org/10.1007/ s00170-019-04868-8 10. Le, V.T., Mai, D.S.: Microstructural and mechanical characteristics of 308L stainless steel manufactured by gas metal arc welding-based additive manufacturing. Mater. Lett. 271, 127791 (2020). https://doi.org/10.1016/j.matlet.2020.127791 11. Foley, R.P.: Microstructural analysis of additively manufactured 308L stainless steel produced by plasma arc welding. Master thesis, Montana Tech (2019) 12. Chakkravarthy, V., Jerome, S.: Printability of multiwalled SS 316L by wire arc additive manufacturing route with tunable texture. Mater. Lett. 260, 126981 (2020). https://doi.org/10.1016/ j.matlet.2019.126981 13. Wang, L., Xue, J., Wang, Q.: Correlation between arc mode, microstructure, and mechanical properties during wire arc additive manufacturing of 316L stainless steel. Mater. Sci. Eng. A 751, 183–190 (2019). https://doi.org/10.1016/j.msea.2019.02.078 14. Liberini, M., Astarita, A., Campatelli, G., Scippa, A., Montevecchi, F., Venturini, G., et al.: Selection of optimal process parameters for wire arc additive manufacturing. Procedia CIRP 62, 470–474 (2017). https://doi.org/10.1016/j.procir.2016.06.124 15. Haden, C.V., Zeng, G., Carter, F.M., Ruhl, C., Krick, B.A., Harlow, D.G.: Wire and arc additive manufactured steel: tensile and wear properties. Addit. Manuf. 16, 115–123 (2017). https:// doi.org/10.1016/j.addma.2017.05.010 16. Lu, X., Zhou, Y.F., Xing, X.L., Shao, L.Y., Yang, Q.X., Gao, S.Y.: Open-source wire and arc additive manufacturing system: formability, microstructures, and mechanical properties. Int. J. Adv. Manuf. Technol. 93, 2145–2154 (2017). https://doi.org/10.1007/s00170-017-0636-z 17. Waqas, A., Qin, X., Xiong, J., Wang, H., Zheng, C.: Optimization of process parameters to improve the effective area of deposition in GMAW-based additive manufacturing and its mechanical and microstructural analysis. Metals (Basel) 9, 775 (2019). https://doi.org/10.3390/ met9070775

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18. Xiong, J., Yin, Z., Zhang, W.: Forming appearance control of arc striking and extinguishing area in multi-layer single-pass GMAW-based additive manufacturing. Int. J. Adv. Manuf. Technol. 87, 579–586 (2016). https://doi.org/10.1007/s00170-016-8543-2 19. Xiong, J., Li, Y., Li, R., Yin, Z.: Influences of process parameters on surface roughness of multi-layer single-pass thin-walled parts in GMAW-based additive manufacturing. J. Mater. Process. Technol. 252, 128–136 (2018). https://doi.org/10.1016/j.jmatprotec.2017.09.020

Chapter 6

An Application of Observer Reconstruction to Estimate Actuator Fault for DC Motor Nonlinear System Under Effects of the Temperature and Disturbance Tan Van Nguyen and Xuan Vinh Ha Abstract In this paper, we analysis the factors that impact the precisional control process of the DC motor such as disturbance, the temperature effect on coil resistance and the temperature effects on magnetic fluxes. From that, we suggest a method to estimate the actuator fault which will be performed in the future to apply for the process of eliminating fault. First, a nonlinear mathematical model of the DC motor under the action of temperature is constructed to control the system. Second, building the inequalities based on the reconstruction of unknown input observer (UIO) with considering disturbance is constructed to estimate the actuator faults using Lyapunov’s stability condition and a linear matrix inequality (LMI) optimization algorithm to obtain the control signal error asymptotically stable. Finally, the numerical simulation process is done to monitor the obtained result of the proposed method. Keywords DC motor nonlinear system · Unknown input observer · Actuator fault estimation

6.1 Introduction The speed simulation result of the DC motor nonlinear system is shown as Fig. 6.4. The result shows the response speed and its estimation signal approaching to the reference signal at the times without touch of actuator fault and opposite the response signal do not approach to the reference signal at the time where have actuator faults. We can see errors in the signal under the impact of the disturbance d. T. Van Nguyen · X. V. Ha (B) Engineering—Technique Faculty, Thu Dau Mot University, Binh Duong, Vietnam e-mail: [email protected] T. Van Nguyen e-mail: [email protected] © Springer Nature Switzerland AG 2021 V. E. Balas et al. (eds.), Further Advances in Internet of Things in Biomedical and Cyber Physical Systems, Intelligent Systems Reference Library 193, https://doi.org/10.1007/978-3-030-57835-0_6

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In the past decades, the types of DC motor have been applied in the role such as actuators because they have the potential choices for modern industries ranging from manipulators to accurate machine tools because of their vantages such as compact structure, controllability, high precision accuracy, reliability, low costs, etc. The temperature effect on coil resistance and the temperature effects on magnetic fluxes are shown in [1–7]. Here, [6, 7] showed the effect of the motor temperature to brushless direct current motor performance on motor and electric vehicle. The temperature effect lead to plant parameter variation on the feedback control loop also showed in [2]. In addition, the motor may arise faults such as Shaft: Imbalance, misalignment, and wear or Rotor: Imbalance, rotor bar faults, loose rotor, and eccentricity or Motor bearing housing: Mechanical looseness, misalignment. However, there are many faults or failures arise from around environment, as well as from system nonlinearities with large uncertainties become critical challenges in utilizing motors to obtain high precision position and accurate speed control. To solve this issue, the fault-tolerant control (FTC) technique for DC motors is proposed for actuator faults. The compensation process is performed once the actuator fault estimation obtained. There have been many researchers to study estimation of the actuator and sensor fault using various algorithms such as unknown input observer (UIO) as in [8–10], or using the augmented system per-formed by [11], using sliding mode observer as shown in [12–15], etc. Especially, Actuator and sensor fault estimation was effectively implemented using variable reconstruction shown in [16, 17]. In this paper, UIO is constructed based on the Lyapunov’s stability condition and LMI optimization algorithm to reconstruct the actuator faults that the control error dynamic is asymptotically stable. The decoupled process of actuator faults is performed via UIO reconstruction. Numerical simulation results show the effectiveness of the proposed method. The important contributions of paper can be shortened as follows: • A UIO reconstruction is constructed to estimate the actuator fault based on set up of control error to obtain asymptotically stable of the system. • Inequalities to apply LMI optimization algorithm is approved by Lyapunov stability. • The numerical simulation results of the actuator fault compensation process show the success of the motor’s proposed method.

6.2 DC Motor Model Formulation The control scheme of the DC motor is implemented by the applied voltage V a controls the angular velocity ω of the shaft. Where a simplified model of the DC motor is shown (Fig. 6.1) with the parameters shown in Table 6.1. The dynamics equation of the DC motor can be rewritten as [1, 17]:

6 An Application of Observer Reconstruction to Estimate Actuator …

57

Fig. 6.1 Scheme of DC motor and control loop design

Table 6.1 Variables and parameters of the DC motor Electrical dynamics

Mechanical dynamics

Variables

Parameters

i(t): Current (A)

R: Resistance ()

v(t): Voltage (V)

L: Inductance (H)

e(t): Back EMF (V)

: Motor torque constant (N m/A)

T m (t): Motor torque (N m)

f : Friction torque (N m s rad−1 )

ω(t): Angle velocity (rad s−1 )

J: Moment of inertia (kg m2 )

⎧ f (t) ⎪ i(t) − ω(t) ˙ = ⎨ ω(t) J J ⎪ ⎩ di = v(t) − (t) ω(t) − R(t) i(t) dt L L L

(6.1)

Resistance and K(t) parameter under the effects of temperature can be written as [1] 

  R(t) = R0 (t) + R0 (t)α t f − t0   (t) = 0 (t) + 0 (t)β t f − t0

(6.2)

where R0 (t) and 0 (t) are resistance and torque constant of DC motor at the temperature 20 °C. α, β are the parameters shown in Tables 6.2 and 6.3 [7]. t0 and t f are the temperature at 20 °C and at any time t respectively. From (6.1) and (6.2), the DC motor dynamics equation can be represented as Table 6.2 Temperature coefficient of metals [7]

Conductor material

α (°C)

Gold

0.0037

Silver

0.0038

Copper

0.0040

Aluminum

0.0043

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Table 6.3 Permanent magnet material temperature coefficients [7]

Magnet material

β (°C)

Maximum temperature (°C)

Cobalt nickel aluminum

−0.0002

540

Samarium cobalt

−0.0004

300

Neodium iron boron

−0.0012

150

Ceramics

−0.0020

300

⎧  0 (t) 0 (t)  f ⎪ ⎪ ω(t) ˙ = i(t) + β t f − t0 i(t) − ω(t) ⎪ ⎪ J J J ⎪ ⎪ ⎨ di  0 (t)i(t) v(t) 0 (t) 0 (t)  = − ω(t) − β t f − t0 ω(t) − ⎪ dt L L L L ⎪   ⎪ ⎪ ⎪ 0 (t)α t f − t0 ⎪ ⎩ i(t) − L

(6.3)

The DC motor dynamic system (6.3) may be described in state space as x(t) ˙ = x(t) + ϒu(t) + ζ (x, t) where x1 = i(t); x˙1 = x2 =

di ; dt

(6.4)

x3 = ω(t); x4 = ω(t) ˙ and

⎧

T ⎪ x = x1T x3T ⎪ ⎪ ⎪  ⎪ R0 (t) 0 (t)  ⎪ ⎪ x1 ⎨ x2 = − L − L 0 (t) − Jf x4 x3 ; J ⎪

  ⎪   ⎪ R0 (t)α (t f −t0 ) 1  (t) ⎪ 0 x1 ⎪ ⎪ ⎪ + L u(t) + −0 (t)  L  − L β t f − t0 ⎩ x 0 0 β t − t 3 f 0 J  R0 (t) 0 (t) 1 − L − L = ; ϒ= L ; 0 (t) − Jf 0 J

   R0 (t)α (t f −t0 ) 0 (t) x − − β t − t f 0 L L ζ (x, t) = 0 (t)  x(t); x(t) = 1  x 0 β t f − t0 3 J

6.3 UIO Design for Nonlinear System Considering a state-space equation of the nonlinear system subject to an unknown input with assuming that disturbance and actuator fault as the following form:

6 An Application of Observer Reconstruction to Estimate Actuator …



x(t) ˙ = x(t) + ϒu(t) + ζ (x, t) + Fa f a (t) + Dd(t) y(t) = x(t)

59

(6.5)

where x(t) ∈ R n is the state vector, y(t) ∈ R p is the outputs vector and d(t) ∈ R rd is the unknown input or disturbance vector, and f a (t) ∈ R f is the actuator fault. Here , ϒ, , and D are known constant matrices with suitable dimensions. ζ (x, t) is a nonlinear function vector ∀x(t), x(t) ∈ R n , u(t) ∈ R m . The equation of (6.5) can be rewritten in the following form: 

˙ E x(t) = x(t) + ϒu(t) + ζ (x, t) + F a f a (t) + Dd(t) y(t) = x(t)

(6.6)

where     Fa I 0 D ζ (x, t) ; E= n ; D= ; ζ (x, t) = 0 −I f 0 0 0f 0   

0 x(t) ϒ = 0 ; Fa = ; x(t) = ∈ Rn ; ϒ = f a (t) If 0 

=

with n = n + p, and f a (t) is the actuator fault. By using [18], a Lipschitz constraint of the nonlinear function vector ζ (x, t) can be expressed as:    ζ (x, t) ≤ χ x(t) − x(t) ˆ 

(6.7)

where     ζ (x, t) = ζ (x, t) − ζ x, ˆ t  Based on [16], the UIO paradigm can be designed in the influences of unknown inputs in the system (6.6) as: ⎧  

˙ = Mx(t) ˆ + Mϒu(t) + Mζ x, ⎪ z(t) ˆ t + L y(t) − yˆ (t) ⎪ ⎨ ˆ x(t) = z(t) + H y(t) ⎪ ⎪ ⎩ ˆ yˆ (t) = x(t)

(6.8)

ˆ ∈ R n , and yˆ (t) ∈ R p are state vector estimation of x(t), and measurement where x(t) output estimation vector, respectively. z(t) ∈ R n is the state vector of the observer.

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M ∈ R n×n , H ∈ R n× p , L ∈ R n× p are the observer matrices and these matrices should be designed according to the state estimation error vector. An estimation control error can be defined as: ˆ e(t) = x(t) − x(t)

(6.9)

˙ˆ ˙ = x(t) ˙ − x(t) e(t)   ˙ − z(t) ˙ = In − H x(t) ˙ ˙ − z(t) = M x(t)

(6.10)

and

where M = In − H Besides e y (t) = e(t)

(6.11)

Equation (6.4) can be rewritten as: ˙ = Mx(t) + Mϒu(t) + Mζ (x, t) + M F a f a (t) + M Dd(t) M E x(t)

(6.12)

and from (6.6), we obtain   ˙ˆ ˆ + Mϒu(t) + L y(t) − L y(t) ˆ + Mζ x, ˙ x(t) = Mx(t) ˆ t + H x(t)

  ˆ + L y(t) + H x(t) ˙ + Mϒu(t) + Mζ x, ˆ t = M − L x(t)

(6.13)

Using (6.7)–(6.11), we have:





˙ˆ ˙ − x(t) ˆ = M − L x(t) − M − L x(t) M E + H x(t) + M ζ (x, t) + M F a f a (t) + M Dd(t)

(6.14)

  ˆ t . where ζ (x, t) = ζ (x, t) − ζ x, From (6.14), the estimation error can be represented as:   ˙ = M − L e(t) + M ζ (x, t) + M F a f a (t) + M Dd(t) e(t) If the satisfy the following conditions

(6.15)

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61

M E + H = In

(6.16)

In 0 . − 0 f

(6.17)

The matrix M can be chosen:  M= From that, we can be calculated H as  H=

0 If

(6.18)

Lemma [8, 16] Consider the state space equation in the following form: ζ˙ (t) = τ (t) + λ(t)

(6.19)

where ∈ R n×n is the eigenvalues of a given matrix belong to the circular region D(ϕ, μ)with the center ϕ + j0 and the radius μif and only if there exists a symmetric positive definite matrix N ∈ R n×n such that the following condition holds: 

−N N( − ϕ In )