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Design and Deploy IoT Network & Security with Microsoft Azure : Embrace Microsoft Azure for IoT Network Enhancement and Security Uplift / / by Puthiyavan Udayakumar, Dr. R Anandan
| Design and Deploy IoT Network & Security with Microsoft Azure : Embrace Microsoft Azure for IoT Network Enhancement and Security Uplift / / by Puthiyavan Udayakumar, Dr. R Anandan |
| Autore | Udayakumar Puthiyavan |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2024 |
| Descrizione fisica | 1 online resource (583 pages) |
| Disciplina | 005.268 |
| Altri autori (Persone) | AnandanR |
| Soggetto topico |
Microsoft software
Microsoft .NET Framework Microsoft |
| ISBN | 9798868809088 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Get Started with IoT Network and Security -- Chapter 2: Design and Deploy Azure Edge Services -- Chapter 3 : Design and Deploy Azure IoT Networks -- Chapter 4 - Design and Deploy Azure IoT Security -- Chapter 5: Design and Deploy Azure IoT Monitoring and Management -- Additional Resources -- Glossary of Terms. |
| Record Nr. | UNINA-9910906191203321 |
Udayakumar Puthiyavan
|
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Design and Deploy Microsoft Defender for IoT : Leveraging Cloud-based Analytics and Machine Learning Capabilities / / by Puthiyavan Udayakumar, Dr. R. Anandan
| Design and Deploy Microsoft Defender for IoT : Leveraging Cloud-based Analytics and Machine Learning Capabilities / / by Puthiyavan Udayakumar, Dr. R. Anandan |
| Autore | Udayakumar Puthiyavan |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2024 |
| Descrizione fisica | 1 online resource (365 pages) |
| Disciplina | 004.67/8 |
| Altri autori (Persone) | AnandanR |
| Soggetto topico |
Microsoft software
Microsoft .NET Framework Data protection Microsoft Data and Information Security |
| ISBN | 979-88-6880-239-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Get Started with IoT -- Chapter 2: Develop Architecture and Strategy for IoT -- Chapter 3: Plan Microsoft Defender for IoT -- Chapter 4: Deploy Microsoft Defender for IoT -- Chapter 5: Manage and Monitor your Defender for IoT System. |
| Record Nr. | UNINA-9910861086703321 |
|
Udayakumar Puthiyavan
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
How COVID-19 is accelerating the digital revolution : challenges and opportunities / / edited by R. Anandan [and four others]
| How COVID-19 is accelerating the digital revolution : challenges and opportunities / / edited by R. Anandan [and four others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (220 pages) |
| Disciplina | 614.592414 |
| Soggetto topico | Information society |
| ISBN | 3-030-98167-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910574045903321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
How COVID-19 is accelerating the digital revolution : challenges and opportunities / / edited by R. Anandan [and four others]
| How COVID-19 is accelerating the digital revolution : challenges and opportunities / / edited by R. Anandan [and four others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (220 pages) |
| Disciplina | 614.592414 |
| Soggetto topico | Information society |
| ISBN | 3-030-98167-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996475762003316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Human communication technology : internet-of-robotic-things and ubiquitous computing / / edited by R. Anandan [and four others]
| Human communication technology : internet-of-robotic-things and ubiquitous computing / / edited by R. Anandan [and four others] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2022] |
| Descrizione fisica | 1 online resource (512 pages) |
| Disciplina | 006.3 |
| Collana | Artificial Intelligence and Soft Computing for Industrial Transformation Ser. |
| Soggetto topico | Internet of things |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-75215-9
1-119-75216-7 1-119-75214-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Internet of Robotic Things: A New Architecture and Platform -- 1.1 Introduction -- 1.1.1 Architecture -- 1.1.1.1 Achievability of the Proposed Architecture -- 1.1.1.2 Qualities of IoRT Architecture -- 1.1.1.3 Reasonable Existing Robots for IoRT Architecture -- 1.2 Platforms -- 1.2.1 Cloud Robotics Platforms -- 1.2.2 IoRT Platform -- 1.2.3 Design a Platform -- 1.2.4 The Main Components of the Proposed Approach -- 1.2.5 IoRT Platform Design -- 1.2.6 Interconnection Design -- 1.2.7 Research Methodology -- 1.2.8 Advancement Process-Systems Thinking -- 1.2.8.1 Development Process -- 1.2.9 Trial Setup-to Confirm the Functionalities -- 1.3 Conclusion -- 1.4 Future Work -- References -- 2 Brain-Computer Interface Using Electroencephalographic Signals for the Internet of Robotic Things -- 2.1 Introduction -- 2.2 Electroencephalography Signal Acquisition Methods -- 2.2.1 Invasive Method -- 2.2.2 Non-Invasive Method -- 2.3 Electroencephalography Signal-Based BCI -- 2.3.1 Prefrontal Cortex in Controlling Concentration Strength -- 2.3.2 Neurosky Mind-Wave Mobile -- 2.3.2.1 Electroencephalography Signal Processing Devices -- 2.3.3 Electromyography Signal Extraction of Features and Its Signal Classifications -- 2.4 IoRT-Based Hardware for BCI -- 2.5 Software Setup for IoRT -- 2.6 Results and Discussions -- 2.7 Conclusion -- References -- 3 Automated Verification and Validation of IoRT Systems -- 3.1 Introduction -- 3.1.1 Automating V& -- V-An Important Key to Success -- 3.2 Program Analysis of IoRT Applications -- 3.2.1 Need for Program Analysis -- 3.2.2 Aspects to Consider in Program Analysis of IoRT Systems -- 3.3 Formal Verification of IoRT Systems -- 3.3.1 Automated Model Checking -- 3.3.2 The Model Checking Process -- 3.3.2.1 PRISM -- 3.3.2.2 UPPAAL.
3.3.2.3 SPIN Model Checker -- 3.3.3 Automated Theorem Prover -- 3.3.3.1 ALT-ERGO -- 3.3.4 Static Analysis -- 3.3.4.1 CODESONAR -- 3.4 Validation of IoRT Systems -- 3.4.1 IoRT Testing Methods -- 3.4.2 Design of IoRT Test -- 3.5 Automated Validation -- 3.5.1 Use of Service Visualization -- 3.5.2 Steps for Automated Validation of IoRT Systems -- 3.5.3 Choice of Appropriate Tool for Automated Validation -- 3.5.4 IoRT Systems Open Source Automated Validation Tools -- 3.5.5 Some Significant Open Source Test Automation Frameworks -- 3.5.6 Finally IoRT Security Testing -- 3.5.7 Prevalent Approaches for Security Validation -- 3.5.8 IoRT Security Tools -- References -- 4 Light Fidelity (Li-Fi) Technology: The Future Man-Machine-Machine Interaction Medium -- 4.1 Introduction -- 4.1.1 Need for Li-Fi -- 4.2 Literature Survey -- 4.2.1 An Overview on Man-to-Machine Interaction System -- 4.2.2 Review on Machine to Machine (M2M) Interaction -- 4.2.2.1 System Model -- 4.3 Light Fidelity Technology -- 4.3.1 Modulation Techniques Supporting Li-Fi -- 4.3.1.1 Single Carrier Modulation (SCM) -- 4.3.1.2 Multi Carrier Modulation -- 4.3.1.3 Li-Fi Specific Modulation -- 4.3.2 Components of Li-Fi -- 4.3.2.1 Light Emitting Diode (LED) -- 4.3.2.2 Photodiode -- 4.3.2.3 Transmitter Block -- 4.3.2.4 Receiver Block -- 4.4 Li-Fi Applications in Real Word Scenario -- 4.4.1 Indoor Navigation System for Blind People -- 4.4.2 Vehicle to Vehicle Communication -- 4.4.3 Li-Fi in Hospital -- 4.4.4 Li-Fi Applications for Pharmacies and the Pharmaceutical Industry -- 4.4.5 Li-Fi in Workplace -- 4.5 Conclusion -- References -- 5 Healthcare Management-Predictive Analysis (IoRT) -- 5.1 Introduction -- 5.1.1 Naive Bayes Classifier Prediction for SPAM -- 5.1.2 Internet of Robotic Things (IoRT) -- 5.2 Related Work -- 5.3 Fuzzy Time Interval Sequential Pattern (FTISPAM). 5.3.1 FTI SPAM Using GA Algorithm -- 5.3.1.1 Chromosome Generation -- 5.3.1.2 Fitness Function -- 5.3.1.3 Crossover -- 5.3.1.4 Mutation -- 5.3.1.5 Termination -- 5.3.2 Patterns Matching Using SCI -- 5.3.3 Pattern Classification Based on SCI Value -- 5.3.4 Significant Pattern Evaluation -- 5.4 Detection of Congestive Heart Failure Using Automatic Classifier -- 5.4.1 Analyzing the Dataset -- 5.4.2 Data Collection -- 5.4.2.1 Long-Term HRV Measures -- 5.4.2.2 Attribute Selection -- 5.4.3 Automatic Classifier-Belief Network -- 5.5 Experimental Analysis -- 5.6 Conclusion -- References -- 6 Multimodal Context-Sensitive Human Communication Interaction System Using Artificial Intelligence-Based Human-Centered Computing -- 6.1 Introduction -- 6.2 Literature Survey -- 6.3 Proposed Model -- 6.3.1 Multimodal Data -- 6.3.2 Dimensionality Reduction -- 6.3.3 Principal Component Analysis -- 6.3.4 Reduce the Number of Dimensions -- 6.3.5 CNN -- 6.3.6 CNN Layers -- 6.3.6.1 Convolution Layers -- 6.3.6.2 Padding Layer -- 6.3.6.3 Pooling/Subsampling Layers -- 6.3.6.4 Nonlinear Layers -- 6.3.7 ReLU -- 6.3.7.1 Fully Connected Layers -- 6.3.7.2 Activation Layer -- 6.3.8 LSTM -- 6.3.9 Weighted Combination of Networks -- 6.4 Experimental Results -- 6.4.1 Accuracy -- 6.4.2 Sensibility -- 6.4.3 Specificity -- 6.4.4 A Predictive Positive Value (PPV) -- 6.4.5 Negative Predictive Value (NPV) -- 6.5 Conclusion -- 6.6 Future Scope -- References -- 7 AI, Planning and Control Algorithms for IoRT Systems -- 7.1 Introduction -- 7.2 General Architecture of IoRT -- 7.2.1 Hardware Layer -- 7.2.2 Network Layer -- 7.2.3 Internet Layer -- 7.2.4 Infrastructure Layer -- 7.2.5 Application Layer -- 7.3 Artificial Intelligence in IoRT Systems -- 7.3.1 Technologies of Robotic Things -- 7.3.2 Artificial Intelligence in IoRT -- 7.4 Control Algorithms and Procedures for IoRT Systems. 7.4.1 Adaptation of IoRT Technologies -- 7.4.2 Multi-Robotic Technologies -- 7.5 Application of IoRT in Different Fields -- References -- 8 Enhancements in Communication Protocols That Powered IoRT -- 8.1 Introduction -- 8.2 IoRT Communication Architecture -- 8.2.1 Robots and Things -- 8.2.2 Wireless Link Layer -- 8.2.3 Networking Layer -- 8.2.4 Communication Layer -- 8.2.5 Application Layer -- 8.3 Bridging Robotics and IoT -- 8.4 Robot as a Node in IoT -- 8.4.1 Enhancements in Low Power WPANs -- 8.4.1.1 Enhancements in IEEE 802.15.4 -- 8.4.1.2 Enhancements in Bluetooth -- 8.4.1.3 Network Layer Protocols -- 8.4.2 Enhancements in Low Power WLANs -- 8.4.2.1 Enhancements in IEEE 802.11 -- 8.4.3 Enhancements in Low Power WWANs -- 8.4.3.1 LoRaWAN -- 8.4.3.2 5G -- 8.5 Robots as Edge Device in IoT -- 8.5.1 Constrained RESTful Environments (CoRE) -- 8.5.2 The Constrained Application Protocol (CoAP) -- 8.5.2.1 Latest in CoAP -- 8.5.3 The MQTT-SN Protocol -- 8.5.4 The Data Distribution Service (DDS) -- 8.5.5 Data Formats -- 8.6 Challenges and Research Solutions -- 8.7 Open Platforms for IoRT Applications -- 8.8 Industrial Drive for Interoperability -- 8.8.1 The Zigbee Alliance -- 8.8.2 The Thread Group -- 8.8.3 The WiFi Alliance -- 8.8.4 The LoRa Alliance -- 8.9 Conclusion -- References -- 9 Real Time Hazardous Gas Classification and Management System Using Artificial Neural Networks -- 9.1 Introduction -- 9.2 Existing Methodology -- 9.3 Proposed Methodology -- 9.4 Hardware & -- Software Requirements -- 9.4.1 Hardware Requirements -- 9.4.1.1 Gas Sensors Employed in Hazardous Detection -- 9.4.1.2 NI Wireless Sensor Node 3202 -- 9.4.1.3 NI WSN Gateway (NI 9795) -- 9.4.1.4 COMPACT RIO (NI-9082) -- 9.5 Experimental Setup -- 9.5.1 Data Set Preparation -- 9.5.2 Artificial Neural Network Model Creation -- 9.6 Results and Discussion. 9.7 Conclusion and Future Work -- References -- 10 Hierarchical Elitism GSO Algorithm For Pattern Recognition -- 10.1 Introduction -- 10.2 Related Works -- 10.3 Methodology -- 10.3.1 Additive Kuan Speckle Noise Filtering Model -- 10.3.2 Hierarchical Elitism Gene GSO of MNN in Pattern Recognition -- 10.4 Experimental Setup -- 10.5 Discussion -- 10.5.1 Scenario 1: Computational Time -- 10.5.2 Scenario 2: Computational Complexity -- 10.5.3 Scenario 3: Pattern Recognition Accuracy -- 10.6 Conclusion -- References -- 11 Multidimensional Survey of Machine Learning Application in IoT (Internet of Things) -- 11.1 Machine Learning-An Introduction -- 11.1.1 Classification of Machine Learning -- 11.2 Internet of Things -- 11.3 ML in IoT -- 11.3.1 Overview -- 11.4 Literature Review -- 11.5 Different Machine Learning Algorithm -- 11.5.1 Bayesian Measurements -- 11.5.2 K-Nearest Neighbors (k-NN) -- 11.5.3 Neural Network -- 11.5.4 Decision Tree (DT) -- 11.5.5 Principal Component Analysis (PCA) t -- 11.5.6 K-Mean Calculations -- 11.5.7 Strength Teaching -- 11.6 Internet of Things in Different Frameworks -- 11.6.1 Computing Framework -- 11.6.1.1 Fog Calculation -- 11.6.1.2 Estimation Edge -- 11.6.1.3 Distributed Computing -- 11.6.1.4 Circulated Figuring -- 11.7 Smart Cities -- 11.7.1 Use Case -- 11.7.1.1 Insightful Vitality -- 11.7.1.2 Brilliant Portability -- 11.7.1.3 Urban Arranging -- 11.7.2 Attributes of the Smart City -- 11.8 Smart Transportation -- 11.8.1 Machine Learning and IoT in Smart Transportation -- 11.8.2 Markov Model -- 11.8.3 Decision Structures -- 11.9 Application of Research -- 11.9.1 In Energy -- 11.9.2 In Routing -- 11.9.3 In Living -- 11.9.4 Application in Industry -- 11.10 Machine Learning for IoT Security -- 11.10.1 Used Machine Learning Algorithms -- 11.10.2 Intrusion Detection -- 11.10.3 Authentication -- 11.11 Conclusion -- References. 12 IoT-Based Bias Analysis in Acoustic Feedback Using Time-Variant Adaptive Algorithm in Hearing Aids. |
| Record Nr. | UNINA-9910555252503321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Human communication technology : internet-of-robotic-things and ubiquitous computing / / edited by R. Anandan [and four others]
| Human communication technology : internet-of-robotic-things and ubiquitous computing / / edited by R. Anandan [and four others] |
| Edizione | [1st edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2022] |
| Descrizione fisica | 1 online resource (512 pages) |
| Disciplina | 006.3 |
| Collana | Artificial Intelligence and Soft Computing for Industrial Transformation |
| Soggetto topico |
Internet of things
Artificial intelligence Computational intelligence Telecommunication |
| ISBN |
1-119-75215-9
1-119-75216-7 1-119-75214-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Internet of Robotic Things: A New Architecture and Platform -- 1.1 Introduction -- 1.1.1 Architecture -- 1.1.1.1 Achievability of the Proposed Architecture -- 1.1.1.2 Qualities of IoRT Architecture -- 1.1.1.3 Reasonable Existing Robots for IoRT Architecture -- 1.2 Platforms -- 1.2.1 Cloud Robotics Platforms -- 1.2.2 IoRT Platform -- 1.2.3 Design a Platform -- 1.2.4 The Main Components of the Proposed Approach -- 1.2.5 IoRT Platform Design -- 1.2.6 Interconnection Design -- 1.2.7 Research Methodology -- 1.2.8 Advancement Process-Systems Thinking -- 1.2.8.1 Development Process -- 1.2.9 Trial Setup-to Confirm the Functionalities -- 1.3 Conclusion -- 1.4 Future Work -- References -- 2 Brain-Computer Interface Using Electroencephalographic Signals for the Internet of Robotic Things -- 2.1 Introduction -- 2.2 Electroencephalography Signal Acquisition Methods -- 2.2.1 Invasive Method -- 2.2.2 Non-Invasive Method -- 2.3 Electroencephalography Signal-Based BCI -- 2.3.1 Prefrontal Cortex in Controlling Concentration Strength -- 2.3.2 Neurosky Mind-Wave Mobile -- 2.3.2.1 Electroencephalography Signal Processing Devices -- 2.3.3 Electromyography Signal Extraction of Features and Its Signal Classifications -- 2.4 IoRT-Based Hardware for BCI -- 2.5 Software Setup for IoRT -- 2.6 Results and Discussions -- 2.7 Conclusion -- References -- 3 Automated Verification and Validation of IoRT Systems -- 3.1 Introduction -- 3.1.1 Automating V& -- V-An Important Key to Success -- 3.2 Program Analysis of IoRT Applications -- 3.2.1 Need for Program Analysis -- 3.2.2 Aspects to Consider in Program Analysis of IoRT Systems -- 3.3 Formal Verification of IoRT Systems -- 3.3.1 Automated Model Checking -- 3.3.2 The Model Checking Process -- 3.3.2.1 PRISM -- 3.3.2.2 UPPAAL.
3.3.2.3 SPIN Model Checker -- 3.3.3 Automated Theorem Prover -- 3.3.3.1 ALT-ERGO -- 3.3.4 Static Analysis -- 3.3.4.1 CODESONAR -- 3.4 Validation of IoRT Systems -- 3.4.1 IoRT Testing Methods -- 3.4.2 Design of IoRT Test -- 3.5 Automated Validation -- 3.5.1 Use of Service Visualization -- 3.5.2 Steps for Automated Validation of IoRT Systems -- 3.5.3 Choice of Appropriate Tool for Automated Validation -- 3.5.4 IoRT Systems Open Source Automated Validation Tools -- 3.5.5 Some Significant Open Source Test Automation Frameworks -- 3.5.6 Finally IoRT Security Testing -- 3.5.7 Prevalent Approaches for Security Validation -- 3.5.8 IoRT Security Tools -- References -- 4 Light Fidelity (Li-Fi) Technology: The Future Man-Machine-Machine Interaction Medium -- 4.1 Introduction -- 4.1.1 Need for Li-Fi -- 4.2 Literature Survey -- 4.2.1 An Overview on Man-to-Machine Interaction System -- 4.2.2 Review on Machine to Machine (M2M) Interaction -- 4.2.2.1 System Model -- 4.3 Light Fidelity Technology -- 4.3.1 Modulation Techniques Supporting Li-Fi -- 4.3.1.1 Single Carrier Modulation (SCM) -- 4.3.1.2 Multi Carrier Modulation -- 4.3.1.3 Li-Fi Specific Modulation -- 4.3.2 Components of Li-Fi -- 4.3.2.1 Light Emitting Diode (LED) -- 4.3.2.2 Photodiode -- 4.3.2.3 Transmitter Block -- 4.3.2.4 Receiver Block -- 4.4 Li-Fi Applications in Real Word Scenario -- 4.4.1 Indoor Navigation System for Blind People -- 4.4.2 Vehicle to Vehicle Communication -- 4.4.3 Li-Fi in Hospital -- 4.4.4 Li-Fi Applications for Pharmacies and the Pharmaceutical Industry -- 4.4.5 Li-Fi in Workplace -- 4.5 Conclusion -- References -- 5 Healthcare Management-Predictive Analysis (IoRT) -- 5.1 Introduction -- 5.1.1 Naive Bayes Classifier Prediction for SPAM -- 5.1.2 Internet of Robotic Things (IoRT) -- 5.2 Related Work -- 5.3 Fuzzy Time Interval Sequential Pattern (FTISPAM). 5.3.1 FTI SPAM Using GA Algorithm -- 5.3.1.1 Chromosome Generation -- 5.3.1.2 Fitness Function -- 5.3.1.3 Crossover -- 5.3.1.4 Mutation -- 5.3.1.5 Termination -- 5.3.2 Patterns Matching Using SCI -- 5.3.3 Pattern Classification Based on SCI Value -- 5.3.4 Significant Pattern Evaluation -- 5.4 Detection of Congestive Heart Failure Using Automatic Classifier -- 5.4.1 Analyzing the Dataset -- 5.4.2 Data Collection -- 5.4.2.1 Long-Term HRV Measures -- 5.4.2.2 Attribute Selection -- 5.4.3 Automatic Classifier-Belief Network -- 5.5 Experimental Analysis -- 5.6 Conclusion -- References -- 6 Multimodal Context-Sensitive Human Communication Interaction System Using Artificial Intelligence-Based Human-Centered Computing -- 6.1 Introduction -- 6.2 Literature Survey -- 6.3 Proposed Model -- 6.3.1 Multimodal Data -- 6.3.2 Dimensionality Reduction -- 6.3.3 Principal Component Analysis -- 6.3.4 Reduce the Number of Dimensions -- 6.3.5 CNN -- 6.3.6 CNN Layers -- 6.3.6.1 Convolution Layers -- 6.3.6.2 Padding Layer -- 6.3.6.3 Pooling/Subsampling Layers -- 6.3.6.4 Nonlinear Layers -- 6.3.7 ReLU -- 6.3.7.1 Fully Connected Layers -- 6.3.7.2 Activation Layer -- 6.3.8 LSTM -- 6.3.9 Weighted Combination of Networks -- 6.4 Experimental Results -- 6.4.1 Accuracy -- 6.4.2 Sensibility -- 6.4.3 Specificity -- 6.4.4 A Predictive Positive Value (PPV) -- 6.4.5 Negative Predictive Value (NPV) -- 6.5 Conclusion -- 6.6 Future Scope -- References -- 7 AI, Planning and Control Algorithms for IoRT Systems -- 7.1 Introduction -- 7.2 General Architecture of IoRT -- 7.2.1 Hardware Layer -- 7.2.2 Network Layer -- 7.2.3 Internet Layer -- 7.2.4 Infrastructure Layer -- 7.2.5 Application Layer -- 7.3 Artificial Intelligence in IoRT Systems -- 7.3.1 Technologies of Robotic Things -- 7.3.2 Artificial Intelligence in IoRT -- 7.4 Control Algorithms and Procedures for IoRT Systems. 7.4.1 Adaptation of IoRT Technologies -- 7.4.2 Multi-Robotic Technologies -- 7.5 Application of IoRT in Different Fields -- References -- 8 Enhancements in Communication Protocols That Powered IoRT -- 8.1 Introduction -- 8.2 IoRT Communication Architecture -- 8.2.1 Robots and Things -- 8.2.2 Wireless Link Layer -- 8.2.3 Networking Layer -- 8.2.4 Communication Layer -- 8.2.5 Application Layer -- 8.3 Bridging Robotics and IoT -- 8.4 Robot as a Node in IoT -- 8.4.1 Enhancements in Low Power WPANs -- 8.4.1.1 Enhancements in IEEE 802.15.4 -- 8.4.1.2 Enhancements in Bluetooth -- 8.4.1.3 Network Layer Protocols -- 8.4.2 Enhancements in Low Power WLANs -- 8.4.2.1 Enhancements in IEEE 802.11 -- 8.4.3 Enhancements in Low Power WWANs -- 8.4.3.1 LoRaWAN -- 8.4.3.2 5G -- 8.5 Robots as Edge Device in IoT -- 8.5.1 Constrained RESTful Environments (CoRE) -- 8.5.2 The Constrained Application Protocol (CoAP) -- 8.5.2.1 Latest in CoAP -- 8.5.3 The MQTT-SN Protocol -- 8.5.4 The Data Distribution Service (DDS) -- 8.5.5 Data Formats -- 8.6 Challenges and Research Solutions -- 8.7 Open Platforms for IoRT Applications -- 8.8 Industrial Drive for Interoperability -- 8.8.1 The Zigbee Alliance -- 8.8.2 The Thread Group -- 8.8.3 The WiFi Alliance -- 8.8.4 The LoRa Alliance -- 8.9 Conclusion -- References -- 9 Real Time Hazardous Gas Classification and Management System Using Artificial Neural Networks -- 9.1 Introduction -- 9.2 Existing Methodology -- 9.3 Proposed Methodology -- 9.4 Hardware & -- Software Requirements -- 9.4.1 Hardware Requirements -- 9.4.1.1 Gas Sensors Employed in Hazardous Detection -- 9.4.1.2 NI Wireless Sensor Node 3202 -- 9.4.1.3 NI WSN Gateway (NI 9795) -- 9.4.1.4 COMPACT RIO (NI-9082) -- 9.5 Experimental Setup -- 9.5.1 Data Set Preparation -- 9.5.2 Artificial Neural Network Model Creation -- 9.6 Results and Discussion. 9.7 Conclusion and Future Work -- References -- 10 Hierarchical Elitism GSO Algorithm For Pattern Recognition -- 10.1 Introduction -- 10.2 Related Works -- 10.3 Methodology -- 10.3.1 Additive Kuan Speckle Noise Filtering Model -- 10.3.2 Hierarchical Elitism Gene GSO of MNN in Pattern Recognition -- 10.4 Experimental Setup -- 10.5 Discussion -- 10.5.1 Scenario 1: Computational Time -- 10.5.2 Scenario 2: Computational Complexity -- 10.5.3 Scenario 3: Pattern Recognition Accuracy -- 10.6 Conclusion -- References -- 11 Multidimensional Survey of Machine Learning Application in IoT (Internet of Things) -- 11.1 Machine Learning-An Introduction -- 11.1.1 Classification of Machine Learning -- 11.2 Internet of Things -- 11.3 ML in IoT -- 11.3.1 Overview -- 11.4 Literature Review -- 11.5 Different Machine Learning Algorithm -- 11.5.1 Bayesian Measurements -- 11.5.2 K-Nearest Neighbors (k-NN) -- 11.5.3 Neural Network -- 11.5.4 Decision Tree (DT) -- 11.5.5 Principal Component Analysis (PCA) t -- 11.5.6 K-Mean Calculations -- 11.5.7 Strength Teaching -- 11.6 Internet of Things in Different Frameworks -- 11.6.1 Computing Framework -- 11.6.1.1 Fog Calculation -- 11.6.1.2 Estimation Edge -- 11.6.1.3 Distributed Computing -- 11.6.1.4 Circulated Figuring -- 11.7 Smart Cities -- 11.7.1 Use Case -- 11.7.1.1 Insightful Vitality -- 11.7.1.2 Brilliant Portability -- 11.7.1.3 Urban Arranging -- 11.7.2 Attributes of the Smart City -- 11.8 Smart Transportation -- 11.8.1 Machine Learning and IoT in Smart Transportation -- 11.8.2 Markov Model -- 11.8.3 Decision Structures -- 11.9 Application of Research -- 11.9.1 In Energy -- 11.9.2 In Routing -- 11.9.3 In Living -- 11.9.4 Application in Industry -- 11.10 Machine Learning for IoT Security -- 11.10.1 Used Machine Learning Algorithms -- 11.10.2 Intrusion Detection -- 11.10.3 Authentication -- 11.11 Conclusion -- References. 12 IoT-Based Bias Analysis in Acoustic Feedback Using Time-Variant Adaptive Algorithm in Hearing Aids. |
| Record Nr. | UNINA-9910830153103321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Industrial internet of things (IIoT) : intelligent analytics for predictive maintenance / / edited by R. Anandan [and three others]
| Industrial internet of things (IIoT) : intelligent analytics for predictive maintenance / / edited by R. Anandan [and three others] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Scrivener Publishing, , [2022] |
| Descrizione fisica | 1 online resource (429 pages) |
| Disciplina | 004.678 |
| Collana | Advances in Learning Analytics for Intelligent Cloud-IoT Systems |
| Soggetto topico | Internet of things |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-76901-9
1-119-76902-7 1-119-76900-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 A Look at IIoT: The Perspective of IoT Technology Applied in the Industrial Field -- 1.1 Introduction -- 1.2 Relationship Between Artificial Intelligence and IoT -- 1.2.1 AI Concept -- 1.2.2 IoT Concept -- 1.3 IoT Ecosystem -- 1.3.1 Industry 4.0 Concept -- 1.3.2 Industrial Internet of Things -- 1.4 Discussion -- 1.5 Trends -- 1.6 Conclusions -- References -- 2 Analysis on Security in IoT Devices- An Overview -- 2.1 Introduction -- 2.2 Security Properties -- 2.3 Security Challenges of IoT -- 2.3.1 Classification of Security Levels -- 2.3.1.1 At Information Level -- 2.3.1.2 At Access Level -- 2.3.1.3 At Functional Level -- 2.3.2 Classification of IoT Layered Architecture -- 2.3.2.1 Edge Layer -- 2.3.2.2 Access Layer -- 2.3.2.3 Application Layer -- 2.4 IoT Security Threats -- 2.4.1 Physical Device Threats -- 2.4.1.1 Device-Threats -- 2.4.1.2 Resource Led Constraints -- 2.4.2 Network-Oriented Communication Assaults -- 2.4.2.1 Structure -- 2.4.2.2 Protocol -- 2.4.3 Data-Based Threats -- 2.4.3.1 Confidentiality -- 2.4.3.2 Availability -- 2.4.3.3 Integrity -- 2.5 Assaults in IoT Devices -- 2.5.1 Devices of IoT -- 2.5.2 Gateways and Networking Devices -- 2.5.3 Cloud Servers and Control Devices -- 2.6 Security Analysis of IoT Platforms -- 2.6.1 ARTIK -- 2.6.2 GiGA IoT Makers -- 2.6.3 AWS IoT -- 2.6.4 Azure IoT -- 2.6.5 Google Cloud IoT (GC IoT) -- 2.7 Future Research Approaches -- 2.7.1 Blockchain Technology -- 2.7.2 5G Technology -- 2.7.3 Fog Computing (FC) and Edge Computing (EC) -- References -- 3 Smart Automation, Smart Energy, and Grid Management Challenges -- 3.1 Introduction -- 3.2 Internet of Things and Smart Grids -- 3.2.1 Smart Grid in IoT -- 3.2.2 IoT Application -- 3.2.3 Trials and Imminent Investigation Guidelines.
3.3 Conceptual Model of Smart Grid -- 3.4 Building Computerization -- 3.4.1 Smart Lighting -- 3.4.2 Smart Parking -- 3.4.3 Smart Buildings -- 3.4.4 Smart Grid -- 3.4.5 Integration IoT in SG -- 3.5 Challenges and Solutions -- 3.6 Conclusions -- References -- 4 Industrial Automation (IIoT) 4.0: An Insight Into Safety Management -- 4.1 Introduction -- 4.1.1 Fundamental Terms in IIoT -- 4.1.1.1 Cloud Computing -- 4.1.1.2 Big Data Analytics -- 4.1.1.3 Fog/Edge Computing -- 4.1.1.4 Internet of Things -- 4.1.1.5 Cyber-Physical-System -- 4.1.1.6 Artificial Intelligence -- 4.1.1.7 Machine Learning -- 4.1.1.8 Machine-to-Machine Communication -- 4.1.2 Intelligent Analytics -- 4.1.3 Predictive Maintenance -- 4.1.4 Disaster Predication and Safety Management -- 4.1.4.1 Natural Disasters -- 4.1.4.2 Disaster Lifecycle -- 4.1.4.3 Disaster Predication -- 4.1.4.4 Safety Management -- 4.1.5 Optimization -- 4.2 Existing Technology and Its Review -- 4.2.1 Survey on Predictive Analysis in Natural Disasters -- 4.2.2 Survey on Safety Management and Recovery -- 4.2.3 Survey on Optimizing Solutions in Natural Disasters -- 4.3 Research Limitation -- 4.3.1 Forward-Looking Strategic Vision (FVS) -- 4.3.2 Availability of Data -- 4.3.3 Load Balancing -- 4.3.4 Energy Saving and Optimization -- 4.3.5 Cost Benefit Analysis -- 4.3.6 Misguidance of Analysis -- 4.4 Finding -- 4.4.1 Data Driven Reasoning -- 4.4.2 Cognitive Ability -- 4.4.3 Edge Intelligence -- 4.4.4 Effect of ML Algorithms and Optimization -- 4.4.5 Security -- 4.5 Conclusion and Future Research -- 4.5.1 Conclusion -- 4.5.2 Future Research -- References -- 5 An Industrial Perspective on Restructured Power Systems Using Soft Computing Techniques -- 5.1 Introduction -- 5.2 Fuzzy Logic -- 5.2.1 Fuzzy Sets -- 5.2.2 Fuzzy Logic Basics -- 5.2.3 Fuzzy Logic and Power System -- 5.2.4 Fuzzy Logic-Automatic Generation Control. 5.2.5 Fuzzy Microgrid Wind -- 5.3 Genetic Algorithm -- 5.3.1 Important Aspects of Genetic Algorithm -- 5.3.2 Standard Genetic Algorithm -- 5.3.3 Genetic Algorithm and Its Application -- 5.3.4 Power System and Genetic Algorithm -- 5.3.5 Economic Dispatch Using Genetic Algorithm -- 5.4 Artificial Neural Network -- 5.4.1 The Biological Neuron -- 5.4.2 A Formal Definition of Neural Network -- 5.4.3 Neural Network Models -- 5.4.4 Rosenblatt's Perceptron -- 5.4.5 Feedforward and Recurrent Networks -- 5.4.6 Back Propagation Algorithm -- 5.4.7 Forward Propagation -- 5.4.8 Algorithm -- 5.4.9 Recurrent Network -- 5.4.10 Examples of Neural Networks -- 5.4.10.1 AND Operation -- 5.4.10.2 OR Operation -- 5.4.10.3 XOR Operation -- 5.4.11 Key Components of an Artificial Neuron Network -- 5.4.12 Neural Network Training -- 5.4.13 Training Types -- 5.4.13.1 Supervised Training -- 5.4.13.2 Unsupervised Training -- 5.4.14 Learning Rates -- 5.4.15 Learning Laws -- 5.4.16 Restructured Power System -- 5.4.17 Advantages of Precise Forecasting of the Price -- 5.5 Conclusion -- References -- 6 Recent Advances in Wearable Antennas: A Survey -- 6.1 Introduction -- 6.2 Types of Antennas -- 6.2.1 Description of Wearable Antennas -- 6.2.1.1 Microstrip Patch Antenna -- 6.2.1.2 Substrate Integrated Waveguide Antenna -- 6.2.1.3 Planar Inverted-F Antenna -- 6.2.1.4 Monopole Antenna -- 6.2.1.5 Metasurface Loaded Antenna -- 6.3 Design of Wearable Antennas -- 6.3.1 Effect of Substrate and Ground Geometries on Antenna Design -- 6.3.1.1 Conducting Coating on Substrate -- 6.3.1.2 Ground Plane With Spiral Metamaterial Meandered Structure -- 6.3.1.3 Partial Ground Plane -- 6.3.2 Logo Antennas -- 6.3.3 Embroidered Antenna -- 6.3.4 Wearable Antenna Based on Electromagnetic Band Gap -- 6.3.5 Wearable Reconfigurable Antenna -- 6.4 Textile Antennas -- 6.5 Comparison of Wearable Antenna Designs. 6.6 Fractal Antennas -- 6.6.1 Minkowski Fractal Geometries Using Wearable Electro-Textile Antennas -- 6.6.2 Antenna Design With Defected Semi-Elliptical Ground Plane -- 6.6.3 Double-Fractal Layer Wearable Antenna -- 6.6.4 Development of Embroidered Sierpinski Carpet Antenna -- 6.7 Future Challenges of Wearable Antenna Designs -- 6.8 Conclusion -- References -- 7 An Overview of IoT and Its Application With Machine Learning in Data Center -- 7.1 Introduction -- 7.1.1 6LoWPAN -- 7.1.2 Data Protocols -- 7.1.2.1 CoAP -- 7.1.2.2 MQTT -- 7.1.2.3 Rest APIs -- 7.1.3 IoT Components -- 7.1.3.1 Hardware -- 7.1.3.2 Middleware -- 7.1.3.3 Visualization -- 7.2 Data Center and Internet of Things -- 7.2.1 Modern Data Centers -- 7.2.2 Data Storage -- 7.2.3 Computing Process -- 7.2.3.1 Fog Computing -- 7.2.3.2 Edge Computing -- 7.2.3.3 Cloud Computing -- 7.2.3.4 Distributed Computing -- 7.2.3.5 Comparison of Cloud Computing and Fog Computing -- 7.3 Machine Learning Models and IoT -- 7.3.1 Classifications of Machine Learning Supported in IoT -- 7.3.1.1 Supervised Learning -- 7.3.1.2 Unsupervised Learning -- 7.3.1.3 Reinforcement Learning -- 7.3.1.4 Ensemble Learning -- 7.3.1.5 Neural Network -- 7.4 Challenges in Data Center and IoT -- 7.4.1 Major Challenges -- 7.5 Conclusion -- References -- 8 Impact of IoT to Meet Challenges in Drone Delivery System -- 8.1 Introduction -- 8.1.1 IoT Components -- 8.1.2 Main Division to Apply IoT in Aviation -- 8.1.3 Required Field of IoT in Aviation -- 8.2 Literature Survey -- 8.3 Smart Airport Architecture -- 8.4 Barriers to IoT Implementation -- 8.4.1 How is the Internet of Things Converting the Aviation Enterprise? -- 8.5 Current Technologies in Aviation Industry -- 8.5.1 Methodology or Research Design -- 8.6 IoT Adoption Challenges -- 8.6.1 Deployment of IoT Applications on Broad Scale Includes the Underlying Challenges. 8.7 Transforming Airline Industry With Internet of Things -- 8.7.1 How the IoT Is Improving the Aviation Industry -- 8.7.2 Applications of AI in the Aviation Industry -- 8.8 Revolution of Change (Paradigm Shift) -- 8.9 The Following Diagram Shows the Design of the Application -- 8.10 Discussion, Limitations, Future Research, and Conclusion -- 8.10.1 Growth of Aviation IoT Industry -- 8.10.2 IoT Applications-Benefits -- 8.10.3 Operational Efficiency -- 8.10.4 Strategic Differentiation -- 8.10.5 New Revenue -- 8.11 Present and Future Scopes -- 8.11.1 Improving Passenger Experience -- 8.11.2 Safety -- 8.11.3 Management of Goods and Luggage -- 8.11.4 Saving -- 8.12 Conclusion -- References -- 9 IoT-Based Water Management System for a Healthy Life -- 9.1 Introduction -- 9.1.1 Human Activities as a Source of Pollutants -- 9.2 Water Management Using IoT -- 9.2.1 Water Quality Management Based on IoT Framework -- 9.3 IoT Characteristics and Measurement Parameters -- 9.4 Platforms and Configurations -- 9.5 Water Quality Measuring Sensors and Data Analysis -- 9.6 Wastewater and Storm Water Monitoring Using IoT -- 9.6.1 System Initialization -- 9.6.2 Capture and Storage of Information -- 9.6.3 Information Modeling -- 9.6.4 Visualization and Management of the Information -- 9.7 Sensing and Sampling of Water Treatment Using IoT -- References -- 10 Fuel Cost Optimization Using IoT in Air Travel -- 10.1 Introduction -- 10.1.1 Introduction to IoT -- 10.1.2 Processing IoT Data -- 10.1.3 Advantages of IoT -- 10.1.4 Disadvantages of IoT -- 10.1.5 IoT Standards -- 10.1.6 Lite Operating System (Lite OS) -- 10.1.7 Low Range Wide Area Network (LoRaWAN) -- 10.2 Emerging Frameworks in IoT -- 10.2.1 Amazon Web Service (AWS) -- 10.2.2 Azure -- 10.2.3 Brillo/Weave Statement -- 10.2.4 Calvin -- 10.3 Applications of IoT -- 10.3.1 Healthcare in IoT. 10.3.2 Smart Construction and Smart Vehicles. |
| Record Nr. | UNINA-9910555295903321 |
| Hoboken, New Jersey : , : Scrivener Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Industrial internet of things (IIoT) : intelligent analytics for predictive maintenance / / edited by R. Anandan [and three others]
| Industrial internet of things (IIoT) : intelligent analytics for predictive maintenance / / edited by R. Anandan [and three others] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Scrivener Publishing, , 2022 |
| Descrizione fisica | xx, 402 pages : illustrations; ; 24 cm |
| Collana | Advances in Learning Analytics for Intelligent Cloud-IoT Systems |
| Soggetto topico | Internet of things - Industrial applications |
| ISBN | 9781119768777 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 A Look at IIoT: The Perspective of IoT Technology Applied in the Industrial Field -- 1.1 Introduction -- 1.2 Relationship Between Artificial Intelligence and IoT -- 1.2.1 AI Concept -- 1.2.2 IoT Concept -- 1.3 IoT Ecosystem -- 1.3.1 Industry 4.0 Concept -- 1.3.2 Industrial Internet of Things -- 1.4 Discussion -- 1.5 Trends -- 1.6 Conclusions -- References -- 2 Analysis on Security in IoT Devices- An Overview -- 2.1 Introduction -- 2.2 Security Properties -- 2.3 Security Challenges of IoT -- 2.3.1 Classification of Security Levels -- 2.3.1.1 At Information Level -- 2.3.1.2 At Access Level -- 2.3.1.3 At Functional Level -- 2.3.2 Classification of IoT Layered Architecture -- 2.3.2.1 Edge Layer -- 2.3.2.2 Access Layer -- 2.3.2.3 Application Layer -- 2.4 IoT Security Threats -- 2.4.1 Physical Device Threats -- 2.4.1.1 Device-Threats -- 2.4.1.2 Resource Led Constraints -- 2.4.2 Network-Oriented Communication Assaults -- 2.4.2.1 Structure -- 2.4.2.2 Protocol -- 2.4.3 Data-Based Threats -- 2.4.3.1 Confidentiality -- 2.4.3.2 Availability -- 2.4.3.3 Integrity -- 2.5 Assaults in IoT Devices -- 2.5.1 Devices of IoT -- 2.5.2 Gateways and Networking Devices -- 2.5.3 Cloud Servers and Control Devices -- 2.6 Security Analysis of IoT Platforms -- 2.6.1 ARTIK -- 2.6.2 GiGA IoT Makers -- 2.6.3 AWS IoT -- 2.6.4 Azure IoT -- 2.6.5 Google Cloud IoT (GC IoT) -- 2.7 Future Research Approaches -- 2.7.1 Blockchain Technology -- 2.7.2 5G Technology -- 2.7.3 Fog Computing (FC) and Edge Computing (EC) -- References -- 3 Smart Automation, Smart Energy, and Grid Management Challenges -- 3.1 Introduction -- 3.2 Internet of Things and Smart Grids -- 3.2.1 Smart Grid in IoT -- 3.2.2 IoT Application -- 3.2.3 Trials and Imminent Investigation Guidelines.
3.3 Conceptual Model of Smart Grid -- 3.4 Building Computerization -- 3.4.1 Smart Lighting -- 3.4.2 Smart Parking -- 3.4.3 Smart Buildings -- 3.4.4 Smart Grid -- 3.4.5 Integration IoT in SG -- 3.5 Challenges and Solutions -- 3.6 Conclusions -- References -- 4 Industrial Automation (IIoT) 4.0: An Insight Into Safety Management -- 4.1 Introduction -- 4.1.1 Fundamental Terms in IIoT -- 4.1.1.1 Cloud Computing -- 4.1.1.2 Big Data Analytics -- 4.1.1.3 Fog/Edge Computing -- 4.1.1.4 Internet of Things -- 4.1.1.5 Cyber-Physical-System -- 4.1.1.6 Artificial Intelligence -- 4.1.1.7 Machine Learning -- 4.1.1.8 Machine-to-Machine Communication -- 4.1.2 Intelligent Analytics -- 4.1.3 Predictive Maintenance -- 4.1.4 Disaster Predication and Safety Management -- 4.1.4.1 Natural Disasters -- 4.1.4.2 Disaster Lifecycle -- 4.1.4.3 Disaster Predication -- 4.1.4.4 Safety Management -- 4.1.5 Optimization -- 4.2 Existing Technology and Its Review -- 4.2.1 Survey on Predictive Analysis in Natural Disasters -- 4.2.2 Survey on Safety Management and Recovery -- 4.2.3 Survey on Optimizing Solutions in Natural Disasters -- 4.3 Research Limitation -- 4.3.1 Forward-Looking Strategic Vision (FVS) -- 4.3.2 Availability of Data -- 4.3.3 Load Balancing -- 4.3.4 Energy Saving and Optimization -- 4.3.5 Cost Benefit Analysis -- 4.3.6 Misguidance of Analysis -- 4.4 Finding -- 4.4.1 Data Driven Reasoning -- 4.4.2 Cognitive Ability -- 4.4.3 Edge Intelligence -- 4.4.4 Effect of ML Algorithms and Optimization -- 4.4.5 Security -- 4.5 Conclusion and Future Research -- 4.5.1 Conclusion -- 4.5.2 Future Research -- References -- 5 An Industrial Perspective on Restructured Power Systems Using Soft Computing Techniques -- 5.1 Introduction -- 5.2 Fuzzy Logic -- 5.2.1 Fuzzy Sets -- 5.2.2 Fuzzy Logic Basics -- 5.2.3 Fuzzy Logic and Power System -- 5.2.4 Fuzzy Logic-Automatic Generation Control. 5.2.5 Fuzzy Microgrid Wind -- 5.3 Genetic Algorithm -- 5.3.1 Important Aspects of Genetic Algorithm -- 5.3.2 Standard Genetic Algorithm -- 5.3.3 Genetic Algorithm and Its Application -- 5.3.4 Power System and Genetic Algorithm -- 5.3.5 Economic Dispatch Using Genetic Algorithm -- 5.4 Artificial Neural Network -- 5.4.1 The Biological Neuron -- 5.4.2 A Formal Definition of Neural Network -- 5.4.3 Neural Network Models -- 5.4.4 Rosenblatt's Perceptron -- 5.4.5 Feedforward and Recurrent Networks -- 5.4.6 Back Propagation Algorithm -- 5.4.7 Forward Propagation -- 5.4.8 Algorithm -- 5.4.9 Recurrent Network -- 5.4.10 Examples of Neural Networks -- 5.4.10.1 AND Operation -- 5.4.10.2 OR Operation -- 5.4.10.3 XOR Operation -- 5.4.11 Key Components of an Artificial Neuron Network -- 5.4.12 Neural Network Training -- 5.4.13 Training Types -- 5.4.13.1 Supervised Training -- 5.4.13.2 Unsupervised Training -- 5.4.14 Learning Rates -- 5.4.15 Learning Laws -- 5.4.16 Restructured Power System -- 5.4.17 Advantages of Precise Forecasting of the Price -- 5.5 Conclusion -- References -- 6 Recent Advances in Wearable Antennas: A Survey -- 6.1 Introduction -- 6.2 Types of Antennas -- 6.2.1 Description of Wearable Antennas -- 6.2.1.1 Microstrip Patch Antenna -- 6.2.1.2 Substrate Integrated Waveguide Antenna -- 6.2.1.3 Planar Inverted-F Antenna -- 6.2.1.4 Monopole Antenna -- 6.2.1.5 Metasurface Loaded Antenna -- 6.3 Design of Wearable Antennas -- 6.3.1 Effect of Substrate and Ground Geometries on Antenna Design -- 6.3.1.1 Conducting Coating on Substrate -- 6.3.1.2 Ground Plane With Spiral Metamaterial Meandered Structure -- 6.3.1.3 Partial Ground Plane -- 6.3.2 Logo Antennas -- 6.3.3 Embroidered Antenna -- 6.3.4 Wearable Antenna Based on Electromagnetic Band Gap -- 6.3.5 Wearable Reconfigurable Antenna -- 6.4 Textile Antennas -- 6.5 Comparison of Wearable Antenna Designs. 6.6 Fractal Antennas -- 6.6.1 Minkowski Fractal Geometries Using Wearable Electro-Textile Antennas -- 6.6.2 Antenna Design With Defected Semi-Elliptical Ground Plane -- 6.6.3 Double-Fractal Layer Wearable Antenna -- 6.6.4 Development of Embroidered Sierpinski Carpet Antenna -- 6.7 Future Challenges of Wearable Antenna Designs -- 6.8 Conclusion -- References -- 7 An Overview of IoT and Its Application With Machine Learning in Data Center -- 7.1 Introduction -- 7.1.1 6LoWPAN -- 7.1.2 Data Protocols -- 7.1.2.1 CoAP -- 7.1.2.2 MQTT -- 7.1.2.3 Rest APIs -- 7.1.3 IoT Components -- 7.1.3.1 Hardware -- 7.1.3.2 Middleware -- 7.1.3.3 Visualization -- 7.2 Data Center and Internet of Things -- 7.2.1 Modern Data Centers -- 7.2.2 Data Storage -- 7.2.3 Computing Process -- 7.2.3.1 Fog Computing -- 7.2.3.2 Edge Computing -- 7.2.3.3 Cloud Computing -- 7.2.3.4 Distributed Computing -- 7.2.3.5 Comparison of Cloud Computing and Fog Computing -- 7.3 Machine Learning Models and IoT -- 7.3.1 Classifications of Machine Learning Supported in IoT -- 7.3.1.1 Supervised Learning -- 7.3.1.2 Unsupervised Learning -- 7.3.1.3 Reinforcement Learning -- 7.3.1.4 Ensemble Learning -- 7.3.1.5 Neural Network -- 7.4 Challenges in Data Center and IoT -- 7.4.1 Major Challenges -- 7.5 Conclusion -- References -- 8 Impact of IoT to Meet Challenges in Drone Delivery System -- 8.1 Introduction -- 8.1.1 IoT Components -- 8.1.2 Main Division to Apply IoT in Aviation -- 8.1.3 Required Field of IoT in Aviation -- 8.2 Literature Survey -- 8.3 Smart Airport Architecture -- 8.4 Barriers to IoT Implementation -- 8.4.1 How is the Internet of Things Converting the Aviation Enterprise? -- 8.5 Current Technologies in Aviation Industry -- 8.5.1 Methodology or Research Design -- 8.6 IoT Adoption Challenges -- 8.6.1 Deployment of IoT Applications on Broad Scale Includes the Underlying Challenges. 8.7 Transforming Airline Industry With Internet of Things -- 8.7.1 How the IoT Is Improving the Aviation Industry -- 8.7.2 Applications of AI in the Aviation Industry -- 8.8 Revolution of Change (Paradigm Shift) -- 8.9 The Following Diagram Shows the Design of the Application -- 8.10 Discussion, Limitations, Future Research, and Conclusion -- 8.10.1 Growth of Aviation IoT Industry -- 8.10.2 IoT Applications-Benefits -- 8.10.3 Operational Efficiency -- 8.10.4 Strategic Differentiation -- 8.10.5 New Revenue -- 8.11 Present and Future Scopes -- 8.11.1 Improving Passenger Experience -- 8.11.2 Safety -- 8.11.3 Management of Goods and Luggage -- 8.11.4 Saving -- 8.12 Conclusion -- References -- 9 IoT-Based Water Management System for a Healthy Life -- 9.1 Introduction -- 9.1.1 Human Activities as a Source of Pollutants -- 9.2 Water Management Using IoT -- 9.2.1 Water Quality Management Based on IoT Framework -- 9.3 IoT Characteristics and Measurement Parameters -- 9.4 Platforms and Configurations -- 9.5 Water Quality Measuring Sensors and Data Analysis -- 9.6 Wastewater and Storm Water Monitoring Using IoT -- 9.6.1 System Initialization -- 9.6.2 Capture and Storage of Information -- 9.6.3 Information Modeling -- 9.6.4 Visualization and Management of the Information -- 9.7 Sensing and Sampling of Water Treatment Using IoT -- References -- 10 Fuel Cost Optimization Using IoT in Air Travel -- 10.1 Introduction -- 10.1.1 Introduction to IoT -- 10.1.2 Processing IoT Data -- 10.1.3 Advantages of IoT -- 10.1.4 Disadvantages of IoT -- 10.1.5 IoT Standards -- 10.1.6 Lite Operating System (Lite OS) -- 10.1.7 Low Range Wide Area Network (LoRaWAN) -- 10.2 Emerging Frameworks in IoT -- 10.2.1 Amazon Web Service (AWS) -- 10.2.2 Azure -- 10.2.3 Brillo/Weave Statement -- 10.2.4 Calvin -- 10.3 Applications of IoT -- 10.3.1 Healthcare in IoT. 10.3.2 Smart Construction and Smart Vehicles. |
| Record Nr. | UNINA-9910677138203321 |
| Hoboken, New Jersey : , : Scrivener Publishing, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations / / edited by Sam Goundar, R. Anandan
| Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations / / edited by Sam Goundar, R. Anandan |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (336 pages) |
| Disciplina | 658.4038028563 |
| Collana | EAI/Springer Innovations in Communication and Computing |
| Soggetto topico |
Telecommunication
Cooperating objects (Computer systems) Artificial intelligence Communications Engineering, Networks Cyber-Physical Systems Artificial Intelligence |
| ISBN | 3-031-35751-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Application Areas, Benefits and Research Challenges of Converging Blockchain and Machine Learning Techniques -- Chapter 2. Internet of Things and Blockchain in Healthcare: Challenges and Solutions -- Chapter 3. A Conceptual Model for the Role of Blockchain in Overcoming Supply Chain Challenges -- Chapter 4. A Hybrid application of Quantum Computing Methodologies to AI Techniques for Paddy Crop Leaf Disease Identification -- Chapter 5. Cognitive Computing for the Internet of Medical Things -- Chapter 6. Blockchain-based Privacy-preserving electronics healthcare records in healthcare 4.0 using Proxy Re-Encryption -- Chapter 7. A Framework for low energy application devices using Blockchain-Enabled IoT in WSNs -- Chapter 8. Implementation of Real-Time Water Quality Monitoring Based on Java and Internet of Things -- Chapter 9. Blockchain-Based Infrastructure for Precision Agriculture -- Chapter 10. Implementation of a Distributed Electronic Voting System Using a Blockchain-Based Framework -- Chapter 11. Blockchain based organ donation and transplant matching system -- Chapter 12. A transparent, distributed, and secure platform for crowd funding based on blockchain technology -- Chapter 13. Certificate Authentication System Using Blockchain -- Chapter 14. Blockchain Based Decentralized Student Verification Platform -- Chapter 15. Application of Internet of Things systems for Aerosol monitoring of quarries in Morocco -- Chapter 16. Blockchain Networks for Cybersecurity Using Machine Learning Algorithms -- Chapter 17. Block Chain of Crypto-Currency Using PoW based Consensus Algorithm with SHA – 256 Hash Algorithm for Making Secured Payments -- Chapter 18. An Efficient Security Enabled Routing Protocol for Data Transmission in VANET Using blockchain Ripple protocol consensus algorithm -- Chapter 19. Blockchain-based Sinkhole Attack Detection in Wireless Sensor Network -- Chapter 20. Secured Smart Manufacturing Systems Using Blockchain Technology For Industry 4.0 -- Chapter 21. Kryptoverse – A Fully-Fledged Cryptocurrency Transfer Website Based On Web 3.0 -- Chapter 22. The Benefits of Combining AI & Blockchain in Enhancing Decision Making in Banking Industry. |
| Record Nr. | UNINA-9910760251503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||