Industrial internet of things (IIoT) : intelligent analytics for predictive maintenance / / edited by R. Anandan [and three others]

• Pubblicazione: Hoboken, New Jersey : , : Scrivener Publishing, , 2022; ©2022
• Descrizione fisica: xx, 402 pages : illustrations; ; 24 cm
• Soggetto topico: Internet of things - Industrial applications
• Persona (resp. second.): AnandanR; SuseendranG; PalSouvik; ZamanNoor <1972->
• 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.
• Sommario/riassunto: INDUSTRIAL INTERNET OF THINGS (IIOT) This book discusses how the industrial internet will be augmented through increased network agility, integrated artificial intelligence (AI) and the capacity to deploy, automate, orchestrate, and secure diverse user cases at hyperscale. Since the internet of things (IoT) dominates all sectors of technology, from home to industry, automation through IoT devices is changing the processes of our daily lives. For example, more and more businesses are adopting and accepting industrial automation on a large scale, with the market for industrial robots expected to reach $73.5 billion in 2023. The primary reason for adopting IoT industrial automation in businesses is the benefits it provides, including enhanced efficiency, high accuracy, cost-effectiveness, quick process completion, low power consumption, fewer errors, and ease of control. The 15 chapters in the book showcase industrial automation through the IoT by including case studies in the areas of the IIoT, robotic and intelligent systems, and web-based applications which will be of interest to working professionals and those in education and research involved in a broad cross-section of technical disciplines. The volume will help industry leaders by Advancing hands-on experience working with industrial architecture Demonstrating the potential of cloud-based Industrial IoT platforms, analytics, and protocols Putting forward business models revitalizing the workforce with Industry 4.0. Audience Researchers and scholars in industrial engineering and manufacturing, artificial intelligence, cyber-physical systems, robotics, safety engineering, safety-critical systems, and application domain communities such as aerospace, agriculture, automotive, critical infrastructures, healthcare, manufacturing, retail, smart transports, smart cities, and smart healthcare.
• Titolo autorizzato: Industrial internet of things (IIoT)
• ISBN: 9781119768777
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910677138203321
• Lo trovi qui: Univ. Federico II
• Serie: Advances in learning analytics for intelligent cloud-IoT systems.