Predictive analytics in cloud, fog, and edge computing : perspectives and practices of Blockchain, IoT, and 5G / / edited by Hiren Kumar Thakkar [and three others]

• Pubblicazione: Cham, Switzerland : , : Springer, , [2023]; ©2023
• Descrizione fisica: 1 online resource (252 pages)
• Disciplina: 621.38456
• Soggetto topico: Blockchains (Databases); 5G mobile communication systems; Cloud computing
• Persona (resp. second.): ThakkarHiren Kumar
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Acknowledgement -- Contents -- Collaboration of IoT and Cloud Computing Towards HealthcareSecurity -- 1 Introduction -- 2 Inspiration -- 3 Related Work and Background -- 4 Cloud Computing Deployment Models -- 4.1 Public Internet -- 4.2 Corporate Cloud -- 4.3 Cloud Hybrid -- 4.4 Cloud Provider -- 5 Utility Computing Service Models -- 5.1 Software as a Service (SaaS) -- 5.2 Infrastructure as a Service (IaaS) -- 5.3 Platform as a Service (PaaS) -- 6 Security Issues -- 7 Threats in Cloud Computing -- 7.1 Compromised Identities and Broken Security -- 7.2 Data Infringement -- 7.3 Hacked Frontier and APIs -- 7.4 Manipulated System Vulnerabilities -- 7.5 Permanent Data Loss -- 7.6 Inadequate Assiduity -- 7.7 Cloud Service Inattention -- 7.8 DoS Attacks -- 7.9 Security Challenges in Cloud Infrastructure -- 7.9.1 Security Challenges -- 7.9.2 Challenges of Deployed Models -- 7.9.3 Resource Pooling -- 7.9.4 Unencrypted Data -- 7.9.5 Identity Management and Authentication -- 7.9.6 Network Issues -- 7.10 Point at Issue in the IoT Health Care Framework -- 7.10.1 Reliability -- 7.10.2 Discretion -- 7.10.3 Solitude -- 7.10.4 Unintended Efforts -- 7.11 Challenges -- 7.11.1 Security -- 7.11.2 Confidentiality -- 7.11.3 Assimilation -- 7.11.4 Business Illustration -- 7.12 Dispensing Refined Patient Supervision -- 7.13 Character of IoT in Healthcare -- 7.14 Conclusion -- 7.15 Future Work -- References -- Robust, Reversible Medical Image Watermarking for Transmission of Medical Images over Cloud in Smart IoT Healthcare -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 3.1 EHR Insertion (Embedding) and Retrieval (Extraction) -- 3.2 EHR Encryption and Decryption -- 4 Experimental Results and Discussion -- 5 Conclusions -- References -- The Role of Blockchain in Cloud Computing -- 1 Blockchain -- 1.1 Introduction -- 1.2 Characteristics. ; 1.2.1 Immutability -- 1.2.2 Distributed -- 1.2.3 Enhanced Security -- 1.2.4 Distributed Ledgers -- 1.2.5 Faster Settlement -- 1.2.6 Working of Blockchain -- 1.3 Major Implementations -- 1.3.1 Cryptocurrencies -- 1.3.2 Smart Contracts -- 1.3.3 Monetary Services -- 1.3.4 Games -- 1.4 Blockchain Types -- 1.5 There Are Mainly 4 Types of Blockchain as Shown in Table 1 -- 1.5.1 Public Blockchain Networks -- 1.5.2 Exclusive Blockchain Networks -- 1.5.3 Hybrid Blockchain Networks -- 1.5.4 Consortium Networks -- 1.6 Advantages -- 1.6.1 Secure -- 1.6.2 There Will Be No Intervention from Third Parties -- 1.6.3 Safe Transactions -- 1.6.4 Automation -- 1.7 Disadvantages -- 1.7.1 High Implementation Cost -- 1.7.2 Incompetency -- 1.7.3 Private Keys -- 1.7.4 Storage Capacity -- 2 Cloud Computing -- 2.1 What Is Cloud Computing? -- 2.2 Deployment Models in Cloud -- 2.2.1 Public Cloud -- 2.2.2 Private Cloud -- 2.2.3 Hybrid Cloud -- 2.2.4 Community Cloud -- 2.3 Implementations of Cloud Computing -- 2.3.1 Web Based Services -- 2.3.2 Software as a Service -- 2.3.3 Infrastructure as a Service -- 2.3.4 Platform as a Service -- 2.4 Comparison of Cloud Computing Model with Traditional Model -- 2.4.1 Persistency -- 2.4.2 Automation -- 2.4.3 Cost -- 2.4.4 Security -- 2.5 Advantages of Cloud Computing -- 2.5.1 Cost Efficiency -- 2.5.2 Backup and Recovery -- 2.5.3 Integration of Software -- 2.5.4 Information Availability -- 2.5.5 Deployment -- 2.5.6 Easier Scale for Services and Delivery of New Services -- 2.6 Challenges of Cloud Computing -- 2.6.1 Technical Problems -- 2.6.2 Certainty -- 2.6.3 Vulnerable Attacks -- 2.6.4 Suspension -- 2.6.5 Inflexibility -- 2.6.6 Lack of Assistance -- 2.7 Integration of Cloud Computing with Block Chain -- 2.7.1 The Advantages of Combining Cloud and Blockchain Technology -- 2.7.2 Blockchain Support for Cloud Computing. ; 2.7.3 Deduplication of Data in the Cloud with Blockchain -- 2.7.4 Access Control Based on Blockchain in Cloud -- References -- Analysis and Prediction of Plant Growth in a Cloud-Based Smart Sensor Controlled Environment -- 1 Introduction -- 2 Literature Survey -- 3 IoT in Greenhouse -- 3.1 Architecture -- 3.2 Cloud Implementation -- 3.3 Hardware Components (Fig. 2) -- 4 System Overview -- 4.1 Dataset -- 4.2 Data Preprocessing -- 4.3 LightGBM -- 4.4 Training and Building the Model -- 5 Results and Explanation -- 6 Conclusion -- References -- Cloud-Based IoT Controlled System Model for Plant DiseaseMonitoring -- 1 Introduction -- 2 Literature Survey -- 3 IoT Controlled Device -- 4 Cloud Architecture -- 5 Methodology -- 5.1 HOG Filter -- 6 Experimental Analysis -- 6.1 Analysis Using Artificial Neural Network -- 6.2 Analysis Using Convolutional Neural Network -- 7 Conclusion -- References -- Design and Usage of a Digital E-Pharmacy Application Framework -- 1 Introduction -- 2 Literature Survey -- 3 Utilization of Cloud in Health Care -- 4 Redefining E-Pharmacy Domain -- 5 Impact of Cloud Computing in Pharmacy -- 6 Model Design and Implementation -- 7 Basic Structure of the Cloud Based E-Pharmacy Application -- 8 Security Provided by the Application -- 8.1 XSS Security (Cross Site Scripting) -- 8.2 CSRF Token (Cross Site Request Forgery) -- 8.3 SQL Injection Security -- 8.4 User Upload Security -- 9 Results and Discussion -- 10 Important Features of the Application -- 11 Critical Goals of the Application -- 12 Benefits of the Model -- 13 Summary/Conclusion -- References -- Serverless Data Pipelines for IoT Data Analytics: A Cloud Vendors Perspective and Solutions -- 1 Introduction -- 1.1 Motivation -- 1.2 Contributions -- 2 Background -- 2.1 Internet of Things -- 2.2 Serverless Data Pipelines for IoT Data Processing -- 3 Literature Survey. ; 4 Cloud Service Providers (CSP) and IoT Solutions -- 4.1 Edge Tier -- 4.1.1 Comparison of AWS IoT Greengrass and Azure IoT Edge -- 4.2 Cloud Tier -- 5 Real-Time IoT Application: Predictive Maintenance of Industrial Motor -- 6 Building SDP for Predictive Maintenance Application -- 6.1 Proposed Serverless Data Pipelines -- 6.1.1 Building an Anomaly Detection Model -- 6.2 SDP Using AWS and Microsoft Azure -- 7 Experiments and Results -- 7.1 Performance Metrics -- 7.2 Experimental Setup -- 7.3 Results and Discussions -- 8 Conclusions -- References -- Integration of Predictive Analytics and Cloud Computing for Mental Health Prediction -- 1 Introduction -- 2 Method of Approach -- 2.1 Overview of the Subject -- 2.1.1 Supervised Learning -- 2.1.2 Unsupervised Learning -- 2.2 Selection of Papers -- 2.3 Literature Search Strategy -- 2.4 Study Selection -- 2.5 Data Extraction and Analysis -- 3 Introduction to Mental Health Research -- 3.1 Machine Learning in Big Data -- 3.2 Deep Learning in Healthcare -- 3.3 Natural Language Processing -- 4 The Pipeline of Data Flows from the Sensors to the Algorithmic Approach -- 4.1 Sensor Data -- 4.2 Extraction of Features -- 4.3 Designing the Behavioural Markers -- 4.4 Clinical Target -- 5 Cloud Computing -- 5.1 Architecture of Cloud Computing -- 5.2 Benefits of Cloud Computing in the Healthcare Industry -- 5.3 Cloud Computing as a Solution to Mental Health Issues -- 6 Review of Personal Sensing Research -- 7 Result of the Research -- 7.1 Limitations of the Study Done on the Algorithms to Detect Mental Health -- 7.2 Results Based on iCBT Test -- 8 Discussion -- 9 Conclusion -- References -- Impact of 5G Technologies on Cloud Analytics -- 1 Introduction -- 2 Self-Organizing Next Generation Network Data Analytics in the Cloud -- 2.1 What Is Network Data Analytics? -- 2.2 Benefits of Network Data Analytics. ; 2.3 The Best Uses of Network Data Analytics -- 2.4 The Near Future -- 2.5 The Opportunities -- 3 Intelligent 5G Network Estimation Techniques in the Cloud -- 3.1 Network Estimation Technique -- 3.2 Literature Review -- 4 5G-cloud Integration: Intelligent Security Protocol and Analytics -- 4.1 Scope -- 4.2 5G Cloud Threat -- 4.3 5G-Cloud Integration -- 4.4 Advantages of Security Capabilities -- 5 5G, Fog and Edge Based Approaches for Predictive Analytics -- 5.1 Introduction -- 5.2 Literature Review -- 6 5G and Beyond in Cloud, Edge, and Fog Computing -- 6.1 Edge Computing -- 6.2 Cloud Computing -- 6.3 5G and Beyond -- 7 AI-Enabled Next Generation 6G Wireless Communication -- 7.1 Computation Efficiency and Accuracy -- 7.2 Hardware Development -- 7.3 Types 6 G Wireless Communication -- 7.4 6G Wireless Access Use Case -- References -- IoT Based ECG-SCG Big Data Analysis Framework for Continuous Cardiac Health Monitoring in Cloud Data Centers -- 1 Introduction -- 2 Related Work -- 3 Proposed Cardiac Big Data Analysis Framework -- 3.1 ECG/SCG Data Collection Framework -- 3.2 Data Processing and Analysis Framework -- 3.3 MapReduce Based Cardiac Big Data Processing Model -- 4 Evaluation Results -- 5 Conclusion and Future Works -- References -- A Workload-Aware Data Placement Scheme for Hadoop-Enabled MapReduce Cloud Data Centers -- 1 Introduction -- 2 Related Works -- 3 Problem Description -- 4 Proposed Protocol -- 4.1 System Model -- 5 Problem Formulation -- 5.1 Network Model -- 5.2 Task Processing Model -- 5.3 Workload Distribution -- 6 Data Locality Problem -- 7 Conclusion and Future Works -- References -- 5G Enabled Smart City Using Cloud Environment -- 1 Introduction -- 2 Technologies Used to Build the Smart City -- 2.1 Edge and Fog Computing -- 2.2 What Price Does 5G Provide for Fog Computing? -- 2.3 Cloud Computing -- 2.4 Internet of Things. ; 3 SmartCity Architecture.
• Titolo autorizzato: Predictive Analytics in Cloud, Fog, and Edge Computing
• ISBN: 3-031-18034-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996547962603316
• Lo trovi qui: Univ. di Salerno