Details

Human Communication Technology


Human Communication Technology

Internet-of-Robotic-Things and Ubiquitous Computing
Artificial Intelligence and Soft Computing for Industrial Transformation 1. Aufl.

von: R. Anandan, G. Suseendran, S. Balamurugan, Ashish Mishra, D. Balaganesh

179,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 25.10.2021
ISBN/EAN: 9781119752158
Sprache: englisch
Anzahl Seiten: 496

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Beschreibungen

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

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