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Emerging extended reality technologies for industry 4.0 : early experiences with conception, design, implementation, evaluation and deployment / / edited by Jolanda G. Tromp, Dac-Nhuong Le, Chung Van Le
| Emerging extended reality technologies for industry 4.0 : early experiences with conception, design, implementation, evaluation and deployment / / edited by Jolanda G. Tromp, Dac-Nhuong Le, Chung Van Le |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2020 |
| Descrizione fisica | 1 online resource (270 pages) |
| Disciplina | 006.8 |
| Soggetto topico |
Mixed reality - Industrial applications
Internet of things - Industrial applications Computer simulation - Industrial applications |
| ISBN |
1-119-65473-4
1-119-65467-X 1-119-65469-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910555242203321 |
| Hoboken, New Jersey : , : Wiley, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Emerging extended reality technologies for industry 4.0 : early experiences with conception, design, implementation, evaluation and deployment / / edited by Jolanda G. Tromp, Dac-Nhuong Le, Chung Van Le
| Emerging extended reality technologies for industry 4.0 : early experiences with conception, design, implementation, evaluation and deployment / / edited by Jolanda G. Tromp, Dac-Nhuong Le, Chung Van Le |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2020 |
| Descrizione fisica | 1 online resource (270 pages) |
| Disciplina | 006.8 |
| Soggetto topico |
Mixed reality - Industrial applications
Internet of things - Industrial applications Computer simulation - Industrial applications |
| ISBN |
1-119-65473-4
1-119-65467-X 1-119-65469-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Mixed reality use in higher education : results from an international survey / J. Riman, N. Winters, J. Zelenak, I. Yucel, J.G. Tromp -- Applying 3D VR technology for human body simulation to teaching, learning and studying / Le Van Chung, Gia Nhu Nguyen, Tung Sanh Nguyen, Tri Huu Nguyen, Dac-Nhuong Le -- A safety tracking and sensor system for school buses in Saudi Arabia / Samah Abbas, Hajar Mohammed, Laila Almalki Maryam Hassan, Maram Meccawy -- A lightweight encryption algorithm applied to a quantized speech image for secure IoT / Mourad Talbi -- The impact of social media adoption on entrepreneurial ecosystem / Bodor Almotairy, Manal Abdullah, Rabeeh Abbasi -- Human factors for e-health training system : UX testing for XR anatomy training app / Zhushun Timothy Cai, Oliver Medonza, Kristen Ray, Chung Van Le, Damian Schofield, Jolanda Tromp -- Augmented reality at heritage sites : technological advances and embodied spatially minded interactions / Lesley Johnston, Romy Galloway, Jordan John Trench, Matthieu Poyade, Jolanda Tromp, Hoang Thi My -- TELECI architecture for machine learning algorithms integration in an existing LMS / V. Zagorskis, A. Gorbunovs, A. Kapenieks -- Enterprise innovation management in industry 4.0 : modeling aspects / V. Babenko -- Using simulation for development of automobile gas diesel engine systems and their operational control / Mikhail G. Shatrov, Vladimir V. Sinyavski, Andrey Yu. Dunin, Ivan G. Shishlov, Sergei D. Skorodelov, Andrey L. Yakovenko -- Classification of concept drift in evolving data stream / Mashail Althabiti and Manal Abdullah -- Dynamical mass transfer systems in Buslaev contour networks with conflicts / Marina Yashina, Alexander Tatashev, Ivan Kuteynikov -- Parallel simulation and visualization of traffic flows using cellular automata theory and QuasigasDynamic approach / Antonina Chechina, Natalia Churbanova, Pavel Sokolov, Marina Trapeznikova, Mikhail German, Alexey Ermakov, Obidzhon Bozorov. |
| Record Nr. | UNINA-9910829979703321 |
| Hoboken, New Jersey : , : Wiley, , 2020 | ||
| 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 | ||
| ||
Integration of mechanical and manufacturing engineering with IoT : a digital transformation / / edited by R. Rajasekar, C. Moganapriya, M.Harikrishna Kumar, P. Sathish Kumar
| Integration of mechanical and manufacturing engineering with IoT : a digital transformation / / edited by R. Rajasekar, C. Moganapriya, M.Harikrishna Kumar, P. Sathish Kumar |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , [2023] |
| Descrizione fisica | 1 online resource (342 pages) |
| Disciplina | 620.00285 |
| Soggetto topico |
Internet of things - Industrial applications
Engineering - Data processing Manufacturing processes Mechanical engineering Production engineering |
| ISBN |
9781119865001
1-119-86539-5 1-119-86538-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Evolution of Internet of Things (IoT): Past, Present and Future for Manufacturing Systems -- 1.1 Introduction -- 1.2 IoT Revolution -- 1.3 IoT -- 1.4 Fundamental Technologies -- 1.4.1 RFID and NFC -- 1.4.2 WSN -- 1.4.3 Data Storage and Analytics (DSA) -- 1.5 IoT Architecture -- 1.6 Cloud Computing (CC) and IoT -- 1.6.1 Service of CC -- 1.6.2 Integration of IoT With CC -- 1.7 Edge Computing (EC) and IoT -- 1.7.1 EC with IoT Architecture -- 1.8 Applications of IoT -- 1.8.1 Smart Mobility -- 1.8.2 Smart Grid -- 1.8.3 Smart Home System -- 1.8.4 Public Safety and Environment Monitoring -- 1.8.5 Smart Healthcare Systems -- 1.8.6 Smart Agriculture System -- 1.9 Industry 4.0 Integrated With IoT Architecture for Incorporation of Designing and Enhanced Production Systems -- 1.9.1 Five-Stage Process of IoT for Design and Manufacturing System -- 1.9.2 IoT Architecture for Advanced Manufacturing Technologies -- 1.9.3 Architecture Development -- 1.10 Current Issues and Challenges in IoT -- 1.10.1 Scalability -- 1.10.2 Issue of Trust -- 1.10.3 Service Availability -- 1.10.4 Security Challenges -- 1.10.5 Mobility Issues -- 1.10.6 Architecture for IoT -- 1.11 Conclusion -- References -- Chapter 2 Fourth Industrial Revolution: Industry 4.0 -- 2.1 Introduction -- 2.1.1 Global Level Adaption -- 2.2 Evolution of Industry -- 2.2.1 Industry 1.0 -- 2.2.2 Industry 2.0 -- 2.2.3 Industry 3.0 -- 2.2.4 Industry 4.0 (or) I4.0 -- 2.3 Basic IoT Concepts and the Term Glossary -- 2.4 Industrial Revolution -- 2.4.1 I4.0 Core Idea -- 2.4.2 Origin of I4.0 Concept -- 2.5 Industry -- 2.5.1 Manufacturing Phases -- 2.5.2 Existing Process Planning vs. I4.0 -- 2.5.3 Software for Product Planning-A Link Between Smart Products and the Main System ERP -- 2.6 Industry Production System 4.0 (Smart Factory).
2.6.1 IT Support -- 2.7 I4.0 in Functional Field -- 2.7.1 I4.0 Logistics -- 2.7.2 Resource Planning -- 2.7.3 Systems for Warehouse Management -- 2.7.4 Transportation Management Systems -- 2.7.5 Transportation Systems with Intelligence -- 2.7.6 Information Security -- 2.8 Existing Technology in I4.0 -- 2.8.1 Applications of I4.0 in Existing Industries -- 2.8.2 Additive Manufacturing (AM) -- 2.8.3 Intelligent Machines -- 2.8.4 Robots that are Self-Aware -- 2.8.5 Materials that are Smart -- 2.8.6 IoT -- 2.8.7 The Internet of Things in Industry (IIoT) -- 2.8.8 Sensors that are Smart -- 2.8.9 System Using a Smart Programmable Logic Controller (PLC) -- 2.8.10 Software -- 2.8.11 Augmented Reality (AR)/Virtual Reality (VR) -- 2.8.12 Gateway for the Internet of Things -- 2.8.13 Cloud -- 2.8.14 Applications of Additive Manufacturing in I4.0 -- 2.8.15 Artificial Intelligence (AI) -- 2.9 Applications in Current Industries -- 2.9.1 I4.0 in Logistics -- 2.9.2 I4.0 in Manufacturing Operation -- 2.10 Future Scope of Research -- 2.10.1 Theoretical Framework of I4.0 -- 2.11 Discussion and Implications -- 2.11.1 Hosting: Microsoft -- 2.11.2 Platform for the Internet of Things (IoT): Microsoft, GE, PTC, and Siemens -- 2.11.3 A Systematic Computational Analysis -- 2.11.4 Festo Proximity Sensor -- 2.11.5 Connectivity Hardware: HMS -- 2.11.6 IT Security: Claroty -- 2.11.7 Accenture Is a Systems Integrator -- 2.11.8 Additive Manufacturing: General Electric -- 2.11.9 Augmented and Virtual Reality: Upskill -- 2.11.10 ABB Collaborative Robots -- 2.11.11 Connected Vision System: Cognex -- 2.11.12 Drones/UAVs: PINC -- 2.11.13 Self-Driving in Vehicles: Clear Path Robotics -- 2.12 Conclusion -- References -- Chapter 3 Interaction of Internet of Things and Sensors for Machining -- 3.1 Introduction -- 3.2 Various Sensors Involved in Machining Process -- 3.2.1 Direct Method Sensors. 3.2.2 Indirect Method Sensors -- 3.2.3 Dynamometer -- 3.2.4 Accelerometer -- 3.2.5 Acoustic Emission Sensor -- 3.2.6 Current Sensors -- 3.3 Other Sensors -- 3.3.1 Temperature Sensors -- 3.3.2 Optical Sensors -- 3.4 Interaction of Sensors During Machining Operation -- 3.4.1 Milling Machining -- 3.4.2 Turning Machining -- 3.4.3 Drilling Machining Operation -- 3.5 Sensor Fusion Technique -- 3.6 Interaction of Internet of Things -- 3.6.1 Identification -- 3.6.2 Sensing -- 3.6.3 Communication -- 3.6.4 Computation -- 3.6.5 Services -- 3.6.6 Semantics -- 3.7 IoT Technologies in Manufacturing Process -- 3.7.1 IoT Challenges -- 3.7.2 IoT-Based Energy Monitoring System -- 3.8 Industrial Application -- 3.8.1 Integrated Structure -- 3.8.2 Monitoring the System Related to Service Based on Internet of Things -- 3.9 Decision Making Methods -- 3.9.1 Artificial Neural Network -- 3.9.2 Fuzzy Inference System -- 3.9.3 Support Vector Mechanism -- 3.9.4 Decision Trees and Random Forest -- 3.9.5 Convolutional Neural Network -- 3.10 Conclusion -- References -- Chapter 4 Application of Internet of Things (IoT) in the Automotive Industry -- 4.1 Introduction -- 4.2 Need For IoT in Automobile Field -- 4.3 Fault Diagnosis in Automobile -- 4.4 Automobile Security and Surveillance System in IoT-Based -- 4.5 A Vehicle Communications -- 4.6 The Smart Vehicle -- 4.7 Connected Vehicles -- 4.7.1 Vehicle-to-Vehicle (V2V) Communications -- 4.7.2 Vehicle-to-Infrastructure (V2I) Communications -- 4.7.3 Vehicle-to-Pedestrian (V2P) Communications -- 4.7.4 Vehicle to Network (V2N) Communication -- 4.7.5 Vehicle to Cloud (V2C) Communication -- 4.7.6 Vehicle to Device (V2D) Communication -- 4.7.7 Vehicle to Grid (V2G) Communications -- 4.8 Conclusion -- References -- Chapter 5 IoT for Food and Beverage Manufacturing -- 5.1 Introduction -- 5.2 The Influence of IoT in a Food Industry. 5.2.1 Management -- 5.2.2 Workers -- 5.2.3 Data -- 5.2.4 IT -- 5.3 A Brief Review of IoT's Involvement in the Food Industry -- 5.4 Challenges to the Food Industry and Role of IoT -- 5.4.1 Handling and Sorting Complex Data -- 5.4.2 A Retiring Skilled Workforce -- 5.4.3 Alternatives for Supply Chain Management -- 5.4.4 Implementation of IoT in Food and Beverage Manufacturing -- 5.4.5 Pilot -- 5.4.6 Plan -- 5.4.7 Proliferate -- 5.5 Applications of IoT in a Food Industry -- 5.5.1 IoT for Handling of Raw Material and Inventory Control -- 5.5.2 Factory Operations and Machine Conditions Using IoT -- 5.5.3 Quality Control With the IoT -- 5.5.4 IoT for Safety -- 5.5.5 The Internet of Things and Sustainability -- 5.5.6 IoT for Product Delivery and Packaging -- 5.5.7 IoT for Vehicle Optimization -- 5.5.8 IoT-Based Water Monitoring Architecture in the Food and Beverage Industry -- 5.6 A FW Tracking System Methodology Based on IoT -- 5.7 Designing an IoT-Based Digital FW Monitoring and Tracking System -- 5.8 The Internet of Things (IoT) Architecture for a Digitized Food Waste System -- 5.9 Hardware Design: Intelligent Scale -- 5.10 Software Design -- References -- Chapter 6 Opportunities: Machine Learning for Industrial IoT Applications -- 6.1 Introduction -- 6.2 I-IoT Applications -- 6.3 Machine Learning Algorithms for Industrial IoT -- 6.3.1 Supervised Learning -- 6.3.2 Semisupervised Learning -- 6.3.3 Unsupervised Learning -- 6.3.4 Reinforcement Learning -- 6.3.5 The Most Common and Popular Machine Learning Algorithms -- 6.4 I-IoT Data Analytics -- 6.4.1 Tools for IoT Analytics -- 6.4.2 Choosing the Right IoT Data Analytics Platforms -- 6.5 Conclusion -- References -- Chapter 7 Role of IoT in Industry Predictive Maintenance -- 7.1 Introduction -- 7.2 Predictive Maintenance -- 7.3 IPdM Systems Framework and Few Key Methodologies. 7.3.1 Detection and Collection of Data -- 7.3.2 Initial Processing of Collected Data -- 7.3.3 Modeling as Per Requirement -- 7.3.4 Influential Parameters -- 7.3.5 Identification of Best Working Path -- 7.3.6 Modifying Output With Respect Sensed Input -- 7.4 Economics of PdM -- 7.5 PdM for Production and Product -- 7.6 Implementation of IPdM -- 7.6.1 Manufacturing with Zero Defects -- 7.6.2 Sense of the Windsene INDSENSE -- 7.7 Case Studies -- 7.7.1 Area 1-Heavy Ash Evacuation -- 7.7.2 Area 2-Seawater Pumps -- 7.7.3 Evaporators -- 7.7.4 System Deployment Considerations in General -- 7.8 Automotive Industry-Integrated IoT -- 7.8.1 Navigation Aspect -- 7.8.2 Continual Working of Toll Booth -- 7.8.3 Theft Security System -- 7.8.4 Black Box-Enabled IoT -- 7.8.5 Regularizing Motion of Emergency Vehicle -- 7.8.6 Pollution Monitoring System -- 7.8.7 Timely Assessment of Driver's Condition -- 7.8.8 Vehicle Performance Monitoring -- 7.9 Conclusion -- References -- Chapter 8 Role of IoT in Product Development -- 8.1 Introduction -- 8.1.1 Industry 4.0 -- 8.2 Need to Understand the Product Architecture -- 8.3 Product Development Process -- 8.3.1 Criteria to Classify the New Products -- 8.3.2 Product Configuration -- 8.3.3 Challenges in Product Development while Developing IoT Products (Data-Driven Product Development) -- 8.3.4 Role of IoT in Product Development for Industrial Applications -- 8.3.5 Impacts and Future Perspectives of IoT in Product Development -- 8.4 Conclusion -- References -- Chapter 9 Benefits of IoT in Automated Systems -- 9.1 Introduction -- 9.2 Benefits of Automation -- 9.2.1 Improved Productivity -- 9.2.2 Efficient Operation Management -- 9.2.3 Better Use of Resources -- 9.2.4 Cost-Effective Operation -- 9.2.5 Improved Work Safety -- 9.2.6 Software Bots -- 9.2.7 Enhanced Public Sector Operations -- 9.2.8 Healthcare Benefits. 9.3 Smart City Automation. |
| Record Nr. | UNINA-9910830752203321 |
| Hoboken, New Jersey : , : John Wiley & Sons, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Internet of things in business transformation : developing an engineering and business strategy for industry 5.0 / / editors, Parul Gandhi [et al.]
| Internet of things in business transformation : developing an engineering and business strategy for industry 5.0 / / editors, Parul Gandhi [et al.] |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, , [2021] |
| Descrizione fisica | 1 online resource (320 pages) : illustrations |
| Disciplina | 658.40602854678 |
| Soggetto topico | Internet of things - Industrial applications |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-71113-4
1-5231-3687-1 1-119-71114-2 1-119-71115-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910555121403321 |
| Hoboken, NJ : , : John Wiley & Sons, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Internet of things in business transformation : developing an engineering and business strategy for industry 5.0 / / editors, Parul Gandhi [et al.]
| Internet of things in business transformation : developing an engineering and business strategy for industry 5.0 / / editors, Parul Gandhi [et al.] |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, , [2021] |
| Descrizione fisica | 1 online resource (320 pages) : illustrations |
| Disciplina | 658.40602854678 |
| Soggetto topico | Internet of things - Industrial applications |
| ISBN |
1-119-71113-4
1-5231-3687-1 1-119-71114-2 1-119-71115-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910830094103321 |
| Hoboken, NJ : , : John Wiley & Sons, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Internet of things legislation : hearing before the Subcommittee on Digital Commerce and Consumer Protection of the Committee on Energy and Commerce, House of Representatives, One Hundred Fifteenth Congress, second session, May 22, 2018
| Internet of things legislation : hearing before the Subcommittee on Digital Commerce and Consumer Protection of the Committee on Energy and Commerce, House of Representatives, One Hundred Fifteenth Congress, second session, May 22, 2018 |
| Pubbl/distr/stampa | Washington : , : U.S. Government Publishing Office, , 2019 |
| Descrizione fisica | 1 online resource (iii, 115 pages) : illustrations |
| Soggetto topico |
Internet of things - Industrial applications
Internet of things - Law and legislation Embedded Internet devices Machine-to-machine communications Disruptive technologies - United States Technological innovations - Social aspects Technological innovations - Economic aspects Disruptive technologies |
| Soggetto genere / forma | Legislative hearings. |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Internet of things legislation |
| Record Nr. | UNINA-9910711837303321 |
| Washington : , : U.S. Government Publishing Office, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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IoT and AI in agriculture : self-sufficiency in food production to achieve society 5.0 and SDG's globally / / Tofael Ahamed, editors
| IoT and AI in agriculture : self-sufficiency in food production to achieve society 5.0 and SDG's globally / / Tofael Ahamed, editors |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (XVII, 461 p. 1 illus.) |
| Disciplina | 338.16 |
| Soggetto topico |
Agricultural innovations
Artificial intelligence - Agricultural applications Internet of things - Industrial applications |
| ISBN | 981-19-8113-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. IoT x AI: Introducing Agricultural Innovation for Global Food Production -- Chapter 2. Transforming Controlled Environment Plant Production toward Circular Bioeconomy Systems -- Chapter 3. Artificial Lighting Systems for Plant Growth and Development in Indoor Farming -- Chapter 4. An IoT-based Precision Irrigation System to Optimize Plant Water Requirements for Indoor and Outdoor Farming Systems -- Chapter 5. Artificial Intelligence & Internet of Things: Application in Urban Water Management -- Chapter 6.Purification of Agricultural Polluted Water Using Solar Distillation and Hot Water Producing with Continuous Monitoring Based on IoT -- Chapter 7. Long Range Wide Area Network (LoRaWAN) for Oil Palm Soil Monitoring -- Chapter 8. Application of Smart Machine Vision in Agriculture, Forestry, Fishery, and Animal Husbandry -- Chapter 9. Artificial Intelligence in Agriculture: Commitment to Establish Society 5.0 -- Chapter 10. Potentials of Deep Learning Frameworks for Tree Trunk Detection in Orchard to Enable Autonomous Navigation System -- Chapter 11. Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT -- Chapter 12. Pear Recognition in an Orchard from 3D Stereo Camera Datasets to Develop an Autonomous Mechanism Compared with Deep Learning Algorithms -- Chapter 13. Thermal Imaging and Deep Learning Object Detection Algorithms for Early Embryo Detection – A Methodology Development Addressed to Quail Precision Hatching -- Chapter 14. Intelligent Sensing and Robotic Picking of Kiwifruit in Orchard -- Chapter 15. Low-cost Automatic Machinery Development to Increase Timeliness and Efficiency of Operation for Small Scale Farmers to Achieve SDGs -- Chapter 16. Vision-based Leader Vehicle Trajectory Tracking for Multiple Agricultural Vehicles -- Chapter 17. Autonomous Robots in Orchard Management: Present status and future trends -- Chapter 18. Comparing Soil Moisture Retrieval from Water Cloud Model and Neural Network Using PALSAR-2 for Oil Palm Estates -- Chapter 19. Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Approach -- Chapter 20. Basal Stem Rot Disease Classification by Machine Learning Using Thermal Images and an Imbalanced Data Approach -- Chapter 21. Early Detection of Plant Disease Infection using Hyperspectral Data and Machine Learning -- Chapter 22. The Spectrum of Autonomous Machinery Development to Increase Agricultural Productivity for Achieving Society 5.0 in Japan. |
| Record Nr. | UNINA-9910686479303321 |
| Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Lifecycle IoT Security for Engineers
| Lifecycle IoT Security for Engineers |
| Autore | Kaustubh Dhondge |
| Pubbl/distr/stampa | Norwood : , : Artech House, , 2021 |
| Descrizione fisica | 1 online resource (219 pages) |
| Disciplina | 004.678 |
| Soggetto topico |
Internet of things - Security measures
Internet of things - Industrial applications |
| ISBN |
1-5231-4587-0
1-63081-804-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910795111403321 |
Kaustubh Dhondge
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| Norwood : , : Artech House, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Lifecycle IoT Security for Engineers
| Lifecycle IoT Security for Engineers |
| Autore | Kaustubh Dhondge |
| Pubbl/distr/stampa | Norwood : , : Artech House, , 2021 |
| Descrizione fisica | 1 online resource (219 pages) |
| Disciplina | 004.678 |
| Soggetto topico |
Internet of things - Security measures
Internet of things - Industrial applications |
| ISBN |
1-5231-4587-0
1-63081-804-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910824649303321 |
|
Kaustubh Dhondge
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| Norwood : , : Artech House, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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