List of contributors |
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xiii | |
About the editors |
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xvii | |
Preface |
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xxi | |
Chapter 1 Impact of Artificial Intelligence on Future Green Communication |
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1 | (12) |
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1 | (1) |
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1.2 The History of Artificial Intelligence |
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1 | (3) |
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1.3 A Road Map of Using Artificial Intelligence for Green Communication |
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4 | (2) |
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1.4 Key Technologies to Make 5G in Reality Using Artificial Intelligence |
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6 | (3) |
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1.5 Features of Artificial Intelligence-Based Green Communication |
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9 | (1) |
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10 | (1) |
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11 | (1) |
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11 | (2) |
Chapter 2 Knowledge Representation and Reasoning in Al-Based Solutions and IoT Applications |
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13 | (38) |
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13 | (1) |
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13 | (1) |
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2.3 Knowledge Representation and Reasoning |
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14 | (1) |
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2.4 Knowledge Inference, Forward and Backward Chaining, and KL-One Languages |
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15 | (1) |
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2.5 Artificial Intelligence |
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16 | (1) |
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2.6 Artificial Intelligence for Information Technology |
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17 | (1) |
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2.7 AI, KRR and IoT Functions |
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18 | (1) |
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2.8 Artificial Intelligence Applications and Tools |
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19 | (1) |
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2.9 Machine Learning and Deep Learning |
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20 | (1) |
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2.10 Robotic Process Automation |
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20 | (2) |
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22 | (2) |
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2.12 Natural-Language Understanding and Interpretation |
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24 | (1) |
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2.13 Learning Using Privileged Information |
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25 | (1) |
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2.14 Picture Archiving and Communication System |
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26 | (1) |
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2.15 Infrastructure-Based Mobile Networks |
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26 | (2) |
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2.16 Dynamically Created Mobile Ad Hoc Networks |
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28 | (1) |
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2.17 Intelligent Agents, Conversational and Natural Intelligence |
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28 | (1) |
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2.18 Advanced Metering Infrastructure |
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29 | (2) |
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2.19 Distributed Automation Networks |
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31 | (1) |
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2.20 Optical Character Recognition and Human Minds |
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31 | (1) |
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2.21 Simple Neural and Biological Neural Networks |
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32 | (1) |
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2.22 Machine Intelligence Learning and Deep Learning |
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33 | (1) |
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2.23 Upper Ontology and Machine Translation |
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34 | (1) |
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2.24 Frame Problem and CycL Projects and Semantic |
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34 | (1) |
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2.25 Presenting, Reasoning, and Problem Solving |
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35 | (1) |
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2.26 Simple Neural Networks, Artificial Neural Networks |
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36 | (1) |
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2.27 Contextual Artificial Intelligence Perspectives |
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37 | (2) |
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2.28 Complex Artificial Intelligence Systems |
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39 | (1) |
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2.29 The Impact of Smart Dust on IoT Technology |
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40 | (3) |
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2.30 The Next Generation of Computers and Functional Trends |
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43 | (1) |
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2.31 Conclusion and Future Reading |
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44 | (1) |
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45 | (3) |
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48 | (3) |
Chapter 3 Artificial Intelligence, Internet of Things, and Communication Networks |
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51 | (16) |
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51 | (1) |
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3.2 Machine Learning/Artificial Intelligence-Assisted Networking |
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52 | (1) |
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3.3 Artificial Intelligence in Communication Networks |
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52 | (2) |
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3.4 Transforming Optical Industries by Artificial Intelligence |
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54 | (1) |
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3.5 Artificial Intelligence in Optical Transmission |
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55 | (1) |
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3.6 Artificial Intelligence in Optical Networking |
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56 | (1) |
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3.7 Advantages of Machine Learning in Networking |
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57 | (1) |
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3.8 Optical Technologies to Support Internet of Things |
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58 | (1) |
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3.9 Applications of Internet of Things with Optical Technologies |
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59 | (1) |
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60 | (1) |
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61 | (6) |
Chapter 4 AI and IoT Capabilities: Standards, Procedures, Applications, and Protocols |
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67 | (18) |
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67 | (1) |
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68 | (11) |
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4.3 Artificial Intelligence |
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79 | (2) |
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81 | (1) |
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82 | (3) |
Chapter 5 Internet of Intelligent Things: Injection of Intelligence into IoT Devices |
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85 | (18) |
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85 | (1) |
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86 | (1) |
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5.3 Architecture of Internet of Things |
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86 | (2) |
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88 | (2) |
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5.5 Artificial Intelligence and Internet of Things |
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90 | (2) |
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5.6 Applications of Internet of Things |
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92 | (4) |
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5.7 Conclusion and Future Directions |
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96 | (1) |
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97 | (6) |
Chapter 6 Artificial Intelligence and Machine Learning Applications in Cloud Computing and Internet of Things |
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103 | (22) |
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103 | (3) |
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6.2 Application of Machine Learning in Different Sectors of Society |
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106 | (5) |
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6.3 Artificial Intelligence with Multiple Application Fields |
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111 | (6) |
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6.4 Application of Internet of Things in Medical Field |
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117 | (1) |
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118 | (1) |
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119 | (4) |
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123 | (2) |
Chapter 7 Knowledge Representation for Causal Calculi on Internet of Things |
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125 | (22) |
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125 | (1) |
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125 | (1) |
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7.3 Knowledge Representation |
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126 | (1) |
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7.4 Dynamic Knowledge Representation |
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126 | (1) |
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7.5 Intersection of Knowledge Representation, Causal Calculus, and Internet of Things |
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126 | (1) |
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7.6 First-Order Logic and Predicate Calculus for KR, CC, and IoT |
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127 | (1) |
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7.7 Structural Causal Model |
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128 | (1) |
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129 | (1) |
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130 | (1) |
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7.10 Pearl's Bayesian Networks |
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130 | (2) |
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7.11 Halpern-Pearl Causality |
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132 | (2) |
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7.12 Shafer's Probability Trees |
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134 | (2) |
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7.13 CP-Logic from Prolog and ProbLog |
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136 | (4) |
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7.14 Probabilities and Causal Relationships |
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140 | (1) |
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7.15 Mathematical Rules for Do-Calculus |
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141 | (1) |
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142 | (1) |
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7.17 Simulation and Equivalence Class |
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142 | (1) |
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7.18 Future Research Direction |
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143 | (1) |
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143 | (1) |
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144 | (1) |
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145 | (2) |
Chapter 8 Examining the Internet of Things-Based Elegant Cultivation Technique in Digital Bharat |
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147 | (14) |
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147 | (1) |
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147 | (5) |
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8.3 Issues to Consider Before Designing Your Elegant Farming Solution |
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152 | (2) |
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8.4 Applications of Internet of Things in Agriculture |
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154 | (3) |
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8.5 Why Will Internet of Things? |
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157 | (1) |
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8.6 Future of Internet of Things in Agriculture |
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157 | (1) |
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158 | (1) |
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159 | (2) |
Chapter 9 Machine Learning and Internet of Things for Smart Processing |
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161 | (22) |
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161 | (1) |
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9.2 Information Labeling and Information Segmentation |
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162 | (1) |
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9.3 Machine Learning Tools |
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163 | (5) |
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9.4 Computer Vision and Neural Network |
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168 | (2) |
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9.5 Challenges and Solutions |
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170 | (3) |
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173 | (3) |
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9.7 Examination and Results |
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176 | (2) |
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178 | (1) |
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9.9 Image Mining for Medical Diagnosis |
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179 | (1) |
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179 | (1) |
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180 | (3) |
Chapter 10 Intelligent Smart Home Energy Efficiency Model Using Artificial Intelligence and Internet of Things |
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183 | (28) |
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183 | (1) |
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184 | (4) |
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10.3 Energy Efficiency Model |
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188 | (2) |
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10.4 Need for Energy-Efficient Intelligent Smart Home Model |
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190 | (1) |
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10.5 Basic Terminology Used |
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191 | (2) |
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10.6 Components of Proposed Model |
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193 | (1) |
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10.7 Working of Proposed Model |
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193 | (2) |
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10.8 Technology Used in Making the Proposed Model |
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195 | (4) |
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10.9 Comparison Between Models |
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199 | (1) |
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10.10 Advantages of Proposed Model |
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199 | (1) |
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10.11 Applications of Proposed Model |
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199 | (5) |
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10.12 Conclusion and Future Scope |
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204 | (1) |
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205 | (6) |
Chapter 11 Adaptive Complex Systems: Digital Twins |
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211 | (28) |
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211 | (1) |
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11.2 Approaches to Complex System Engineering |
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212 | (1) |
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213 | (8) |
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11.4 Technology for Digital Twins |
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221 | (10) |
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231 | (6) |
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11.6 Conclusion and Research Roadmap |
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237 | (1) |
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237 | (2) |
Chapter 12 Artificial Intelligence Powered Healthcare Internet of Things Devices and Their Role |
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239 | (12) |
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239 | (1) |
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12.2 Role of Internet of Things in Healthcare |
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239 | (2) |
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241 | (1) |
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12.4 Uses for Healthcare Establishments |
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242 | (1) |
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12.5 Encountering Possible Challenges and Vulnerabilities |
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243 | (1) |
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12.6 Innovation and Business Perspective of Internet |
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244 | (2) |
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12.7 Robotics and Nanotechnology Amalgamation |
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246 | (1) |
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12.8 The Implication of Nanotechnology |
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247 | (1) |
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12.9 Complementing Government Schemes |
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248 | (1) |
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249 | (1) |
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250 | (1) |
Chapter 13 IoT: Integrating Artificial Intelligence With IoT to Solve Pervasive IoT Issues |
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251 | (18) |
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251 | (15) |
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266 | (1) |
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267 | (2) |
Chapter 14 Intelligent Energy-Oriented Home |
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269 | (22) |
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269 | (1) |
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14.2 Smart Homes, Ambient Intelligence, and Smart Grids |
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270 | (2) |
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14.3 Architecture for an Intelligent Energy-Oriented Home |
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272 | (3) |
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14.4 Implementation of Systems for Illustration of Intelligent Behaviors in an Intelligent Energy-Oriented Home |
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275 | (7) |
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14.5 Test and Analysis of the Intelligent Energy-Oriented Home |
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282 | (4) |
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14.6 Conclusions and Further Developments |
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286 | (1) |
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287 | (1) |
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287 | (2) |
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289 | (2) |
Chapter 15 Corporate Cybersecurity Strategy to Enable Artificial Intelligence and Internet of Things |
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291 | (26) |
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291 | (1) |
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292 | (1) |
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15.3 Understanding the Cyber-Adversarial System |
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293 | (1) |
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15.4 Threat Vectors: Internal and External Threats |
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294 | (1) |
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15.5 Nonmalicious Noncompliance |
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294 | (1) |
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15.6 Malicious Noncompliance |
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294 | (1) |
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15.7 Financially Motivated Cyber-Attackers |
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295 | (1) |
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15.8 Ideologically and Politically Motivated Cyber-Attackers |
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295 | (1) |
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15.9 Anatomy of a Cyber-Attack |
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296 | (2) |
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15.10 Why Cybersecurity and Why Corporate Strategy |
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298 | (1) |
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298 | (2) |
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15.12 Causes of Cybersecurity Inertia |
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300 | (1) |
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15.13 Three I's of Corporate Cybersecurity Strategy |
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300 | (1) |
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15.14 Cybersecurity Systems Engineering |
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301 | (1) |
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15.15 Enterprise Architecture Frameworks |
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302 | (1) |
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302 | (1) |
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15.17 US Department of Defense Architecture Framework |
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303 | (1) |
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15.18 Service-Oriented Architecture |
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304 | (1) |
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15.19 Cybersecurity IT Portfolio Management |
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304 | (2) |
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15.20 Smarter Cybersecurity Leveraging Artificial Intelligence |
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306 | (1) |
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15.21 IoT and Growing Cybersecurity Risk |
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306 | (2) |
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15.22 The Change Management Challenge |
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308 | (1) |
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15.23 Cybersecurity Expertise |
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308 | (1) |
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15.24 Assess Current State |
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309 | (1) |
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15.25 Making Cyber Part of Corporate Strategy |
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310 | (2) |
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312 | (5) |
Chapter 16 Role of Artificial Intelligence and the Internet of Things in Agriculture |
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317 | (14) |
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317 | (1) |
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16.2 Artificial Intelligence in Agriculture |
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318 | (1) |
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16.3 Components of Artificial Intelligence Required in Agriculture |
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318 | (2) |
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16.4 Role of Machine Learning in Agriculture |
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320 | (2) |
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16.5 Models for Farmers Services |
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322 | (1) |
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16.6 Internet of Things Applications in Agriculture |
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323 | (3) |
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16.7 Currently Used Artificial Intelligence |
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326 | (2) |
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16.8 Challenges with Artificial Intelligence and Internet of Things in Agriculture |
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328 | (1) |
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328 | (1) |
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329 | (2) |
Chapter 17 Integrating Artificial Intelligence/Internet of Things Technologies to Support Medical Devices and Systems |
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331 | (20) |
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17.1 Introduction-Medical Devices and Healthcare Systems |
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331 | (1) |
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17.2 Problems in Conventional Medical Systems |
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331 | (1) |
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17.3 Categories of Medical Devices |
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332 | (1) |
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17.4 Internet of Things/Artificial Intelligence in Medical Devices and Healthcare |
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333 | (2) |
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17.5 Enabling Technologies of Internet of Things in Medical Devices and Systems |
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335 | (2) |
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17.6 Monitoring Using Internet of Things/Artificial Intelligence in Medical Devices and System |
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337 | (1) |
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17.7 Critical Issues and Challenges of Internet of Things in Medical Devices and Systems |
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338 | (2) |
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17.8 IoT Medical Devices and System Security |
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340 | (3) |
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343 | (4) |
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347 | (1) |
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347 | (4) |
Chapter 18 Machine Learning for Optical Communication to Solve Pervasive Issues of Internet of Things |
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351 | (16) |
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351 | (1) |
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18.2 Machine Learning Techniques |
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351 | (2) |
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18.3 Machine Learning Techniques Used in Optical Communication |
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353 | (1) |
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18.4 Machine Learning in Physical Layer |
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353 | (5) |
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18.5 Machine Learning in Network Layer |
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358 | (5) |
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363 | (1) |
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364 | (3) |
Chapter 19 Impact of Artificial Intelligence to Solve Pervasive Issues of Sensor Networks of Internet of Things |
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367 | (10) |
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367 | (1) |
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19.2 Sensor Network Technology |
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368 | (3) |
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19.3 Pervasive Issues Related to Sensor Networks |
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371 | (3) |
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19.4 Role of Artificial Intelligence to Solve Pervasive Issues of Sensor Network |
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374 | (1) |
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19.5 Features of Artificial Intelligence in the Internet of Things Revolution |
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374 | (1) |
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375 | (1) |
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375 | (1) |
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375 | (2) |
Chapter 20 Principles and Foundations of Artificial Intelligence and Internet of Things Technology |
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377 | (16) |
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377 | (1) |
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378 | (2) |
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20.3 Existing Technologies in Artificial Intelligence and Internet of Things |
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380 | (8) |
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20.4 Future of Artificial Intelligence and Internet of Things |
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388 | (1) |
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389 | (1) |
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390 | (3) |
Index |
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