Kooperativer Bibliotheksverbund

UID:

Format: 1 online resource (339 pages)

Edition: 1st ed.

ISBN: 9781000057355

Content: This book is presents academic and practical research in IoT and big data. With contributions from both practitioners and academic researchers, the book examines new technology and compares it to existing technology. Experimental case studies are related to real-time scenarios

Note: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Author Biography -- Contributors -- 1 Taxonomy of Big Data and Analytics Solutions for Internet of Things -- 1.1 Introduction -- 1.1.1 IoT Emergence -- 1.1.2 IoT Architecture -- 1.1.2.1 Three Layers of IoT -- 1.1.2.2 IoT Devices -- 1.1.2.3 Cloud Server -- 1.1.2.4 End User -- 1.1.3 IoT Challenges -- 1.1.4 IoT Opportunities -- 1.1.4.1 IoT and the Cloud -- 1.1.4.2 IoT and Security -- 1.1.4.3 IoT at the Edge -- 1.1.4.4 IoT and Integration -- 1.1.5 IoT Applications -- 1.1.5.1 Real-Time Applications of IoT -- 1.1.6 Big Data and Analytics Solutions for IoT -- 1.1.6.1 Big Data in IoT -- 1.1.6.2 Big Data Challenges -- 1.1.6.3 Different Patterns of Data -- 1.7 Big Data Sources -- 1.7.1 Media -- 1.7.2 Business Data -- 1.7.2.1 Customer's Details -- 1.7.2.2 Transaction Details -- 1.7.2.3 Interactions -- 1.7.3 IoT Data -- 1.8 Big Data System Components -- 1.8.1 Data Acquisition (DAQ) -- 1.8.2 Data Retention -- 1.8.3 Data Transportation -- 1.8.4 Data Processing -- 1.8.5 Data Leverage -- 1.9 Big Data Analytics Types -- 1.9.1 Predictive Analytics -- 1.9.1.1 What Will Happen If ...? -- 1.9.2 Descriptive Analytics -- 1.9.2.1 What Has Happened? -- 1.9.3 Diagnostic Analytics -- 1.9.3.1 Why Did It Happen? -- 1.9.3.2 Real-Time Example -- 1.9.4 Prescriptive Analytics -- 1.9.4.1 What Should We Do about This? -- 1.10 Big Data Analytics Tools -- 1.10.1 Hadoop -- 1.10.1.1 Features of Hadoop -- 1.10.2 Apache Spark -- 1.10.3 Apache Storm -- 1.10.4 NoSQL Databases -- 1.10.5 Cassandra -- 1.10.6 RapidMiner -- 1.11 Conclusion -- References -- 2 Big Data Preparation and Exploration -- 2.1 Understanding Original Data Analysis -- 2.2 Benefits of Big Data Pre-Processing -- 2.3 Data Pre-Processing and Data Wrangling Techniques for IoT -- 2.3.1 Data Pre-Processing , 2.3.2 Steps Involved in Data Pre-Processing -- 2.3.3 Typical Use of Data Wrangling -- 2.3.4 Data Wrangling versus ETL -- 2.3.5 Data Wrangling versus Data Pre-Processing -- 2.3.6 Major Challenges in Data Cleansing -- 2.4 Challenges in Big Data Processing -- 2.4.1 Data Analysis -- 2.4.2 Countermeasures for Big-Data-Related Issues -- 2.4.2.1 Increasing Collection Coverage -- 2.4.2.2 Dimension Reduction and Processing Algorithms -- 2.5 Opportunities of Big Data -- 2.5.1 Big Data in Biomedical Image Processing -- 2.5.2 Big Data Opportunity for Genome -- References -- 3 Emerging IoT-Big Data Platform Oriented Technologies -- 3.1 Introduction -- 3.2 Ubiquitous Wireless Communication -- 3.2.1 Ubiquitous Computing -- 3.2.1.1 Ubiquitous Architecture -- 3.2.1.2 Communication Technologies -- 3.2.1.3 Applications -- 3.3 Real-Time Analytics: Overview -- 3.3.1 Challenges in Real-Time Analytics -- 3.3.2 Real-Time Analytics Platforms -- 3.4 Cloud Computing -- 3.4.1 Cloud Computing Era -- 3.4.2 Relationship between IoT and Cloud -- 3.4.3 Relationship between Big Data and Cloud -- 3.4.4 Convergence of IoT, Big Data, and Cloud Computing -- 3.5 Machine Learning -- 3.5.1 Introduction to Machine Learning with Big Data and IoT -- 3.5.2 Evaluation of Machine Learning Models -- 3.5.2.1 Holdout -- 3.5.2.2 Cross-Validation -- 3.5.3 Machine Learning and Big Data Applications -- 3.5.3.1 Machine Leaning Applications -- 3.5.3.2 Big Data Applications -- 3.6 Deep Learning -- 3.6.1 Applying Deep Learning into Big Data -- 3.6.1.1 Semantic Indexing -- 3.6.1.2 Performing Discriminative Tasks -- 3.6.1.3 Semantic Multimedia Tagging -- 3.6.2 Deep Learning Algorithms -- 3.6.3 Deep Learning Applications in IoT - Foundational Services -- References , 4 IoT-Big Data Systems (IoTBDSs) Enabling Technologies: Ubiquitous Wireless Communication, Real-Time Analytics, Machine Learning, Deep Learning, Commodity Sensors -- 4.1 Internet of Things -- 4.1.1 Fundamentals of IoT -- 4.1.2 IoT Framework and Its Working -- 4.1.2.1 Sensing Layer -- 4.1.2.2 Network Layer -- 4.1.2.3 Application Support Layer -- 4.1.2.4 Application Layer -- 4.1.3 Real-Time Applications of IoT -- 4.1.4 Challenges Involved in IoT Deployment -- 4.1.4.1 Security -- 4.1.4.2 Privacy -- 4.1.4.3 Software Complexity -- 4.1.4.4 Flexibility -- 4.1.4.5 Compliance -- 4.1.4.6 Unforeseeable Response -- 4.2 Big Data Analytics -- 4.2.1 Data Science and Big Data -- 4.2.2 Technologies Involved in Big Data -- 4.2.3 Applications of Big Data -- 4.2.4 Opportunities and Issues in Big Data Analytics -- 4.2.4.1 Advantages of Big Data Analytics -- 4.2.4.2 Issues in Big Data Analytics -- 4.3 Wireless Communication and IoT -- 4.3.1 Introduction -- 4.3.2 Architecture of Wireless Communication -- 4.3.3 Modern Ubiquitous Wireless Communication with IoT -- 4.3.4 Implementation of Communication Models for IoT -- 4.4 Machine and Deep Learning Techniques for Wireless IoT Big Data Analytics -- 4.4.1 Introduction to Machine and Deep Learning -- 4.4.1.1 Design Considerations in Machine Learning -- 4.4.1.2 Design Considerations of Deep Learning -- 4.4.2 Machine and Deep Learning Methods -- 4.4.3 Utilization of Learning Methods for IoT Big Data Analysis -- 4.4.4 Applications of IoT with Big Data Analytics in Wireless Mode -- 4.4.4.1 Smart Cities -- 4.4.4.2 Transport Sector -- 4.4.4.3 Weather Forecasting -- 4.4.4.4 Agriculture -- 4.4.4.5 Healthcare -- 4.5 Conclusion -- References -- 5 Distinctive Attributes of Big Data Platform and Big Data Analytics Software for IoT -- 5.1 Introduction -- 5.2 Data Ingestion -- 5.3 Typical Issues of Knowledge Consumption , 5.4 Features Required for Data Ingestion Tools -- 5.5 Big Data Management -- 5.6 ETL -- 5.6.1 History -- 5.6.2 How the ETL Method Works -- 5.6.3 ETL Challenges -- 5.6.3.1 Challenge #1 -- 5.6.3.2 Challenge #2 -- 5.6.3.3 Challenge #3 -- 5.6.4 Types of ETL Tools -- 5.7 Data Warehouse -- 5.8 Related Systems -- 5.8.1 Benetfis -- 5.9 Hadoop -- 5.9.1 History -- 5.9.2 Core Hadoop Elements -- 5.10 Stream Computing -- 5.10.1 Why Is the Streaming Process Needed? -- 5.10.2 How to Do Stream Processing? -- 5.11 Data Analytics -- 5.11.1 Types of Data Analytics -- 5.11.2 Working with Massive Data Analytics -- 5.11.3 Tools in Data Analytics -- 5.12 Machine Learning -- 5.13 Supervised Learning -- 5.13.1 Steps -- 5.13.2 Unsupervised Learning -- 5.14 Content Management -- 5.15 Content Management Process -- 5.16 Content Governance -- 5.16.1 Types of Digital Content Management -- 5.17 Content Management Systems and Tools -- 5.18 Data Integration -- 5.19 Data Governance -- 5.19.1 Data Governance Implementation -- 5.20 Data Stewardship -- References -- 6 Big Data Architecture for IoT -- 6.1 Introduction -- 6.2 IoT with Big Data Characteristics -- 6.3 IoT Reference Architecture -- 6.3.1 Big Data Components -- 6.3.2 IoT Architecture Layers Mapping to Big Data Components -- 6.4 Device Connectivity Options -- 6.4.1 Communication between IoT Devices and Internet -- 6.4.2 Communication between IoT Devices and Gateway -- 6.5 Device Stores -- 6.5.1 IoT Impacts on Storage -- 6.5.1.1 Storage Implications -- 6.5.1.2 Data Center Impact -- 6.6 Device Identity -- 6.6.1 Identity Management of IoT -- 6.7 Registry and Data Stores -- 6.7.1 Data Life Cycle Management in IoT for Device Data Stores -- 6.7.2 Data Management Framework for IoT Device Data Stores -- 6.8 Device Provisioning -- 6.9 Stream Processing and Processing -- 6.9.1 Stream Processing Architecture for IoT Applications , 6.10 High-Scale Compute Models -- 6.11 Corton Intelligence Suite Use Cases -- References -- 7 Algorithms for Big Data Delivery over Internet of Things -- 7.1 Introduction -- 7.2 Wireless Sensor Network -- 7.3 Ecosystem -- 7.4 Protocols for IoT -- 7.4.1 Data Link Protocol -- 7.4.1.1 IEEE 802.15.4 -- 7.4.1.2 IEEE 802.11ah -- 7.4.1.3 ZigBee Smart Energy -- 7.4.1.4 WirelessHART -- 7.4.1.5 LoRaWAN -- 7.4.1.6 Weightless -- 7.4.1.7 Z-Wave -- 7.4.1.8 EnOcean -- 7.5 Network Layer Protocols -- 7.5.1 Network Layer Routing Protocols -- 7.5.1.1 Routing Protocol for Low-Power and Lossy Networks (RPL) -- 7.5.1.2 Cognitive RPL (CORPL) -- 7.5.1.3 Channel-Aware Routing Protocol (CARP) and E-CARP -- 7.5.2 Network Layer Encapsulation Protocols -- 7.5.2.1 IPv6 over Low-Power Wireless Personal Area Network (6LoWPAN) -- 7.5.2.2 6TiSCH -- 7.6 Session Layer Protocols -- 7.6.1 MQTT -- 7.6.2 SMQTT -- 7.6.3 AMQP -- 7.6.4 DDS -- 7.7 Secure Communication in IoT and Big Data -- 7.8 Conclusion -- References -- 8 Big Data Storage Systems for IoT - Perspectives and Challenges -- 8.1 Introduction -- 8.1.1 Data Processing in IoT -- 8.2 Big Data Analytics for IoT -- 8.2.1 What Is Big Data? -- 8.2.1.1 Velocity of Data -- 8.2.1.2 Variety of Data -- 8.2.1.3 Veracity of Data -- 8.2.2 Need for Data Analytics in Big Data -- 8.2.3 Role of Big Data Analytics in IoT -- 8.2.4 Architecture of Big Data Analytics in IoT -- 8.3 Data Storage and Access for IoT -- 8.3.1 Distributed Storage Systems -- 8.3.2 NoSQL Databases -- 8.3.3 Data Integration and Virtualization -- 8.4 Dynamic-Data Handling in Big Data Storage Systems -- 8.4.1 Dynamic Data Assimilation Mechanisms -- 8.4.2 Interpolation Techniques -- 8.5 Heterogeneous Datasets in IoT Big Data -- 8.5.1 Data Cleaning for Heterogeneous Data -- 8.5.2 Data Integration -- 8.5.2.1 Dataset Annotation -- 8.5.2.2 Code Mapping -- 8.5.2.3 Data Linking , 8.5.2.4 Resource Description Framework

Additional Edition: Print version Raj, Pethuru The Internet of Things and Big Data Analytics Milton : Auerbach Publishers, Incorporated,c2020 ISBN 9780367342890

Language: English

Keywords: Electronic books ; Electronic books

URL: FULL  ((OIS Credentials Required))

URL: FULL  ((OIS Credentials Required))