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