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Biblioteca

MARC Lista (tabellare)
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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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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  
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  
Norwood : , : Artech House, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Smart Health Technologies for the COVID-19 Pandemic : Internet of Medical Things Perspectives
Smart Health Technologies for the COVID-19 Pandemic : Internet of Medical Things Perspectives
Autore Chakraborty Chinmay
Edizione [1st ed.]
Pubbl/distr/stampa Stevenage : , : Institution of Engineering & Technology, , 2022
Descrizione fisica 1 online resource (502 pages)
Disciplina 610.285
Altri autori (Persone) RodriguesJoel J. P. C
Collana Healthcare Technologies
Soggetto topico Internet of things - Industrial applications
Internet of things - Health aspects
ISBN 1-83724-474-X
1-5231-4717-2
1-83953-519-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Title -- Copyright -- Contents -- About the editors -- Preface -- 1 Internet of Things (IoT) and blockchain-based solutions to confront COVID-19 pandemic -- 1.1 Introduction -- 1.2 Internet of Things (IoT) and blockchain overview -- 1.2.1 Internet of Things -- 1.2.2 Blockchain -- 1.3 IoT technologies to confront COVID-19 -- 1.3.1 Health monitoring systems -- 1.3.2 Tracking and detecting possible patients -- 1.3.3 Disinfecting area -- 1.3.4 Telemedicine -- 1.3.5 Logistics delivery -- 1.4 Blockchain technologies to confront COVID-19 -- 1.4.1 Contact tracing -- 1.4.2 Database security -- 1.4.3 Information sharing -- 1.4.4 Prevention of data fabrication -- 1.4.5 Internet of Medical Things -- 1.5 Challenges, solutions, and deliverables -- 1.5.1 Challenges of IoT and blockchain technology -- 1.5.2 Possible solutions and deliverables -- 1.6 Key findings and discussion -- 1.7 Conclusion and future scopes -- References -- 2 Application of big data and computational intelligence in fighting COVID-19 epidemic -- 2.1 Introduction -- 2.2 Applicability of computational intelligence in combating COVID-19 pandemic -- 2.3 Big data and analytics in battling COVID-19 outbreak -- 2.4 The limitations of using big data and computational intelligence to fight the COVID-19 pandemic -- 2.5 The practical case of using computational intelligence in fighting COVID-19 pandemic -- 2.5.1 Confusion matrix -- 2.5.2 ROC curves -- 2.5.3 Precision-recall curve -- 2.6 Conclusion -- References -- 3 Cloud-based IoMT for early COVID-19 diagnosis and monitoring -- 3.1 Introduction -- 3.2 Overview about COVID-19 treatments -- 3.2.1 Symptoms -- 3.2.2 Methodologies in COVID-19 diagnosis -- 3.2.3 Treatment approaches -- 3.2.4 Available vaccine -- 3.2.5 COVID-19 timeline -- 3.3 Related work -- 3.3.1 Lightweight block encryption__amp__#8211.
based secure health monitoring system for data management -- 3.3.2 Smart diagnostic/therapeutic framework for COVID-19 patients -- 3.3.3 IoT-based framework for collecting real-time symptom data using machine learning algorithms -- 3.4 Proposed methodology -- 3.4.1 Architecture of proposed IoT framework -- 3.4.2 Data acquisition using wearables devices -- 3.5 Implementation of proposed framework -- 3.6 Results and discussion -- 3.7 Conclusion and future scopes -- References -- 4 Assessment analysis of COVID-19 on the global economics and trades -- 4.1 Introduction -- 4.2 Backgrounds -- 4.3 Social impacts on finance -- 4.4 Framework for the international financial system, bionetworks, and maintainability on pandemic -- 4.4.1 Assessment strategy constructions to fight COVID-19 -- 4.4.2 Macro-finance impacts -- 4.4.3 Econometric effects: consumer preferences -- 4.4.4 Nonpositive impacts of COVID-19 -- 4.4.5 Impact of international commercial trading -- 4.4.6 COVID-19__amp__#8217 -- s effect on the aviation industry -- 4.4.7 Significant collision on the travel sector -- 4.4.8 Significant reduction in primary energy usage -- 4.4.9 Record decrease in CO2 emissions -- 4.4.10 Rise in digitalization -- 4.5 The role of circular economy -- 4.5.1 The circular economy for slowing the onset of climate collapse -- 4.5.2 Social finance system -- 4.5.3 Hurdles to CE for context of COVID-19 -- 4.6 Chances financial support after COVID-19 -- 4.6.1 Several solutions to manage hospital medical and general waste -- 4.6.2 Facilities for CE in communication sector -- 4.6.3 Use digitalization after COVID-19 -- 4.7 Conclusions -- References -- 5 Early diagnosis and remote monitoring using cloud-based IoMT for COVID-19 -- 5.1 Introduction -- 5.2 Detection techniques -- 5.3 Internet of Medical Things.
5.4 IoMT devices for the identification of COVID-19 symptoms and remote monitoring -- 5.4.1 Wearables -- 5.4.2 Smartphone applications -- 5.5 Early diagnosis of COVID-19 and remote monitoring procedures -- 5.6 Machine learning and deep learning in COVID-19 diagnosis -- 5.7 Related works -- 5.8 Experimental case study -- 5.8.1 Dataset description -- 5.8.2 Methodology -- 5.8.3 Training -- 5.8.4 Experimental setup and results -- 5.9 Measures for monitoring and tracking COVID-19 -- 5.10 Limitations of using IoMT devices -- 5.11 Conclusion and future scope -- References -- 6 Blockchain technology for secure COVID-19 pandemic data handling -- 6.1 Introduction -- 6.2 Recent developments in blockchain technology -- 6.2.1 Healthcare data systems -- 6.2.2 Healthcare data exchanges -- 6.2.3 Healthcare administration -- 6.2.4 Pharmaceuticals -- 6.3 Potential benefits of blockchain technology in data handling -- 6.3.1 Better exchange of healthcare data records -- 6.3.2 Validating trust in medical research and supplies -- 6.3.3 Validating correct billing management -- 6.3.4 Internet of Things (IoT) in healthcare -- 6.3.5 Optimized privacy and data security -- 6.4 Key challenges of blockchain technology in data handling -- 6.4.1 Security -- 6.4.2 Speed -- 6.4.3 Interoperability -- 6.4.4 Stringent data protection regulation -- 6.4.5 Scalability -- 6.4.6 Privacy -- 6.5 Prospects of blockchain technology -- 6.6 Research on blockchain technology in COVID-19 healthcare -- 6.7 Real-time analysis of COVID-19 pandemic data -- 6.7.1 The susceptible recovered infectious (SIR) model -- 6.7.2 Standard logistic regression model -- 6.7.3 Time-to-event analytics model -- 6.7.4 Results of major real-time analysis -- 6.8 Recommendations and future directions -- 6.9 Conclusion and future scopes -- Acknowledgments -- References -- 7 Social distancing technologies for COVID-19.
7.1 Introduction -- 7.2 Methodology -- 7.3 Social distancing technologies for education -- 7.3.1 Learning management system -- 7.3.2 Social networking and conference software for education -- 7.4 Social distancing technology in healthcare -- 7.4.1 Wearable technology -- 7.4.2 Screening system -- 7.4.3 Queue systems -- 7.4.4 Payment system -- 7.4.5 Social distancing notified people in public -- 7.5 Social distancing technology in manufacturing -- 7.5.1 Checking the distance using wearable device -- 7.5.2 Distance monitoring using Wi-Fi -- 7.5.3 Distance monitoring using video analytics -- 7.5.4 Social distancing by replacing some work with a robot -- 7.6 Social-distancing technologies for supporting everyday life -- 7.6.1 Technologies support working at home -- 7.6.2 Applications support work from home (WFH) service -- 7.6.3 Conferencing application -- 7.7 Social distancing and smart city -- 7.7.1 AI and big data -- 7.7.2 Implementation and usability -- 7.7.3 Privacy and security -- 7.7.4 Policy and legislation -- 7.8 Conclusion and future works -- References -- 8 Social health protection in touristic destinations during COVID-19 -- 8.1 Introduction -- 8.2 Related work -- 8.3 Proposal of software solution for health protection -- 8.3.1 System architecture -- 8.3.2 Healthcare service -- 8.3.3 Tourist service -- 8.3.4 Local government service -- 8.3.5 Border control -- 8.4 Data protection -- 8.5 Conclusion and future works -- References -- 9 Analysis of Artificial Intelligence and Internet of Things in biomedical imaging and sequential data for COVID-19 -- 9.1 Introduction -- 9.2 Definition of biomedical keywords -- 9.2.1 Microarray and RNA-seq data -- 9.2.2 De novo mutation -- 9.2.3 ChiP-seq data -- 9.2.4 Biomedical imaging -- 9.3 Categories of computational algorithms in biomedical data -- 9.3.1 Biomedical data analysis.
9.3.2 Array-based data analysis -- 9.3.3 Hybrid data analysis -- 9.4 Different techniques for diagnosis using biomedical imaging -- 9.4.1 Brain -- 9.4.2 Breast -- 9.4.3 Kidney -- 9.4.4 Ovary -- 9.4.5 Skin cancer -- 9.4.6 Soft tissue sarcoma -- 9.5 Comparative review of computational algorithms -- 9.6 Role of CT in COVID-19 pandemic -- 9.7 Advent of smart technologies during COVID-19 -- 9.7.1 Building ML models to diagnose COVID-19 -- 9.7.2 Impact of IoT in healthcare -- 9.8 Conclusion -- References -- 10 Review of medical imaging with machine learning and deep learning-based approaches for COVID-19 -- 10.1 Introduction -- 10.2 Literature review -- 10.2.1 Reviewed work -- 10.3 Comparative analysis of existing work -- 10.4 Research gaps -- 10.4.1 Unavailability of large datasets -- 10.4.2 Imbalanced datasets -- 10.4.3 Multiple image sources -- 10.5 Conclusion -- References -- 11 Machine-based drug design to inhibit SARS-CoV-2 virus -- 11.1 Introduction -- 11.2 What is SARS-coronavirus-2? -- 11.3 Mechanism of SARS-coronavirus-2 infection in human -- 11.4 How SARS-coronavirus-2 multiplies? -- 11.5 Human antibody generation and role of vaccine -- 11.5.1 Immediate action of human antibody -- 11.5.2 Role of synthetic vaccine on COVID-19 -- 11.6 Real-time COVID-19 identification test (RT-PCR) -- 11.6.1 Limitations of RT-PCR tool -- 11.7 Discussion on in silico methods in COVID-19 drug research -- 11.7.1 In silico-assisted anchoring site analysis -- 11.7.2 Machine-assisted designing and evaluation of COVID-19 drug -- 11.8 Machine-integrated advanced techniques for COVID-19 -- 11.8.1 Computerized tomography in COVID-19 detection -- 11.8.2 Advanced MRI for COVID-19 treatment -- 11.9 Summary -- 11.10 Conclusion and future scopes -- 11.10.1 Future scope -- References -- 12 Stress detection for cognitive rehabilitation in COVID-19 scenario -- 12.1 Introduction.
12.2 Related works.
Record Nr. UNINA-9911004718703321
Chakraborty Chinmay  
Stevenage : , : Institution of Engineering & Technology, , 2022
Materiale a stampa
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