R. Anandan PhD, completed his PhD in Computer Science and Engineering, is an IBMS/390 Mainframe professional, and is recognized as a Chartered Engineer from the Institution of Engineers in India and received a fellowship from Bose Science Society, India. He is a Professor in the Department of Computer Science and Engineering, School of Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. He has published more than 110 research papers in various international journals, authored 9 books in the computer science and engineering disciplines, and has received 13 awards. G. Suseendran PhD, received his PhD in Information Technology-Mathematics from Presidency College, University of Madras, Tamil Nadu, India. He passed away during the production of this book. S. Balamurugan PhD, SMIEEE, ACM Distinguished Speaker, received his PhD from Anna University, India. He has published 57 books, 300+ international journals/conferences, and 100 patents. He is the Director of the Albert Einstein Engineering and Research Labs. He is also the Vice-Chairman of the Renewable Energy Society of India (RESI). He is serving as a research consultant to many companies, startups, SMEs, and MSMEs. He has received numerous awards for research at national and international levels. Ashish Mishra PhD, is a professor in the Department of Computer Science and Engineering, Gyan Ganga Institute of Technology and Sciences, Jabalpur [M.P]. He received his PhD from AISECT University, Bhopal, India. He has published many research papers in reputed journals and conferences, been granted 1 patent, and has authored/edited 4 books in the areas of data mining, image processing, and artificial intelligence. D. Balaganesh PhD, is a Dean of Faculty Computer Science and Multimedia, Lincoln University College, Malaysia. He has developed software applications “Timetable Automation”, “Online Exam” as well as published the book Computer Applications in Business.

• Preface xix1 Internet of Robotic Things: A New Architecture and Platform 1V. Vijayalakshmi, S. Vimal and M. Saravanan1.1 Introduction 21.1.1 Architecture 31.1.1.1 Achievability of the Proposed Architecture 61.1.1.2 Qualities of IoRT Architecture 61.1.1.3 Reasonable Existing Robots for IoRT Architecture 81.2 Platforms 91.2.1 Cloud Robotics Platforms 91.2.2 IoRT Platform 101.2.3 Design a Platform 111.2.4 The Main Components of the Proposed Approach 111.2.5 IoRT Platform Design 121.2.6 Interconnection Design 151.2.7 Research Methodology 171.2.8 Advancement Process—Systems Thinking 171.2.8.1 Development Process 171.2.9 Trial Setup-to Confirm the Functionalities 181.3 Conclusion 201.4 Future Work 21References 212 Brain–Computer Interface Using Electroencephalographic Signals for the Internet of Robotic Things 27R. Raja Sudharsan and J. Deny2.1 Introduction 282.2 Electroencephalography Signal Acquisition Methods 302.2.1 Invasive Method 312.2.2 Non-Invasive Method 322.3 Electroencephalography Signal-Based BCI 322.3.1 Prefrontal Cortex in Controlling Concentration Strength 332.3.2 Neurosky Mind-Wave Mobile 342.3.2.1 Electroencephalography Signal Processing Devices 342.3.3 Electromyography Signal Extraction of Features and Its Signal Classifications 372.4 IoRT-Based Hardware for BCI 402.5 Software Setup for IoRT 402.6 Results and Discussions 422.7 Conclusion 47References 483 Automated Verification and Validation of IoRT Systems 55S.V. Gayetri Devi and C. Nalini3.1 Introduction 563.1.1 Automating V&V—An Important Key to Success 583.2 Program Analysis of IoRT Applications 593.2.1 Need for Program Analysis 593.2.2 Aspects to Consider in Program Analysis of IoRT Systems 593.3 Formal Verification of IoRT Systems 613.3.1 Automated Model Checking 613.3.2 The Model Checking Process 623.3.2.1 PRISM 653.3.2.2 UPPAAL 663.3.2.3 SPIN Model Checker 673.3.3 Automated Theorem Prover 693.3.3.1 ALT-ERGO 703.3.4 Static Analysis 713.3.4.1 CODESONAR 723.4 Validation of IoRT Systems 733.4.1 IoRT Testing Methods 793.4.2 Design of IoRT Test 803.5 Automated Validation 803.5.1 Use of Service Visualization 823.5.2 Steps for Automated Validation of IoRT Systems 823.5.3 Choice of Appropriate Tool for Automated Validation 843.5.4 IoRT Systems Open Source Automated Validation Tools 853.5.5 Some of Significant Open Source Test Automation Frameworks 863.5.6 Finally IoRT Security Testing 863.5.7 Prevalent Approaches for Security Validation 873.5.8 IoRT Security Tools 87References 884 Light Fidelity (Li-Fi) Technology: The Future Man–Machine–Machine Interaction Medium 91J.M. Gnanasekar and T. Veeramakali4.1 Introduction 924.1.1 Need for Li-Fi 944.2 Literature Survey 944.2.1 An Overview on Man-to-Machine Interaction System 954.2.2 Review on Machine to Machine (M2M) Interaction 964.2.2.1 System Model 974.3 Light Fidelity Technology 984.3.1 Modulation Techniques Supporting Li-Fi 994.3.1.1 Single Carrier Modulation (SCM) 1004.3.1.2 Multi Carrier Modulation 1004.3.1.3 Li-Fi Specific Modulation 1014.3.2 Components of Li-Fi 1024.3.2.1 Light Emitting Diode (LED) 1024.3.2.2 Photodiode 1034.3.2.3 Transmitter Block 1034.3.2.4 Receiver Block 1044.4 Li-Fi Applications in Real Word Scenario 1054.4.1 Indoor Navigation System for Blind People 1054.4.2 Vehicle to Vehicle Communication 1064.4.3 Li-Fi in Hospital 1074.4.4 Li-Fi Applications for Pharmacies and the Pharmaceutical Industry 1094.4.5 Li-Fi in Workplace 1104.5 Conclusion 111References 1115 Healthcare Management-Predictive Analysis (IoRT) 113L. Mary Gladence, V. Maria Anu and Y. Bevish Jinila5.1 Introduction 1145.1.1 Naive Bayes Classifier Prediction for SPAM 1155.1.2 Internet of Robotic Things (IoRT) 1155.2 Related Work 1165.3 Fuzzy Time Interval Sequential Pattern (FTISPAM) 1175.3.1 FTI SPAM Using GA Algorithm 1185.3.1.1 Chromosome Generation 1195.3.1.2 Fitness Function 1205.3.1.3 Crossover 1205.3.1.4 Mutation 1215.3.1.5 Termination 1215.3.2 Patterns Matching Using SCI 1215.3.3 Pattern Classification Based on SCI Value 1225.3.4 Significant Pattern Evaluation 1235.4 Detection of Congestive Heart Failure Using Automatic Classifier 1245.4.1 Analyzing the Dataset 1255.4.2 Data Collection 1265.4.2.1 Long-Term HRV Measures 1275.4.2.2 Attribute Selection 1285.4.3 Automatic Classifier—Belief Network 1285.5 Experimental Analysis 1305.6 Conclusion 132References 1346 Multimodal Context-Sensitive Human Communication Interaction System Using Artificial Intelligence-Based Human-Centered Computing 137S. Murugan, R. Manikandan and Ambeshwar Kumar6.1 Introduction 1386.2 Literature Survey 1416.3 Proposed Model 1456.3.1 Multimodal Data 1456.3.2 Dimensionality Reduction 1466.3.3 Principal Component Analysis 1476.3.4 Reduce the Number of Dimensions 1486.3.5 CNN 1486.3.6 CNN Layers 1496.3.6.1 Convolution Layers 1496.3.6.2 Padding Layer 1506.3.6.3 Pooling/Subsampling Layers 1506.3.6.4 Nonlinear Layers 1516.3.7 ReLU 1516.3.7.1 Fully Connected Layers 1526.3.7.2 Activation Layer 1526.3.8 LSTM 1526.3.9 Weighted Combination of Networks 1536.4 Experimental Results 1556.4.1 Accuracy 1556.4.2 Sensibility 1566.4.3 Specificity 1566.4.4 A Predictive Positive Value (PPV) 1566.4.5 Negative Predictive Value (NPV) 1566.5 Conclusion 1596.6 Future Scope 159References 1607 AI, Planning and Control Algorithms for IoRT Systems 163T.R. Thamizhvani, R.J. Hemalatha, R. Chandrasekaran and A. Josephin Arockia Dhivya7.1 Introduction 1647.2 General Architecture of IoRT 1677.2.1 Hardware Layer 1687.2.2 Network Layer 1687.2.3 Internet Layer 1687.2.4 Infrastructure Layer 1687.2.5 Application Layer 1697.3 Artificial Intelligence in IoRT Systems 1707.3.1 Technologies of Robotic Things 1707.3.2 Artificial Intelligence in IoRT 1727.4 Control Algorithms and Procedures for IoRT Systems 1807.4.1 Adaptation of IoRT Technologies 1837.4.2 Multi-Robotic Technologies 1867.5 Application of IoRT in Different Fields 187References 1908 Enhancements in Communication Protocols That Powered IoRT 193T. Anusha and M. Pushpalatha8.1 Introduction 1948.2 IoRT Communication Architecture 1948.2.1 Robots and Things 1968.2.2 Wireless Link Layer 1978.2.3 Networking Layer 1978.2.4 Communication Layer 198­­8.2.5 Application Layer 1988.3 Bridging Robotics and IoT 1988.4 Robot as a Node in IoT 2008.4.1 Enhancements in Low Power WPANs 2008.4.1.1 Enhancements in IEEE 802.15.4 2008.4.1.2 Enhancements in Bluetooth 2018.4.1.3 Network Layer Protocols 2028.4.2 Enhancements in Low Power WLANs 2038.4.2.1 Enhancements in IEEE 802.11 2038.4.3 Enhancements in Low Power WWANs 2048.4.3.1 LoRaWAN 2058.4.3.2 5G 2058.5 Robots as Edge Device in IoT 2068.5.1 Constrained RESTful Environments (CoRE) 2068.5.2 The Constrained Application Protocol (CoAP) 2078.5.2.1 Latest in CoAP 2078.5.3 The MQTT-SN Protocol 2078.5.4 The Data Distribution Service (DDS) 2088.5.5 Data Formats 2098.6 Challenges and Research Solutions 2098.7 Open Platforms for IoRT Applications 2108.8 Industrial Drive for Interoperability 2128.8.1 The Zigbee Alliance 2128.8.2 The Thread Group 2138.8.3 The WiFi Alliance 2138.8.4 The LoRa Alliance 2148.9 Conclusion 214References 2159 Real Time Hazardous Gas Classification and Management System Using Artificial Neural Networks 219R. Anitha, S. Anusooya, V. Jean Shilpa and Mohamed Hishaam9.1 Introduction 2209.2 Existing Methodology 2209.3 Proposed Methodology 2219.4 Hardware & Software Requirements 2239.4.1 Hardware Requirements 2239.4.1.1 Gas Sensors Employed in Hazardous Detection 2239.4.1.2 NI Wireless Sensor Node 3202 2269.4.1.3 NI WSN gateway (NI 9795) 2289.4.1.4 COMPACT RIO (NI-9082) 2299.5 Experimental Setup 2329.5.1 Data Set Preparation 2339.5.2 Artificial Neural Network Model Creation 2369.6 Results and Discussion 2409.7 Conclusion and Future Work 243References 24410 Hierarchical Elitism GSO Algorithm For Pattern Recognition 245Ilavazhagi Bala S. and Latha Parthiban10.1 Introduction 24610.2 Related Works 24710.3 Methodology 24810.3.1 Additive Kuan Speckle Noise Filtering Model 24910.3.2 Hierarchical Elitism Gene GSO of MNN in Pattern Recognition 25110.4 Experimental Setup 25510.5 Discussion 25510.5.1 Scenario 1: Computational Time 25610.5.2 Scenario 2: Computational Complexity 25710.5.3 Scenario 3: Pattern Recognition Accuracy 25810.6 Conclusion 260References 26011 Multidimensional Survey of Machine Learning Application in IoT (Internet of Things) 263Anurag Sinha and Pooja Jha11.1 Machine Learning—An Introduction 26411.1.1 Classification of Machine Learning 26511.2 Internet of Things 26711.3 ML in IoT 26811.3.1 Overview 26811.4 Literature Review 27011.5 Different Machine Learning Algorithm 27111.5.1 Bayesian Measurements 27111.5.2 K-Nearest Neighbors (k-NN) 27211.5.3 Neural Network 27211.5.4 Decision Tree (DT) 27211.5.5 Principal Component Analysis (PCA) t 27311.5.6 K-Mean Calculations 27311.5.7 Strength Teaching 27311.6 Internet of Things in Different Frameworks 27311.6.1 Computing Framework 27411.6.1.1 Fog Calculation 27411.6.1.2 Estimation Edge 27511.6.1.3 Distributed Computing 27511.6.1.4 Circulated Figuring 27611.7 Smart Cities 27611.7.1 Use Case 27711.7.1.1 Insightful Vitality 27711.7.1.2 Brilliant Portability 27711.7.1.3 Urban Arranging 27811.7.2 Attributes of the Smart City 27811.8 Smart Transportation 27911.8.1 Machine Learning and IoT in Smart Transportation 28011.8.2 Markov Model 28311.8.3 Decision Structures 28411.9 Application of Research 28511.9.1 In Energy 28511.9.2 In Routing 28511.9.3 In Living 28611.9.4 Application in Industry 28711.10 Machine Learning for IoT Security 29011.10.1 Used Machine Learning Algorithms 29111.10.2 Intrusion Detection 29311.10.3 Authentication 29411.11 Conclusion 294References 29512 IoT-Based Bias Analysis in Acoustic Feedback Using Time-Variant Adaptive Algorithm in Hearing Aids 301G. Jayanthi and Latha Parthiban12.1 Introduction 30212.2 Existence of Acoustic Feedback 30312.2.1 Causes of Acoustic Feedback 30312.2.2 Amplification of Feedback Process 30412.3 Analysis of Acoustic Feedback 30412.3.1 Frequency Analysis Using Impulse Response 30512.3.2 Feedback Analysis Using Phase Difference 30612.4 Filtering of Signals 31012.4.1 Digital Filters 31012.4.2 Adaptive Filters 31112.4.2.1 Order of Adaptive Filters 31112.4.2.2 Filter Coefficients in Adaptive Filters 31112.4.3 Adaptive Feedback Cancellation 31212.4.3.1 Non-Continuous Adaptation 31212.4.3.2 Continuous Adaptation 31412.4.4 Estimation of Acoustic Feedback 31512.4.5 Analysis of Acoustic Feedback Signal 31712.4.5.1 Forward Path of the Signal 31712.4.5.2 Feedback Path of the Signal 31712.4.5.3 Bias Identification 31912.5 Adaptive Algorithms 32012.5.1 Step-Size Algorithms 32112.5.1.1 Fixed Step-Size 32212.5.1.2 Variable Step-Size 32312.6 Simulation 32512.6.1 Training of Adaptive Filter for Removal of Acoustic Feedback 32512.6.2 Testing of Adaptive Filter 32612.6.2.1 Subjective and Objective Evaluation Using KEMAR 32612.6.2.2 Experimental Setup Using Manikin Channel 32712.7 Performance Evaluation 32812.8 Conclusions 333References 33413 Internet of Things Platform for Smart Farming 337R. Anandan, Deepak B.S., G. Suseendran and Noor Zaman Jhanjhi13.1 Introduction 33713.2 History 33813.3 Electronic Terminologies 33913.3.1 Input and Output Devices 33913.3.2 GPIO 34013.3.3 ADC 34013.3.4 Communication Protocols 34013.3.4.1 UART 34013.3.4.2 I2C 34013.3.4.3 SPI 34113.4 IoT Cloud Architecture 34113.4.1 Communication From User to Cloud Platform 34213.4.2 Communication From Cloud Platform To IoT Device 34213.5 Components of IoT 34313.5.1 Real-Time Analytics 34313.5.1.1 Understanding Driving Styles 34313.5.1.2 Creating Driver Segmentation 34413.5.1.3 Identifying Risky Neighbors 34413.5.1.4 Creating Risk Profiles 34413.5.1.5 Comparing Microsegments 34413.5.2 Machine Learning 34413.5.2.1 Understanding the Farm 34513.5.2.2 Creating Farm Segmentation 34513.5.2.3 Identifying Risky Factors 34613.5.2.4 Creating Risk Profiles 34613.5.2.5 Comparing Microsegments 34613.5.3 Sensors 34613.5.3.1 Temperature Sensor 34713.5.3.2 Water Quality Sensor 34713.5.3.3 Humidity Sensor 34713.5.3.4 Light Dependent Resistor 34713.5.4 Embedded Systems 34913.6 IoT-Based Crop Management System 35013.6.1 Temperature and Humidity Management System 35013.6.1.1 Project Circuit 35113.6.1.2 Connections 35313.6.1.3 Program 35613.6.2 Water Quality Monitoring System 36113.6.2.1 Dissolved Oxygen Monitoring System 36113.6.2.2 pH Monitoring System 36313.6.3 Light Intensity Monitoring System 36413.6.3.1 Project Circuit 36513.6.3.2 Connections 36513.6.3.3 Program Code 36613.7 Future Prospects 36713.8 Conclusion 368References 36914 Scrutinizing the Level of Awareness on Green Computing Practices in Combating Covid-19 at Institute of Health Science-Gaborone 371Ishmael Gala and Srinath Doss14.1 Introduction 37214.1.1 Institute of Health Science-Gaborone 37314.1.2 Research Objectives 37414.1.3 Green Computing 37414.1.4 Covid-19 37514.1.5 The Necessity of Green Computing in Combating Covid-19 37614.1.6 Green Computing Awareness 37914.1.7 Knowledge 38014.1.8 Attitude 38114.1.9 Behavior 38114.2 Research Methodology 38114.2.1 Target Population 38214.2.2 Sample Frame 38214.2.3 Questionnaire as a Data Collection Instrument 38314.2.4 Validity and Reliability 38314.3 Analysis of Data and Presentation 38314.3.1 Demographics: Gender and Age 38414.3.2 How Effective is Green Computing Policies in Combating Covid-19 at Institute of Health Science-Gaborone? 38614.3.3 What are Green Computing Practices Among Users at Gaborone Institute of Health Science? 38814.3.4 What is the Role of Green Computing Training in Combating Covid-19 at Institute of HealthScience-Gaborone? 38814.3.5 What is the Likelihood of Threats Associated With a Lack of Awareness on Green ComputingPractices While Combating Covid-19? 39014.3.6 What is the Level of User Conduct, Awareness and Attitude With Regard to Awareness on Green Computing Practices at Institute of Health Science-Gaborone? 39114.4 Recommendations 39314.4.1 Green Computing Policy 39314.4.2 Risk Assessment 39414.4.3 Green Computing Awareness Training 39414.4.4 Compliance 39414.5 Conclusion 394References 39515 Detailed Analysis of Medical IoT Using Wireless Body Sensor Network and Application of IoT in Healthcare 401Anurag Sinha and Shubham Singh15.1 Introduction 40215.2 History of IoT 40315.3 Internet of Objects 40515.3.1 Definitions 40515.3.2 Internet of Things (IoT): Data Flow 40615.3.3 Structure of IoT—Enabling Technologies 40615.4 Applications of IoT 40715.5 IoT in Healthcare of Human Beings 40715.5.1 Remote Healthcare—Telemedicine 40815.5.2 Telemedicine System—Overview 40815.6 Telemedicine Through a Speech-Based Query System 40915.6.1 Outpatient Monitoring 41015.6.2 Telemedicine Umbrella Service 41015.6.3 Advantages of the Telemedicine Service 41115.6.4 Some Examples of IoT in the Health Sector 41115.7 Conclusion 41215.8 Sensors 41215.8.1 Classification of Sensors 41315.8.2 Commonly Used Sensors in BSNs 41515.8.2.1 Accelerometer 41715.8.2.2 ECG Sensors 41815.8.2.3 Pressure Sensors 41915.8.2.4 Respiration Sensors 42015.9 Design of Sensor Nodes 42015.9.1 Energy Control 42115.9.2 Fault Diagnosis 42215.9.3 Reduction of Sensor Nodes 42215.10 Applications of BSNs 42315.11 Conclusions 42315.12 Introduction 42415.12.1 From WBANs to BBNs 42515.12.2 Overview of WBAN 42515.12.3 Architecture 42615.12.4 Standards 42715.12.5 Applications 42715.13 Body-to-Body Network Concept 42815.14 Conclusions 429References 43016 DCMM: A Data Capture and Risk Management for Wireless Sensing Using IoT Platform 435Siripuri Kiran, Bandi Krishna, Janga Vijaykumar and Sridhar manda16.1 Introduction 43616.2 Background 43816.2.1 Internet of Things 43816.2.2 Middleware Data Acquisition 43816.2.3 Context Acquisition 43916.3 Architecture 43916.3.1 Proposed Architecture 43916.3.1.1 Protocol Adaption 44116.3.1.2 Device Management 44316.3.1.3 Data Handler 44516.4 Implementation 44616.4.1 Requirement and Functionality 44616.4.1.1 Requirement 44616.4.1.2 Functionalities 44716.4.2 Adopted Technologies 44816.4.2.1 Middleware Software 44816.4.2.2 Usability Dependency 44916.4.2.3 Sensor Node Software 44916.4.2.4 Hardware Technology 45016.4.2.5 Sensors 45116.4.3 Details of IoT Hub 45216.4.3.1 Data Poster 45216.4.3.2 Data Management 45216.4.3.3 Data Listener 45316.4.3.4 Models 45416.5 Results and Discussions 45416.6 Conclusion 460References 461Index 463

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