A unique book explaining how perception, location, communication, cognition, computation, networking, propulsion, integration of federated Internet of Robotic Things (IoRT) and digital platforms are important components of new-generation IoRT applications through continuous, real-time interaction with the world.
The 16 chapters in this book discuss new architectures, networking paradigms, trustworthy structures, and platforms for the integration of applications across various business and industrial domains that are needed for the emergence of intelligent things (static or mobile) in collaborative autonomous fleets. These new apps speed up the progress of paradigms of autonomous system design and the proliferation of the Internet of Robotic Things (IoRT). Collaborative robotic things can communicate with other things in the IoRT, learn independently, interact securely with the world, people, and other things, and acquire characteristics that make them self-maintaining, self-aware, self-healing, and fail-safe operational. Due to the ubiquitous nature of collaborative robotic things, the IoRT, which binds together the sensors and the objects of robotic things, is gaining popularity. Therefore, the information contained in this book will provide readers with a better understanding of this interdisciplinary field.
Audience
Researchers in various fields including computer science, IoT, artificial intelligence, machine learning, and big data analytics.
Table of Contents
Preface xix
1 Internet of Robotic Things: A New Architecture and Platform 1
V. Vijayalakshmi, S. Vimal and M. Saravanan
1.1 Introduction 2
1.1.1 Architecture 3
1.1.1.1 Achievability of the Proposed Architecture 6
1.1.1.2 Qualities of IoRT Architecture 6
1.1.1.3 Reasonable Existing Robots for IoRT Architecture 8
1.2 Platforms 9
1.2.1 Cloud Robotics Platforms 9
1.2.2 IoRT Platform 10
1.2.3 Design a Platform 11
1.2.4 The Main Components of the Proposed Approach 11
1.2.5 IoRT Platform Design 12
1.2.6 Interconnection Design 15
1.2.7 Research Methodology 17
1.2.8 Advancement Process - Systems Thinking 17
1.2.8.1 Development Process 17
1.2.9 Trial Setup-to Confirm the Functionalities 18
1.3 Conclusion 20
1.4 Future Work 21
References 21
2 Brain-Computer Interface Using Electroencephalographic Signals for the Internet of Robotic Things 27
R. Raja Sudharsan and J. Deny
2.1 Introduction 28
2.2 Electroencephalography Signal Acquisition Methods 30
2.2.1 Invasive Method 31
2.2.2 Non-Invasive Method 32
2.3 Electroencephalography Signal-Based BCI 32
2.3.1 Prefrontal Cortex in Controlling Concentration Strength 33
2.3.2 Neurosky Mind-Wave Mobile 34
2.3.2.1 Electroencephalography Signal Processing Devices 34
2.3.3 Electromyography Signal Extraction of Features and Its Signal Classifications 37
2.4 IoRT-Based Hardware for BCI 40
2.5 Software Setup for IoRT 40
2.6 Results and Discussions 42
2.7 Conclusion 47
References 48
3 Automated Verification and Validation of IoRT Systems 55
S.V. Gayetri Devi and C. Nalini
3.1 Introduction 56
3.1.1 Automating V&V - An Important Key to Success 58
3.2 Program Analysis of IoRT Applications 59
3.2.1 Need for Program Analysis 59
3.2.2 Aspects to Consider in Program Analysis of IoRT Systems 59
3.3 Formal Verification of IoRT Systems 61
3.3.1 Automated Model Checking 61
3.3.2 The Model Checking Process 62
3.3.2.1 PRISM 65
3.3.2.2 UPPAAL 66
3.3.2.3 SPIN Model Checker 67
3.3.3 Automated Theorem Prover 69
3.3.3.1 ALT-ERGO 70
3.3.4 Static Analysis 71
3.3.4.1 CODESONAR 72
3.4 Validation of IoRT Systems 73
3.4.1 IoRT Testing Methods 79
3.4.2 Design of IoRT Test 80
3.5 Automated Validation 80
3.5.1 Use of Service Visualization 82
3.5.2 Steps for Automated Validation of IoRT Systems 82
3.5.3 Choice of Appropriate Tool for Automated Validation 84
3.5.4 IoRT Systems Open Source Automated Validation Tools 85
3.5.5 Some of Significant Open Source Test Automation Frameworks 86
3.5.6 Finally IoRT Security Testing 86
3.5.7 Prevalent Approaches for Security Validation 87
3.5.8 IoRT Security Tools 87
References 88
4 Light Fidelity (Li-Fi) Technology: The Future Man-Machine-Machine Interaction Medium 91
J.M. Gnanasekar and T. Veeramakali
4.1 Introduction 92
4.1.1 Need for Li-Fi 94
4.2 Literature Survey 94
4.2.1 An Overview on Man-to-Machine Interaction System 95
4.2.2 Review on Machine to Machine (M2M) Interaction 96
4.2.2.1 System Model 97
4.3 Light Fidelity Technology 98
4.3.1 Modulation Techniques Supporting Li-Fi 99
4.3.1.1 Single Carrier Modulation (SCM) 100
4.3.1.2 Multi Carrier Modulation 100
4.3.1.3 Li-Fi Specific Modulation 101
4.3.2 Components of Li-Fi 102
4.3.2.1 Light Emitting Diode (LED) 102
4.3.2.2 Photodiode 103
4.3.2.3 Transmitter Block 103
4.3.2.4 Receiver Block 104
4.4 Li-Fi Applications in Real Word Scenario 105
4.4.1 Indoor Navigation System for Blind People 105
4.4.2 Vehicle to Vehicle Communication 106
4.4.3 Li-Fi in Hospital 107
4.4.4 Li-Fi Applications for Pharmacies and the Pharmaceutical Industry 109
4.4.5 Li-Fi in Workplace 110
4.5 Conclusion 111
References 111
5 Healthcare Management-Predictive Analysis (IoRT) 113
L. Mary Gladence, V. Maria Anu and Y. Bevish Jinila
5.1 Introduction 114
5.1.1 Naive Bayes Classifier Prediction for SPAM 115
5.1.2 Internet of Robotic Things (IoRT) 115
5.2 Related Work 116
5.3 Fuzzy Time Interval Sequential Pattern (FTISPAM) 117
5.3.1 FTI SPAM Using GA Algorithm 118
5.3.1.1 Chromosome Generation 119
5.3.1.2 Fitness Function 120
5.3.1.3 Crossover 120
5.3.1.4 Mutation 121
5.3.1.5 Termination 121
5.3.2 Patterns Matching Using SCI 121
5.3.3 Pattern Classification Based on SCI Value 122
5.3.4 Significant Pattern Evaluation 123
5.4 Detection of Congestive Heart Failure Using Automatic Classifier 124
5.4.1 Analyzing the Dataset 125
5.4.2 Data Collection 126
5.4.2.1 Long-Term HRV Measures 127
5.4.2.2 Attribute Selection 128
5.4.3 Automatic Classifier - Belief Network 128
5.5 Experimental Analysis 130
5.6 Conclusion 132
References 134
6 Multimodal Context-Sensitive Human Communication Interaction System Using Artificial Intelligence-Based Human-Centered Computing 137
S. Murugan, R. Manikandan and Ambeshwar Kumar
6.1 Introduction 138
6.2 Literature Survey 141
6.3 Proposed Model 145
6.3.1 Multimodal Data 145
6.3.2 Dimensionality Reduction 146
6.3.3 Principal Component Analysis 147
6.3.4 Reduce the Number of Dimensions 148
6.3.5 CNN 148
6.3.6 CNN Layers 149
6.3.6.1 Convolution Layers 149
6.3.6.2 Padding Layer 150
6.3.6.3 Pooling/Subsampling Layers 150
6.3.6.4 Nonlinear Layers 151
6.3.7 ReLU 151
6.3.7.1 Fully Connected Layers 152
6.3.7.2 Activation Layer 152
6.3.8 LSTM 152
6.3.9 Weighted Combination of Networks 153
6.4 Experimental Results 155
6.4.1 Accuracy 155
6.4.2 Sensibility 156
6.4.3 Specificity 156
6.4.4 A Predictive Positive Value (PPV) 156
6.4.5 Negative Predictive Value (NPV) 156
6.5 Conclusion 159
6.6 Future Scope 159
References 160
7 AI, Planning and Control Algorithms for IoRT Systems 163
T.R. Thamizhvani, R.J. Hemalatha, R. Chandrasekaran and A. Josephin Arockia Dhivya
7.1 Introduction 164
7.2 General Architecture of IoRT 167
7.2.1 Hardware Layer 168
7.2.2 Network Layer 168
7.2.3 Internet Layer 168
7.2.4 Infrastructure Layer 168
7.2.5 Application Layer 169
7.3 Artificial Intelligence in IoRT Systems 170
7.3.1 Technologies of Robotic Things 170
7.3.2 Artificial Intelligence in IoRT 172
7.4 Control Algorithms and Procedures for IoRT Systems 180
7.4.1 Adaptation of IoRT Technologies 183
7.4.2 Multi-Robotic Technologies 186
7.5 Application of IoRT in Different Fields 187
References 190
8 Enhancements in Communication Protocols That Powered IoRT 193
T. Anusha and M. Pushpalatha
8.1 Introduction 194
8.2 IoRT Communication Architecture 194
8.2.1 Robots and Things 196
8.2.2 Wireless Link Layer 197
8.2.3 Networking Layer 197
8.2.4 Communication Layer 198
8.2.5 Application Layer 198
8.3 Bridging Robotics and IoT 198
8.4 Robot as a Node in IoT 200
8.4.1 Enhancements in Low Power WPANs 200
8.4.1.1 Enhancements in IEEE 802.15.4 200
8.4.1.2 Enhancements in Bluetooth 201
8.4.1.3 Network Layer Protocols 202
8.4.2 Enhancements in Low Power WLANs 203
8.4.2.1 Enhancements in IEEE 802.11 203
8.4.3 Enhancements in Low Power WWANs 204
8.4.3.1 LoRaWAN 205
8.4.3.2 5G 205
8.5 Robots as Edge Device in IoT 206
8.5.1 Constrained RESTful Environments (CoRE) 206
8.5.2 The Constrained Application Protocol (CoAP) 207
8.5.2.1 Latest in CoAP 207
8.5.3 The MQTT-SN Protocol 207
8.5.4 The Data Distribution Service (DDS) 208
8.5.5 Data Formats 209
8.6 Challenges and Research Solutions 209
8.7 Open Platforms for IoRT Applications 210
8.8 Industrial Drive for Interoperability 212
8.8.1 The Zigbee Alliance 212
8.8.2 The Thread Group 213
8.8.3 The WiFi Alliance 213
8.8.4 The LoRa Alliance 214
8.9 Conclusion 214
References 215
9 Real Time Hazardous Gas Classification and Management System Using Artificial Neural Networks 219
R. Anitha, S. Anusooya, V. Jean Shilpa and Mohamed Hishaam
9.1 Introduction 220
9.2 Existing Methodology 220
9.3 Proposed Methodology 221
9.4 Hardware & Software Requirements 223
9.4.1 Hardware Requirements 223
9.4.1.1 Gas Sensors Employed in Hazardous Detection 223
9.4.1.2 NI Wireless Sensor Node 3202 226
9.4.1.3 NI WSN gateway (NI 9795) 228
9.4.1.4 COMPACT RIO (NI-9082) 229
9.5 Experimental Setup 232
9.5.1 Data Set Preparation 233
9.5.2 Artificial Neural Network Model Creation 236
9.6 Results and Discussion 240
9.7 Conclusion and Future Work 243
References 244
10 Hierarchical Elitism GSO Algorithm For Pattern Recognition 245
Ilavazhagi Bala S. and Latha Parthiban
10.1 Introduction 246
10.2 Related Works 247
10.3 Methodology 248
10.3.1 Additive Kuan Speckle Noise Filtering Model 249
10.3.2 Hierarchical Elitism Gene GSO of MNN in Pattern Recognition 251
10.4 Experimental Setup 255
10.5 Discussion 255
10.5.1 Scenario 1: Computational Time 256
10.5.2 Scenario 2: Computational Complexity 257
10.5.3 Scenario 3: Pattern Recognition Accuracy 258
10.6 Conclusion 260
References 260
11 Multidimensional Survey of Machine Learning Application in IoT (Internet of Things) 263
Anurag Sinha and Pooja Jha
11.1 Machine Learning - An Introduction 264
11.1.1 Classification of Machine Learning 265
11.2 Internet of Things 267
11.3 ML in IoT 268
11.3.1 Overview 268
11.4 Literature Review 270
11.5 Different Machine Learning Algorithm 271
11.5.1 Bayesian Measurements 271
11.5.2 K-Nearest Neighbors (k-NN) 272
11.5.3 Neural Network 272
11.5.4 Decision Tree (DT) 272
11.5.5 Principal Component Analysis (PCA) t 273
11.5.6 K-Mean Calculations 273
11.5.7 Strength Teaching 273
11.6 Internet of Things in Different Frameworks 273
11.6.1 Computing Framework 274
11.6.1.1 Fog Calculation 274
11.6.1.2 Estimation Edge 275
11.6.1.3 Distributed Computing 275
11.6.1.4 Circulated Figuring 276
11.7 Smart Cities 276
11.7.1 Use Case 277
11.7.1.1 Insightful Vitality 277
11.7.1.2 Brilliant Portability 277
11.7.1.3 Urban Arranging 278
11.7.2 Attributes of the Smart City 278
11.8 Smart Transportation 279
11.8.1 Machine Learning and IoT in Smart Transportation 280
11.8.2 Markov Model 283
11.8.3 Decision Structures 284
11.9 Application of Research 285
11.9.1 In Energy 285
11.9.2 In Routing 285
11.9.3 In Living 286
11.9.4 Application in Industry 287
11.10 Machine Learning for IoT Security 290
11.10.1 Used Machine Learning Algorithms 291
11.10.2 Intrusion Detection 293
11.10.3 Authentication 294
11.11 Conclusion 294
References 295
12 IoT-Based Bias Analysis in Acoustic Feedback Using Time-Variant Adaptive Algorithm in Hearing Aids 301
G. Jayanthi and Latha Parthiban
12.1 Introduction 302
12.2 Existence of Acoustic Feedback 303
12.2.1 Causes of Acoustic Feedback 303
12.2.2 Amplification of Feedback Process 304
12.3 Analysis of Acoustic Feedback 304
12.3.1 Frequency Analysis Using Impulse Response 305
12.3.2 Feedback Analysis Using Phase Difference 306
12.4 Filtering of Signals 310
12.4.1 Digital Filters 310
12.4.2 Adaptive Filters 311
12.4.2.1 Order of Adaptive Filters 311
12.4.2.2 Filter Coefficients in Adaptive Filters 311
12.4.3 Adaptive Feedback Cancellation 312
12.4.3.1 Non-Continuous Adaptation 312
12.4.3.2 Continuous Adaptation 314
12.4.4 Estimation of Acoustic Feedback 315
12.4.5 Analysis of Acoustic Feedback Signal 317
12.4.5.1 Forward Path of the Signal 317
12.4.5.2 Feedback Path of the Signal 317
12.4.5.3 Bias Identification 319
12.5 Adaptive Algorithms 320
12.5.1 Step-Size Algorithms 321
12.5.1.1 Fixed Step-Size 322
12.5.1.2 Variable Step-Size 323
12.6 Simulation 325
12.6.1 Training of Adaptive Filter for Removal of Acoustic Feedback 325
12.6.2 Testing of Adaptive Filter 326
12.6.2.1 Subjective and Objective Evaluation Using KEMAR 326
12.6.2.2 Experimental Setup Using Manikin Channel 327
12.7 Performance Evaluation 328
12.8 Conclusions 333
References 334
13 Internet of Things Platform for Smart Farming 337
R. Anandan, Deepak B.S., G. Suseendran and Noor Zaman Jhanjhi
13.1 Introduction 337
13.2 History 338
13.3 Electronic Terminologies 339
13.3.1 Input and Output Devices 339
13.3.2 GPIO 340
13.3.3 ADC 340
13.3.4 Communication Protocols 340
13.3.4.1 UART 340
13.3.4.2 I2C 340
13.3.4.3 SPI 341
13.4 IoT Cloud Architecture 341
13.4.1 Communication From User to Cloud Platform 342
13.4.2 Communication From Cloud Platform To IoT Device 342
13.5 Components of IoT 343
13.5.1 Real-Time Analytics 343
13.5.1.1 Understanding Driving Styles 343
13.5.1.2 Creating Driver Segmentation 344
13.5.1.3 Identifying Risky Neighbors 344
13.5.1.4 Creating Risk Profiles 344
13.5.1.5 Comparing Microsegments 344
13.5.2 Machine Learning 344
13.5.2.1 Understanding the Farm 345
13.5.2.2 Creating Farm Segmentation 345
13.5.2.3 Identifying Risky Factors 346
13.5.2.4 Creating Risk Profiles 346
13.5.2.5 Comparing Microsegments 346
13.5.3 Sensors 346
13.5.3.1 Temperature Sensor 347
13.5.3.2 Water Quality Sensor 347
13.5.3.3 Humidity Sensor 347
13.5.3.4 Light Dependent Resistor 347
13.5.4 Embedded Systems 349
13.6 IoT-Based Crop Management System 350
13.6.1 Temperature and Humidity Management System 350
13.6.1.1 Project Circuit 351
13.6.1.2 Connections 353
13.6.1.3 Program 356
13.6.2 Water Quality Monitoring System 361
13.6.2.1 Dissolved Oxygen Monitoring System 361
13.6.2.2 pH Monitoring System 363
13.6.3 Light Intensity Monitoring System 364
13.6.3.1 Project Circuit 365
13.6.3.2 Connections 365
13.6.3.3 Program Code 366
13.7 Future Prospects 367
13.8 Conclusion 368
References 369
14 Scrutinizing the Level of Awareness on Green Computing Practices in Combating Covid-19 at Institute of Health Science-Gaborone 371
Ishmael Gala and Srinath Doss
14.1 Introduction 372
14.1.1 Institute of Health Science-Gaborone 373
14.1.2 Research Objectives 374
14.1.3 Green Computing 374
14.1.4 Covid-19 375
14.1.5 The Necessity of Green Computing in Combating Covid-19 376
14.1.6 Green Computing Awareness 379
14.1.7 Knowledge 380
14.1.8 Attitude 381
14.1.9 Behavior 381
14.2 Research Methodology 381
14.2.1 Target Population 382
14.2.2 Sample Frame 382
14.2.3 Questionnaire as a Data Collection Instrument 383
14.2.4 Validity and Reliability 383
14.3 Analysis of Data and Presentation 383
14.3.1 Demographics: Gender and Age 384
14.3.2 How Effective is Green Computing Policies in Combating Covid-19 at Institute of Health Science-Gaborone? 386
14.3.3 What are Green Computing Practices Among Users at Gaborone Institute of Health Science? 388
14.3.4 What is the Role of Green Computing Training in Combating Covid-19 at Institute of Health
Science-Gaborone? 388
14.3.5 What is the Likelihood of Threats Associated With a Lack of Awareness on Green Computing
Practices While Combating Covid-19? 390
14.3.6 What is the Level of User Conduct, Awareness and Attitude With Regard to Awareness on Green Computing Practices at Institute of Health Science-Gaborone? 391
14.4 Recommendations 393
14.4.1 Green Computing Policy 393
14.4.2 Risk Assessment 394
14.4.3 Green Computing Awareness Training 394
14.4.4 Compliance 394
14.5 Conclusion 394
References 395
15 Detailed Analysis of Medical IoT Using Wireless Body Sensor Network and Application of IoT in Healthcare 401
Anurag Sinha and Shubham Singh
15.1 Introduction 402
15.2 History of IoT 403
15.3 Internet of Objects 405
15.3.1 Definitions 405
15.3.2 Internet of Things (IoT): Data Flow 406
15.3.3 Structure of IoT - Enabling Technologies 406
15.4 Applications of IoT 407
15.5 IoT in Healthcare of Human Beings 407
15.5.1 Remote Healthcare - Telemedicine 408
15.5.2 Telemedicine System - Overview 408
15.6 Telemedicine Through a Speech-Based Query System 409
15.6.1 Outpatient Monitoring 410
15.6.2 Telemedicine Umbrella Service 410
15.6.3 Advantages of the Telemedicine Service 411
15.6.4 Some Examples of IoT in the Health Sector 411
15.7 Conclusion 412
15.8 Sensors 412
15.8.1 Classification of Sensors 413
15.8.2 Commonly Used Sensors in BSNs 415
15.8.2.1 Accelerometer 417
15.8.2.2 ECG Sensors 418
15.8.2.3 Pressure Sensors 419
15.8.2.4 Respiration Sensors 420
15.9 Design of Sensor Nodes 420
15.9.1 Energy Control 421
15.9.2 Fault Diagnosis 422
15.9.3 Reduction of Sensor Nodes 422
15.10 Applications of BSNs 423
15.11 Conclusions 423
15.12 Introduction 424
15.12.1 From WBANs to BBNs 425
15.12.2 Overview of WBAN 425
15.12.3 Architecture 426
15.12.4 Standards 427
15.12.5 Applications 427
15.13 Body-to-Body Network Concept 428
15.14 Conclusions 429
References 430
16 DCMM: A Data Capture and Risk Management for Wireless Sensing Using IoT Platform 435
Siripuri Kiran, Bandi Krishna, Janga Vijaykumar and Sridhar manda
16.1 Introduction 436
16.2 Background 438
16.2.1 Internet of Things 438
16.2.2 Middleware Data Acquisition 438
16.2.3 Context Acquisition 439
16.3 Architecture 439
16.3.1 Proposed Architecture 439
16.3.1.1 Protocol Adaption 441
16.3.1.2 Device Management 443
16.3.1.3 Data Handler 445
16.4 Implementation 446
16.4.1 Requirement and Functionality 446
16.4.1.1 Requirement 446
16.4.1.2 Functionalities 447
16.4.2 Adopted Technologies 448
16.4.2.1 Middleware Software 448
16.4.2.2 Usability Dependency 449
16.4.2.3 Sensor Node Software 449
16.4.2.4 Hardware Technology 450
16.4.2.5 Sensors 451
16.4.3 Details of IoT Hub 452
16.4.3.1 Data Poster 452
16.4.3.2 Data Management 452
16.4.3.3 Data Listener 453
16.4.3.4 Models 454
16.5 Results and Discussions 454
16.6 Conclusion 460
References 461
Index 463
