IoT and Cloud Computing for Societal Good

• Record Nr.: UNINA9910510580503321
• Autore: Verma Jitendra Kumar
• Pubbl/distr/stampa: Cham : , : Springer International Publishing AG, , 2022; ©2022
• ISBN: 9783030738853; 9783030738846
• Descrizione fisica: 1 online resource (331 pages)
• Collana: EAI/Springer Innovations in Communication and Computing Ser.
• Altri autori (Persone): SaxenaDeepak; González-PridaVicente
• Soggetti: Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Preface -- Technical Programme Committee -- Editorial Advisory Board -- Contents -- About the Editors -- Part I Tackling Climate Change -- 1 Towards Energy Efficient Cloud Computing: Research Directions and Methodological Approach -- 1.1 Introduction -- 1.2 Background Motivation -- 1.3 Power Consumption and Energy Efficient Dynamic VM Consolidation -- 1.4 Defining Objective and Setting Research Questions -- 1.5 Methodological Approach -- 1.6 Conclusion -- References -- 2 IoT-Based Smart Air Quality Control System: Prevention to COVID-19 -- 2.1 Introduction -- 2.1.1 Motivation -- 2.1.2 Contribution -- 2.1.3 Organization -- 2.2 Related Work -- 2.3 Proposed Model -- 2.4 Results and Discussion -- 2.5 Conclusion and Future Scope -- References -- 3 Forecasting of Air Pollution via a Low-Cost IoT-Based Monitoring System -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Methodology -- 3.3.1 Data -- 3.3.2 Models -- 3.3.3 Model Calibration -- 3.4 Result -- 3.5 Discussion and Future Work -- References -- 4 Internet of Things Based Best Fruit Segregation and Taxonomy System for Smart Agriculture -- 4.1 Introduction -- 4.2 Literature Review -- 4.3 Methodology -- 4.4 Software/Hardware Tool Used -- 4.4.1 Software Part -- 4.4.2 Hardware Part -- 4.5 Design and Implementation -- 4.5.1 Background Separation of Image -- 4.5.2 Calculating the Center of Blob ; -- 4.5.3 Calculating Black Dots -- 4.5.4 Fourier Transform -- 4.6 Conclusion -- References -- 5 Toward the Creation of a Web-Based Platform "Bike Sharing" in the Local Transport System -- 5.1 General Formulation of the Problem -- 5.2 Analysis of Recent Research and Publications -- 5.3 Selection of Previously Unsolved Parts of the Overall Problem -- 5.4 Statement of Research Tasks -- 5.5 Material and Research Results -- 5.6 Conclusions -- References -- Part II Digital Health, Learning and Industry.; 6 A Survey of Societal Applications of IOT -- 6.1 Introduction -- 6.2 Motivation -- 6.3 IOT -- 6.4 Trends in the Internet of Things -- 6.5 Comparison with the Existing System -- 6.6 Agriculture Applications of IoT -- 6.6.1 Monitoring of Irrigation in Agriculture with IoT -- 6.6.2 Smart Agriculture -- 6.6.3 Intelligent Irrigation -- 6.6.4 Automated Weather Report Causation for Smart Irrigation -- 6.6.5 IoT Based Disease Analysis in Agriculture -- 6.6.6 Flood Prevention Using IoT -- 6.7 Healthcare Applications of IoT -- 6.7.1 IoT in Healthcare -- 6.7.2 Challenges in Health Care with IoT -- 6.7.3 Multidisciplinary Health Care System -- 6.7.4 Survey on Internet of Things Based on Health Care -- 6.7.5 IoT Incorporate Attention Monitoring to Intercept Incursion -- 6.7.6 IoT Based Healthcare with Body Sensor Network (BSN) -- 6.8 IoT in Military -- 6.8.1 Smart Sniper with IoT -- 6.8.2 Military Applications in Smart City with IoT -- 6.8.3 IoT Based Military Federation -- 6.8.4 Fault Tolerant Techniques on the Internet of Military Things -- 6.8.5 IoT Based Military Applications -- 6.9 IoT in Railways -- 6.10 Smart Train Detector Using IoT Approach -- 6.11 IoT in Smart Cities -- 6.12 Smart Home -- 6.12.1 Energy Competent Home Automation Using IoT -- 6.12.2 Home Gadgets Control with IoT -- 6.12.3 Performance Analysis on Wireless Smart Home Automation -- 6.13 Industrial IoT -- 6.13.1 The Internet of Robotic Things -- 6.14 Recognition Proficiency -- 6.15 Fluctuation Proficiency -- 6.16 Conclusion -- References -- 7 Simplify the Difficult: Artificial Intelligence and Cloud Computing in Healthcare -- 7.1 Introduction -- 7.2 Motivation -- 7.3 Cloud Computing -- 7.3.1 Software as a Service (SaaS) -- 7.3.2 Platform as a Service (PaaS) -- 7.3.3 Infrastructure as a Service (IaaS) -- 7.3.4 Internet of Things (IoT) -- 7.4 Artificial Intelligence Tools -- 7.4.1 Neuron.; 7.4.2 Neural Networks -- 7.4.2.1 Deep Neural Network -- 7.4.2.2 Natural Language Processing -- 7.4.2.3 Recurrent Neural Networks (RNN) -- 7.4.2.4 Convolutional Neural Network (CNN) -- 7.5 Application Areas -- 7.5.1 Information Tools -- 7.5.1.1 Applications in Health Care Administration -- 7.5.1.2 Cloud Computing Systems for Healthcare Management -- 7.5.1.3 Health Monitoring with IoT -- 7.5.2 Disease Management -- 7.5.2.1 Diabetes Management Through Artificial Intelligence -- 7.5.2.2 Monitoring Mental Health Through IoT -- 7.5.3 Preemptive Measures -- 7.5.3.1 Early Detection Through Biomarkers -- 7.5.3.2 Diagnosis Through Image Recognition -- 7.5.4 Pathological Diagnosis -- 7.5.4.1 Rheumatoid Arthritis -- 7.5.4.2 Epileptic Seizures -- 7.5.4.3 Alzheimer's Disease -- 7.5.4.4 Diabetic Retinopathy -- 7.5.4.5 Breast Cancer -- 7.5.4.6 Obstructive Lung Disease -- 7.5.4.7 Cardiovascular Diseases (CVDs) -- 7.5.5 Applications in Mental Healthcare -- 7.5.5.1 Mental Health Detection Through Sentimental Analysis on Tweets -- 7.5.5.2 Suicide Prevention -- 7.6 Current Limitations -- 7.7 Conclusions and Future Prospects -- References -- 8 NOS Personal Assistant to Engage Elderly People withSmart Home -- 8.1 Introduction -- 8.2 Background -- 8.2.1 Internet of Things -- 8.2.2 Ambient Assisted Living Market -- 8.2.3 Natural Interfaces Landscape -- 8.3 NOS Technology -- 8.3.1 The Challenges of NOS Personal Assistant -- 8.3.2 The Overall Solution -- ; 8.3.3 A Bottom-Up Approach -- 8.4 Future Research and Innovation Directions -- 8.5 Conclusion -- References -- 9 Digital Technologies Changing the Landscape of Corporate Learning and Development -- 9.1 Introduction -- 9.1.1 Rise of Digital Learning -- 9.2 Digital Learning Framework -- 9.2.1 Start with the End in Mind -- 9.2.2 Assess the Digital Fluency of Your Target Audience -- 9.2.3 Design the Content.; 9.2.4 Select Appropriate Digital Learning Platforms and Tools -- 9.2.5 Implement the Digital Learning Program -- 9.2.6 Measure the Impact and Foster Continuous Improvement -- 9.3 Creating the Culture of Learning -- 9.4 Upskilling L&amp -- D Personnel -- 9.5 Conclusion -- References -- 10 An Assessment of the Behavioral Intention of Generation Z Toward the Adoption of Digital Learning Applications -- 10.1 Introduction -- 10.2 Literature Review -- 10.2.1 Digital Learning -- 10.2.2 Internet of Things (IoT) and Cloud Computing -- 10.2.3 Digital Learning App -- 10.2.4 Actual Use (AU) -- 10.2.5 Behavioral Intention to Use (BITU) -- 10.2.6 Attitude Toward Use (ATU) -- 10.2.7 Perceived Usefulness (PU) -- 10.2.8 Perceived Ease of Use (PEOU) -- 10.2.9 Utility, Learning, and Perceived Usefulness -- 10.2.10 Student Engagement and Perceived Usefulness -- 10.2.11 Data Security and Perceived Usefulness -- 10.2.12 Feedback and Rating and Perceived Usefulness -- 10.2.13 Accessibility and Perceived Usefulness -- 10.2.14 User Interface and Perceived Usefulness -- 10.2.15 Entertainment and Perceived Ease of Use -- 10.2.16 Accessibility and Perceived Ease of Use -- 10.2.17 User Interface and Perceived Ease of Use -- 10.2.18 Exiting TAM Models -- 10.3 Research Methodology -- 10.4 Data Analysis and Interpretation -- 10.4.1 Sample Characteristics and Distributions (N == 490) -- 10.4.2 Measurement Model: Reliability and Validity -- 10.4.3 Structural Equation Model -- 10.5 Discussion -- 10.6 Practical Implications -- 10.7 Theoretical Implications -- 10.8 Conclusion -- References -- 11 A Literature Review on Lean Manufacturing in the Industry 4.0: From Integrated Systems to IoT and Smart Factories -- 11.1 Introduction -- 11.2 The Term Industry 4.0 -- 11.3 From Integrated System to IoT -- 11.4 Smart Factory: Future of Automated Production -- 11.5 Industry 4.0 Characteristics.; 11.6 Enabling Technologies of Industry 4.0 -- 11.6.1 Big Data and Analytics -- 11.6.2 Industrial Cloud and Cloud Computing -- 11.6.3 Internet of Things (IoT) -- 11.6.4 Augmented Reality -- 11.6.5 Simulation -- 11.6.6 Autonomous Robot -- 11.6.7 Horizontal and Vertical Integration -- 11.6.8 Additive Manufacturing -- 11.6.9 Cybersecurity -- 11.7 Conclusions -- References -- Part III Improving the Technology -- 12 Multimodal Feature Analysis for Precise Human Hand Gesture Recognition -- 12.1 Introduction and Background -- 12.2 Multimodal Feature Analysis for Gesture Recognition -- 12.2.1 Multi-model Feature Extraction -- 12.2.2 Multi-model Feature Recognition -- 12.3 Results and Discussion -- 12.3.1 Success Ratio -- 12.3.2 Recognition Time -- 12.4 Conclusion -- References -- 13 Calculating the Optimal Frequency of Maintenance for the Improvement of Risk Management: Plausible Models for the Integration of Cloud and IoT -- 13.1 Introduction -- 13.2 Variables Used in the Maintenance Frequency Optimization Process -- 13.2.1 Reliability and Risk -- 13.2.2 Operational Costs -- 13.2.3 Loss of Performance -- 13.2.4 Extension of Equipment Life -- 13.3 Mathematical Models Used for Preventive Replacement -- 13.3.1 Optimal Replacement Model with Use -- 13.3.2 Optimal Interval of Preventive Replacement -- 13.4 Case Study -- 13.5 Conclusions -- References -- 14 Interceptor Pattern-Based Middleware for IoT Protocol Interoperability -- 14.1 Introduction -- 14.2 Related Works -- 14.3 Related Works -- 14.4 Related Works -- 14.5 Specific ; Use Case -- 14.6 Test Results and Discussion -- 14.7 Conclusions -- References -- 15 Mining Active Influential Nodes for Finding Information Diffusion in Social Networks -- 15.1 Introduction -- 15.2 Literature Review -- 15.2.1 Influential Nodes Identification in Static Network.; 15.2.2 Influential Nodes Identification in Dynamic/Temporal Networks.