Computationally Intensive Statistics for Intelligent IoT (Studies in Autonomic, Data-driven and Industrial Computing)

✍ Scribed by Debabrata Samanta, Amit Banerjee

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 233
• Category: Library

✦ Synopsis

The book covers computational statistics, its methodologies and applications for IoT device. It includes the details in the areas of computational arithmetic and its influence on computational statistics, numerical algorithms in statistical application software, basics of computer systems, statistical techniques, linear algebra and its role in optimization techniques, evolution of optimization techniques, optimal utilization of computer resources, and statistical graphics role in data analysis. It also explores computational inferencing and computer model's role in design of experiments, Bayesian analysis, survival analysis and data mining in computational statistics.

✦ Table of Contents

Preface
Acknowledgments
Contents
About the Authors
1 Introduction to Intelligent IoT
1.1 Introduction
1.2 Background of Intelligent IoT
1.3 Big Data with Intelligent IoT
1.3.1 Big Data Analytics in IoT
1.4 Conclusion
References
2 ML and Information Advancement Platform in Intelligent IoT
2.1 Introduction
2.2 Training Through Contact with Humans and Machines
2.3 Epistemologies
2.4 A Computer Awareness System
2.5 Case Studies
2.5.1 Initialization
2.5.2 Key Information Sharing with Other SOs
2.5.3 Getting Key Knowledge from Supplementary Knowledge
2.5.4 Pioneered Knowledge from Principle Knowledge
2.5.5 Being Secondary Knowledge from Fabricated Information
2.6 Analysis
2.6.1 Latency
2.6.2 Machine-to-Machine Correspondence in NLP
2.6.3 Inconveniences
2.7 Prospective Study
2.8 Improved Cyber Security
2.9 At the Edge, Machine Learning
2.10 Scalability
2.11 Hyperconvergence
2.12 Reaching the Inference
2.13 Conclusion
References
3 Application of Machine Intelligence and Data Science for Intelligent IoT
3.1 Introduction
3.2 The Combination of Intelligent IoT and ML
3.3 Overview of Different Approaches for IoT Analytics
3.3.1 Descriptive Analysis
3.3.2 Predictive Analysis
3.3.3 Prescriptive Analysis
3.3.4 IoT Adaptive Analysis
3.4 Information Analysis Grouping in IoT Established on Technical Foundation
3.4.1 Computation in the Cloud
3.4.2 Edge Computing
3.5 Utilizations of Data in IoT Analysis
3.5.1 Analysis in IoT in Intelligent Transportation
3.5.2 Smart Healthcare IoT Data Analytics
3.5.3 IoT in Agriculture
3.5.4 Analysis of Data in IoT for Energy Applications
3.6 An Essential Analysis for IoT for Data Mining with an Exploration of Information
3.6.1 Translating Information to Data
3.7 The Protocol of the Application Layer
3.8 The Gateway
3.9 Knowledge Derivation from Data
3.10 Interchange of Node-to-Node Information
3.11 Intelligent Artifacts
3.12 Conclusion
References
4 Approaches of Data Analytics in Intelligent Medicare Utilizing IoT
4.1 Introduction
4.2 Literature Survey
4.2.1 Smart-Phone eHealth
4.2.2 For IoT Along with Big Data Age, What Are the Problems and Open Research Issues?
4.2.3 Challenges
4.2.4 IoT System Architecture Recommendations
4.3 Allied Jobs
4.3.1 Electronic Healthcare
4.3.2 Mobile Health
4.3.3 Telephonic Medicine
4.4 Problems of Big Data Protection and Secrecy
4.5 Methods and Algorithms in Computer Science
4.5.1 Classification
4.5.2 Clustering
4.5.3 Minimizing Dimensions
4.5.4 Identifying Irregularities
4.5.5 Ensemble Strategies
4.6 The Methods and Strategies of Big Data Analytics
4.6.1 Big Data Systems
4.6.2 Big Data Appliances and Strategies
4.7 Intelligent Healthcare
4.7.1 Healthcare Tracking Biomedical Sensors
4.7.2 Big Data Platform for Intelligent Healthcare
4.7.3 Case Analysis
4.8 Results and Discussion
4.8.1 Gathering of Data
4.8.2 The Configuration of Internet of Things Framework
4.8.3 Information Pre-Processing Planning for Information Analysis
4.8.4 The Cleaning of Evidence
4.8.5 Pre-Processing
4.8.6 Range of Characteristics
4.8.7 Big Data Collection Research
4.9 Experimental Review Along with Findings
4.10 Conclusions
References
5 Trends and Applications of Intelligent IoT in Agriculture
5.1 Introduction
5.2 Internet of Things Ecosystem
5.2.1 Internet of Things (IoT) Devices
5.2.2 Information and Transmission Automation
5.2.3 Internet
5.2.4 Computer Management and Retrieval Units
5.2.5 Inference
5.3 Intellectual Property in Agriculture
5.3.1 Observation
5.3.2 Tracing and Monitoring
5.3.3 Farming Equipments
5.3.4 Farming Precision
5.4 Internet of Things and Big Data Analysis in Farming
5.4.1 Forecasting
5.4.2 Storage Administration
5.4.3 Determination
5.4.4 Farming Administration
5.4.5 Accurate Implementation
5.4.6 Insurance
5.5 Benefits
5.6 Research Methodology
5.6.1 Data Collection Using Sensors for Different Parameters
5.6.2 Monitoring the Data Process on a Regular Basis
5.7 Data Visualization and Analysis
5.8 Hosting and Connection
5.9 System Architecture Proposed
5.10 Main Challenges and Open Issues
5.10.1 Commercial Concerns
5.10.2 Technical Concerns
5.10.3 Sectoral Issues
5.10.4 Conclusion
5.11 Future Opportunities and Trends
5.11.1 Scientific Progress
5.11.2 Scenarios for Implementation
5.11.3 Marketability Along with Business
5.12 Conclusion
References
6 Transformation of Intelligent IoT in the Energy Sector
6.1 Introduction
6.1.1 Concepts
6.1.2 Motivation
6.1.3 Methodologies for Testing
6.2 Internet of Things
6.3 The Breakthroughs that Would Make It Possible
6.3.1 Instruments That Are Pleasant to Listen to
6.3.2 Actuators Are Automated Systems That Keep Track of the Movement of Information
6.3.3 Technology That Allows for Communication
6.3.4 Data and Computation in the Internet of Things
6.3.5 The Term “Cloud Computing” Indicates a Form of Computation That Makes Use of the Internet
6.4 The Energy Sector and the Internet of Things
6.4.1 Power Production Along with the Internet of Things
6.4.2 Intelligent Cities
6.4.3 The Smart Grid is an Interactive System That Enables You to Keep Track of Your Energy Use
6.4.4 Intelligent Building
6.4.5 Industrial Intelligent Power Application
6.4.6 Transportation That Is Intelligent
6.5 Confrontations in Soliciting IoT
6.5.1 Consumption of Electricity
6.5.2 Subsystems of the Internet of Things (IoT) Are Being Incorporated
6.5.3 Security of the Consumer
6.5.4 Apprehensions Concerning Protection
6.5.5 The Internet of Things (IoT) Has Developed a Series of Principles
6.5.6 IoT Networks Connect an Increasing Number of Smart Integrated Devices and Sensors
6.5.7 Transmission System Primary Variable Sensing and Actuation
6.6 Future Possibilities
6.6.1 The Internet of Things (IoT) and Blockchain
6.6.2 IoT Power Use That Is Long-Term and Economically Friendly
6.7 Conclusion
References
7 Abnormality Diagnosis from Ambient Data: IoT Data Sequences in Real Time
7.1 Introduction
7.2 Properties of the Device and the Application
7.3 Implementations Versus Machines
7.4 Hardware and Software Framework for the IoT
7.5 Efficiencies at Various Stages
7.5.1 Data Collection
7.5.2 Information Processing
7.5.3 Information Repository
7.5.4 Transmission of Data
7.5.5 Different Computing Layers
7.6 Exact Versus Approximate Computing
7.7 Electronic Design Automation (EDA) Tools for the IoT
7.8 A Foundation for Implementing Sensor Information Sequences
7.8.1 Layer Called Content
7.8.2 Layer of Resources
7.8.3 Message Provider in Real Time
7.8.4 Streaming Sensor Data
7.8.5 Brokering Policy
7.8.6 Algorithm Implementation
7.9 Performance Distribution
7.10 Application Layer
7.10.1 The CUSUM Algorithm is Used to Track Real-Time Events
7.10.2 Prior Implementations in the CUSUM Approach
7.10.3 The CUSUM Algorithm is Defined
7.11 A Device for Identifying Anomalies in the Atmosphere
7.11.1 Layer of Material
7.11.2 Layer of Resources
7.11.3 Message Provider in Real Time
7.11.4 Streaming Sensor Results
7.11.5 STORM Topology
7.12 Case Management Dashboard
7.13 The Proposed Approach is Discussed
7.13.1 Sensor Internetworks as a Case Study
7.13.2 Expanding the Model to Other Contexts
7.13.3 Identification of Irregularities
7.14 Function that is Connected
7.15 Conclusion
References
8 Future of Intelligent IoT
8.1 Introduction
8.2 IoT Versus Cloud Computing
8.3 Assessment with Intelligent IoT
8.4 Future Scope for Intelligent IoT
8.5 Conclusion
References
Index