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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 | ||
| ||
Intelligent Computing and Innovation on Data Science : Proceedings of ICTIDS 2019 / / edited by Sheng-Lung Peng, Le Hoang Son, G. Suseendran, D. Balaganesh
| Intelligent Computing and Innovation on Data Science : Proceedings of ICTIDS 2019 / / edited by Sheng-Lung Peng, Le Hoang Son, G. Suseendran, D. Balaganesh |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (795 pages) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Computational intelligence
Artificial intelligence Big data Computer security Computational Intelligence Artificial Intelligence Big Data Systems and Data Security |
| ISBN | 981-15-3284-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Comprehensive Guide to Implementation of Data Warehouse in Education -- Computational Biology Tool Towards Studying the Interaction Between Azadirachtin Plant Compound with Cervical Cancer Proteins -- Intelligent Agent Based Organization For Studying the Big Five Personality Traits -- Automatic Pruning of Rules Through Multi-Objective Optimization – A Case Study With a Multi-Objective Cultural Algorithm -- Knowledge Genesis And Dissemination: Impact On Performance In Information Technology Services -- Artificial Intelligence Based Load Balancing In Cloud Computing Environment: A Study -- Implementation of Statistical Data Analytics in Data Science Life Cycle -- A Big Data Analytics-Based Design for Viable Evolution of Retail Sector -- Document Content Analysis Based on Random Forest Algorithm -- Sensing The Prostatectomy in Neuroendocrine Metastatic Active Surveillance in Data Mining Techniques -- Work Load Forecasting Based On Big Data Characteristics In Cloud Systems -- Phrase Extraction Using Pattern Based Bootstrapping Approach -- IOT Based Trash Collection Bin Using Arduino. |
| Record Nr. | UNINA-9910484009103321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||