Industrial internet application development :: simplify IIoT development using the elasticity of public cloud and native cloud services.
Industrial Internet is the integration of complex physical machines and networked sensors and software. Increasing the number of sensors in industrial equipment is going to increase the data being captured, which needs to be analyzed. This book is a one-stop guide for software professionals to desig...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Industrial Internet is the integration of complex physical machines and networked sensors and software. Increasing the number of sensors in industrial equipment is going to increase the data being captured, which needs to be analyzed. This book is a one-stop guide for software professionals to design, build, manage, and operate IIoT applications. |
| Beschreibung: | 1 online resource (405 pages) |
| ISBN: | 9781788297585 178829758X |
Internformat
MARC
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| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: IIoT Fundamentals and Components -- IIoT fundamentals and components -- Impact of the IoT -- Overview of the IoT technology components -- IoT business models -- How the IoT changes business models -- IIoT use cases -- Healthcare -- Manufacturing -- Aviation (quality control) -- Summary -- Chapter 2: IIoT Application Architecture and Design -- IIoT applications -- an introduction -- The challenges of building an IIoT application -- IIoT system architecture -- Tier 1 -- IIoT machines and sensors -- Tier 2 -- Edge Gateway and cloud connectivity -- Edge Gateway -- Cloud Connectivity -- MQTT Communication -- WebSocket communication -- MQTT over WebSockets -- Event/Message hub-based connectivity -- Tier 3 -- Cloud (IIoT application, data, and analytics) -- Microservice-based application design for IIoT cloud applications -- Platform as a Service (PaaS) -- Overview of Cloud Foundry -- The Hello world application using Cloud Foundry -- Data design for IIoT applications -- Data ingestion -- Timeseries/telemetry data -- The in-memory, Blob, and OLTP data stores -- Analytics for IIoT -- Descriptive analytics -- insight into device health -- Predictive analytics -- understanding the future failure modes of the device -- Prescriptive analytics: advice on possible outcomes -- The anatomy of our first IIoT application -- Edge Gateway triggering alerts -- Cloud connectivity using WebSockets -- Cloud microservices aggregating the alerts -- IIoT/IoT platforms overview -- Predix IIoT architecture -- AWS IoT application architecture -- Google IoT application architecture -- Summary -- Chapter 3: IIoT Edge Development -- Hardware for prototypes -- Variety and cost -- Modifications -- Comparing options -- Supported sensors -- Choosing hardware. | |
| 505 | 8 | |a Community -- Choosing a data exchange protocol -- Application-level protocols -- HTTP -- Assembling a device -- Preparing an SD card -- Running a sensor application on an RPi -- Running a receiver application on a PC -- Application-level protocols -- WebSocket -- Assembling a device -- Preparing an SD card -- Running a sensor application on an RPi -- Running a receiver application on a PC -- Industrial M2M protocols -- Modbus -- Preparing an SD card -- Running a hub application on an RPi -- Running a simulator application on an RPi -- Running a receiver application on a PC -- Running a receiver application in Predix -- Industrial M2M protocols -- OPC UA -- Preparing an SD card -- Running a simulator application on an RPi -- Running a receiver application on a PC -- Running a receiver application in Predix -- Running a hub application on an RPi -- Getting statistics -- Data management options in Predix -- Asset -- Event Hub -- Time series -- Database as a Service -- Blobstore -- Message Queue -- Predix cache -- Predix Functions -- Predix Message Queue -- Predix-Search -- Predix Insights -- Predix Columnar Store -- Summary -- Chapter 4: Data for IIoT -- Data for IIoT -- Challenges in handling IIoT data -- Data architecture for IIoT -- Technology stack to handle data for IIoT -- Best practices and standards -- Sample code and frameworks for handling data -- Summary -- Chapter 5: Advanced Analytics for the IIoT -- IIoT business use cases and analytics -- Power plant performance using heat rate -- Manufacturing process -- IIoT analytics types -- Reliable analytics -- Efficient analytics -- Profitable analytics -- Digital twins -- What-if -- analysis and simulations -- Recommendation, notifications, and alarms -- Analytics catalog and market opportunity -- IIoT analytics -- cloud and edge -- Cloud-based analytics -- Edge-based analytics. | |
| 505 | 8 | |a Cloud and edge-analytics combined -- IIoT data for analytics -- Time series data -- Asset data -- Process, recipes, and steps -- Manufacturing Execution System (MES) data -- IIoT analytics -- architecture -- Big data and analytics -- technology stack -- Automation and cloud provisioning -- Big data and analytics -- architecture -- Data ingestion -- Data streaming -- Data computing -- Data persistence -- Data search -- Applications -- Analytics definition -- Streaming and batch analytics -- Event-driven analytics -- ETL pipelines -- Analytics orchestration -- Advanced analytics -- artificial intelligence, machine learning, and deep learning -- Building a model -- Exploratory data analysis -- Analytics life cycle -- Machine learning model life cycle -- Training a model -- Testing a model -- Validating a model -- Predictions using a model -- Retraining a model -- Model performance -- Hypertuning parameters, or the optimization of model parameters -- Model performance metrics -- Determining outliers and offset management -- Continuous training of a model -- ML pipelines and orchestration -- IIoT data -- ETL -- Feature extraction process -- Model generation process -- Storing the model -- Developing an ML pipeline -- IIoT data variety -- Spatial analytics -- Image analysis -- Acoustics -- based analytics -- Machine learning types -- Supervised learning -- Unsupervised learning -- PMML for predictive analytics -- Event -- driven machine learning model -- Event -- driven model architecture -- Building models in offline mode -- Reference architecture -- Real-time model tuning and deployment -- Machine learning as a service -- Creating an ML model endpoint -- Step 1 -- Step 2 -- Step 3 -- Containerization of machine learning models -- Legacy analytics and challenges -- Containerization for legacy analytics -- Data for legacy analytics. | |
| 505 | 8 | |a Analytic Orchestration -- Architecture -- Analytics orchestration -- Data flow -- Pros and cons of this approach -- Time series data-based analytics -- Windows-based calculations -- Forecasting of time series data points -- Developing a neural network using Keras and TensorFlow using Jupyter -- Environment setup -- Developing the neural network -- Developing an analytics for analyzing time series data using Spark -- Environment setup -- Creating a Spark-based Notebook and creating the Spark session -- Developing streaming analytics using Spark -- Environment setup -- Developing the streaming analytics -- Summary -- Chapter 6: Developing Your First Application for IIoT -- Developing and modeling assets using the S95 standard -- ISA-95 control levels -- Exchange of asset data as represented in S95 -- Selecting a storage -- Relational DBMS -- Key-value stores -- Advanced forms -- Document stores -- Graph DBMS -- Time series DBMS -- RDF stores -- Object-oriented DBMS -- Search engines -- MultiValue DBMS -- Wide column stores -- Native XML DBMS -- Content stores -- Event stores -- Navigational DBMS -- Blockchain -- Important considerations -- Time series storage -- Using InfluxDB as a time series storage -- Creating instances of assets and adding time series data -- Understanding the analytics -- Exploring descriptive analytics with InfluxDB -- Example -- count the field values associated with a field key -- Example -- calculate the mean field value associated with a field key -- Deploying your first analytics -- Examples of queries with InfluxDB analytical functions -- Example -- select all fields and tags from a single measurement -- Example -- group query results by a single tag -- Running a query -- Visualizing time series data and charts -- Visualizing time series data with Highcharts -- Visualizing time series data with Grafana -- Grafana building blocks. | |
| 505 | 8 | |a Configuring a Grafana visualization -- Graph panel -- Visualizing the outcomes of the analytics as alerts -- Configuring email notifications -- Configuring notifications via Slack -- Configuring alerts in Grafana -- Summary -- Chapter 7: Deployment, Scale, and Security -- IIoT security practices -- Key principles of securing IIoT applications -- Phase 1 -- third-party and architecture risk assessments -- Phase 2 -- technical security assessments -- Static analysis security testing (SAST) -- Dynamic analysis security testing (DAST) -- Open source scans -- Phase 3 -- secure by design -- Phase 4 -- penetration testing -- IIoT device security design and architecture -- IIoT device and IIoT device management -- IIoT device communication and privacy controls -- IIoT device communication and encryption -- IIoT device user privacy controls -- IIoT device placement in the network -- IIoT Gateway security principles -- TPM -- TEE -- IIoT Gateway network security -- IIoT Gateway authentication -- IIoT cloud security architecture and design -- IIoT API security -- IIoT access control -- IIoT identity store -- IIoT security analytics -- IIoT application deployment -- IIoT applications at scale -- Capacity planning -- Testing for load/performance -- Measure and identify bottlenecks -- Scale individual components -- X-scaling or horizontal duplication -- Y-axis scaling -- Z-axis scaling -- Summary -- Chapter 8: Reliability, Fault Tolerance, and Monitoring IIoT Applications -- Complexity of an IIoT system -- Art of building reliable and resilient IIoT applications -- Designing for reliability on the cloud -- Programming for network latency using the circuit breaker pattern -- Issues and considerations -- When to use this pattern -- Example -- Handling for bandwidth constraints and transport costs using the API Gateway pattern -- Issues and considerations. | |
| 520 | |a Industrial Internet is the integration of complex physical machines and networked sensors and software. Increasing the number of sensors in industrial equipment is going to increase the data being captured, which needs to be analyzed. This book is a one-stop guide for software professionals to design, build, manage, and operate IIoT applications. | ||
| 650 | 0 | |a Computer software |x Development. |0 http://id.loc.gov/authorities/subjects/sh85029535 | |
| 650 | 0 | |a Application software. |0 http://id.loc.gov/authorities/subjects/sh90001980 | |
| 650 | 0 | |a Information technology |x Management. |0 http://id.loc.gov/authorities/subjects/sh2008006980 | |
| 650 | 6 | |a Logiciels d'application. | |
| 650 | 6 | |a Technologie de l'information |x Gestion. | |
| 650 | 7 | |a Computer programming |x software development. |2 bicssc | |
| 650 | 7 | |a Network security. |2 bicssc | |
| 650 | 7 | |a Web programming. |2 bicssc | |
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| 650 | 7 | |a Application software |2 fast | |
| 650 | 7 | |a Computer software |x Development |2 fast | |
| 650 | 7 | |a Information technology |x Management |2 fast | |
| 700 | 1 | |a Thomas, Jayant. | |
| 700 | 1 | |a Tyagi, Prashant. | |
| 700 | 1 | |a Reddipalli, Kishore. | |
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| contents | Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: IIoT Fundamentals and Components -- IIoT fundamentals and components -- Impact of the IoT -- Overview of the IoT technology components -- IoT business models -- How the IoT changes business models -- IIoT use cases -- Healthcare -- Manufacturing -- Aviation (quality control) -- Summary -- Chapter 2: IIoT Application Architecture and Design -- IIoT applications -- an introduction -- The challenges of building an IIoT application -- IIoT system architecture -- Tier 1 -- IIoT machines and sensors -- Tier 2 -- Edge Gateway and cloud connectivity -- Edge Gateway -- Cloud Connectivity -- MQTT Communication -- WebSocket communication -- MQTT over WebSockets -- Event/Message hub-based connectivity -- Tier 3 -- Cloud (IIoT application, data, and analytics) -- Microservice-based application design for IIoT cloud applications -- Platform as a Service (PaaS) -- Overview of Cloud Foundry -- The Hello world application using Cloud Foundry -- Data design for IIoT applications -- Data ingestion -- Timeseries/telemetry data -- The in-memory, Blob, and OLTP data stores -- Analytics for IIoT -- Descriptive analytics -- insight into device health -- Predictive analytics -- understanding the future failure modes of the device -- Prescriptive analytics: advice on possible outcomes -- The anatomy of our first IIoT application -- Edge Gateway triggering alerts -- Cloud connectivity using WebSockets -- Cloud microservices aggregating the alerts -- IIoT/IoT platforms overview -- Predix IIoT architecture -- AWS IoT application architecture -- Google IoT application architecture -- Summary -- Chapter 3: IIoT Edge Development -- Hardware for prototypes -- Variety and cost -- Modifications -- Comparing options -- Supported sensors -- Choosing hardware. Community -- Choosing a data exchange protocol -- Application-level protocols -- HTTP -- Assembling a device -- Preparing an SD card -- Running a sensor application on an RPi -- Running a receiver application on a PC -- Application-level protocols -- WebSocket -- Assembling a device -- Preparing an SD card -- Running a sensor application on an RPi -- Running a receiver application on a PC -- Industrial M2M protocols -- Modbus -- Preparing an SD card -- Running a hub application on an RPi -- Running a simulator application on an RPi -- Running a receiver application on a PC -- Running a receiver application in Predix -- Industrial M2M protocols -- OPC UA -- Preparing an SD card -- Running a simulator application on an RPi -- Running a receiver application on a PC -- Running a receiver application in Predix -- Running a hub application on an RPi -- Getting statistics -- Data management options in Predix -- Asset -- Event Hub -- Time series -- Database as a Service -- Blobstore -- Message Queue -- Predix cache -- Predix Functions -- Predix Message Queue -- Predix-Search -- Predix Insights -- Predix Columnar Store -- Summary -- Chapter 4: Data for IIoT -- Data for IIoT -- Challenges in handling IIoT data -- Data architecture for IIoT -- Technology stack to handle data for IIoT -- Best practices and standards -- Sample code and frameworks for handling data -- Summary -- Chapter 5: Advanced Analytics for the IIoT -- IIoT business use cases and analytics -- Power plant performance using heat rate -- Manufacturing process -- IIoT analytics types -- Reliable analytics -- Efficient analytics -- Profitable analytics -- Digital twins -- What-if -- analysis and simulations -- Recommendation, notifications, and alarms -- Analytics catalog and market opportunity -- IIoT analytics -- cloud and edge -- Cloud-based analytics -- Edge-based analytics. Cloud and edge-analytics combined -- IIoT data for analytics -- Time series data -- Asset data -- Process, recipes, and steps -- Manufacturing Execution System (MES) data -- IIoT analytics -- architecture -- Big data and analytics -- technology stack -- Automation and cloud provisioning -- Big data and analytics -- architecture -- Data ingestion -- Data streaming -- Data computing -- Data persistence -- Data search -- Applications -- Analytics definition -- Streaming and batch analytics -- Event-driven analytics -- ETL pipelines -- Analytics orchestration -- Advanced analytics -- artificial intelligence, machine learning, and deep learning -- Building a model -- Exploratory data analysis -- Analytics life cycle -- Machine learning model life cycle -- Training a model -- Testing a model -- Validating a model -- Predictions using a model -- Retraining a model -- Model performance -- Hypertuning parameters, or the optimization of model parameters -- Model performance metrics -- Determining outliers and offset management -- Continuous training of a model -- ML pipelines and orchestration -- IIoT data -- ETL -- Feature extraction process -- Model generation process -- Storing the model -- Developing an ML pipeline -- IIoT data variety -- Spatial analytics -- Image analysis -- Acoustics -- based analytics -- Machine learning types -- Supervised learning -- Unsupervised learning -- PMML for predictive analytics -- Event -- driven machine learning model -- Event -- driven model architecture -- Building models in offline mode -- Reference architecture -- Real-time model tuning and deployment -- Machine learning as a service -- Creating an ML model endpoint -- Step 1 -- Step 2 -- Step 3 -- Containerization of machine learning models -- Legacy analytics and challenges -- Containerization for legacy analytics -- Data for legacy analytics. Analytic Orchestration -- Architecture -- Analytics orchestration -- Data flow -- Pros and cons of this approach -- Time series data-based analytics -- Windows-based calculations -- Forecasting of time series data points -- Developing a neural network using Keras and TensorFlow using Jupyter -- Environment setup -- Developing the neural network -- Developing an analytics for analyzing time series data using Spark -- Environment setup -- Creating a Spark-based Notebook and creating the Spark session -- Developing streaming analytics using Spark -- Environment setup -- Developing the streaming analytics -- Summary -- Chapter 6: Developing Your First Application for IIoT -- Developing and modeling assets using the S95 standard -- ISA-95 control levels -- Exchange of asset data as represented in S95 -- Selecting a storage -- Relational DBMS -- Key-value stores -- Advanced forms -- Document stores -- Graph DBMS -- Time series DBMS -- RDF stores -- Object-oriented DBMS -- Search engines -- MultiValue DBMS -- Wide column stores -- Native XML DBMS -- Content stores -- Event stores -- Navigational DBMS -- Blockchain -- Important considerations -- Time series storage -- Using InfluxDB as a time series storage -- Creating instances of assets and adding time series data -- Understanding the analytics -- Exploring descriptive analytics with InfluxDB -- Example -- count the field values associated with a field key -- Example -- calculate the mean field value associated with a field key -- Deploying your first analytics -- Examples of queries with InfluxDB analytical functions -- Example -- select all fields and tags from a single measurement -- Example -- group query results by a single tag -- Running a query -- Visualizing time series data and charts -- Visualizing time series data with Highcharts -- Visualizing time series data with Grafana -- Grafana building blocks. Configuring a Grafana visualization -- Graph panel -- Visualizing the outcomes of the analytics as alerts -- Configuring email notifications -- Configuring notifications via Slack -- Configuring alerts in Grafana -- Summary -- Chapter 7: Deployment, Scale, and Security -- IIoT security practices -- Key principles of securing IIoT applications -- Phase 1 -- third-party and architecture risk assessments -- Phase 2 -- technical security assessments -- Static analysis security testing (SAST) -- Dynamic analysis security testing (DAST) -- Open source scans -- Phase 3 -- secure by design -- Phase 4 -- penetration testing -- IIoT device security design and architecture -- IIoT device and IIoT device management -- IIoT device communication and privacy controls -- IIoT device communication and encryption -- IIoT device user privacy controls -- IIoT device placement in the network -- IIoT Gateway security principles -- TPM -- TEE -- IIoT Gateway network security -- IIoT Gateway authentication -- IIoT cloud security architecture and design -- IIoT API security -- IIoT access control -- IIoT identity store -- IIoT security analytics -- IIoT application deployment -- IIoT applications at scale -- Capacity planning -- Testing for load/performance -- Measure and identify bottlenecks -- Scale individual components -- X-scaling or horizontal duplication -- Y-axis scaling -- Z-axis scaling -- Summary -- Chapter 8: Reliability, Fault Tolerance, and Monitoring IIoT Applications -- Complexity of an IIoT system -- Art of building reliable and resilient IIoT applications -- Designing for reliability on the cloud -- Programming for network latency using the circuit breaker pattern -- Issues and considerations -- When to use this pattern -- Example -- Handling for bandwidth constraints and transport costs using the API Gateway pattern -- Issues and considerations. |
| ctrlnum | (OCoLC)1056907457 |
| dewey-full | 005.1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.1 |
| dewey-search | 005.1 |
| dewey-sort | 15.1 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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Assembling a device -- Preparing an SD card -- Running a sensor application on an RPi -- Running a receiver application on a PC -- Application-level protocols -- WebSocket -- Assembling a device -- Preparing an SD card -- Running a sensor application on an RPi -- Running a receiver application on a PC -- Industrial M2M protocols -- Modbus -- Preparing an SD card -- Running a hub application on an RPi -- Running a simulator application on an RPi -- Running a receiver application on a PC -- Running a receiver application in Predix -- Industrial M2M protocols -- OPC UA -- Preparing an SD card -- Running a simulator application on an RPi -- Running a receiver application on a PC -- Running a receiver application in Predix -- Running a hub application on an RPi -- Getting statistics -- Data management options in Predix -- Asset -- Event Hub -- Time series -- Database as a Service -- Blobstore -- Message Queue -- Predix cache -- Predix Functions -- Predix Message Queue -- Predix-Search -- Predix Insights -- Predix Columnar Store -- Summary -- Chapter 4: Data for IIoT -- Data for IIoT -- Challenges in handling IIoT data -- Data architecture for IIoT -- Technology stack to handle data for IIoT -- Best practices and standards -- Sample code and frameworks for handling data -- Summary -- Chapter 5: Advanced Analytics for the IIoT -- IIoT business use cases and analytics -- Power plant performance using heat rate -- Manufacturing process -- IIoT analytics types -- Reliable analytics -- Efficient analytics -- Profitable analytics -- Digital twins -- What-if -- analysis and simulations -- Recommendation, notifications, and alarms -- Analytics catalog and market opportunity -- IIoT analytics -- cloud and edge -- Cloud-based analytics -- Edge-based analytics.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Cloud and edge-analytics combined -- IIoT data for analytics -- Time series data -- Asset data -- Process, recipes, and steps -- Manufacturing Execution System (MES) data -- IIoT analytics -- architecture -- Big data and analytics -- technology stack -- Automation and cloud provisioning -- Big data and analytics -- architecture -- Data ingestion -- Data streaming -- Data computing -- Data persistence -- Data search -- Applications -- Analytics definition -- Streaming and batch analytics -- Event-driven analytics -- ETL pipelines -- Analytics orchestration -- Advanced analytics -- artificial intelligence, machine learning, and deep learning -- Building a model -- Exploratory data analysis -- Analytics life cycle -- Machine learning model life cycle -- Training a model -- Testing a model -- Validating a model -- Predictions using a model -- Retraining a model -- Model performance -- Hypertuning parameters, or the optimization of model parameters -- Model performance metrics -- Determining outliers and offset management -- Continuous training of a model -- ML pipelines and orchestration -- IIoT data -- ETL -- Feature extraction process -- Model generation process -- Storing the model -- Developing an ML pipeline -- IIoT data variety -- Spatial analytics -- Image analysis -- Acoustics -- based analytics -- Machine learning types -- Supervised learning -- Unsupervised learning -- PMML for predictive analytics -- Event -- driven machine learning model -- Event -- driven model architecture -- Building models in offline mode -- Reference architecture -- Real-time model tuning and deployment -- Machine learning as a service -- Creating an ML model endpoint -- Step 1 -- Step 2 -- Step 3 -- Containerization of machine learning models -- Legacy analytics and challenges -- Containerization for legacy analytics -- Data for legacy analytics.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Analytic Orchestration -- Architecture -- Analytics orchestration -- Data flow -- Pros and cons of this approach -- Time series data-based analytics -- Windows-based calculations -- Forecasting of time series data points -- Developing a neural network using Keras and TensorFlow using Jupyter -- Environment setup -- Developing the neural network -- Developing an analytics for analyzing time series data using Spark -- Environment setup -- Creating a Spark-based Notebook and creating the Spark session -- Developing streaming analytics using Spark -- Environment setup -- Developing the streaming analytics -- Summary -- Chapter 6: Developing Your First Application for IIoT -- Developing and modeling assets using the S95 standard -- ISA-95 control levels -- Exchange of asset data as represented in S95 -- Selecting a storage -- Relational DBMS -- Key-value stores -- Advanced forms -- Document stores -- Graph DBMS -- Time series DBMS -- RDF stores -- Object-oriented DBMS -- Search engines -- MultiValue DBMS -- Wide column stores -- Native XML DBMS -- Content stores -- Event stores -- Navigational DBMS -- Blockchain -- Important considerations -- Time series storage -- Using InfluxDB as a time series storage -- Creating instances of assets and adding time series data -- Understanding the analytics -- Exploring descriptive analytics with InfluxDB -- Example -- count the field values associated with a field key -- Example -- calculate the mean field value associated with a field key -- Deploying your first analytics -- Examples of queries with InfluxDB analytical functions -- Example -- select all fields and tags from a single measurement -- Example -- group query results by a single tag -- Running a query -- Visualizing time series data and charts -- Visualizing time series data with Highcharts -- Visualizing time series data with Grafana -- Grafana building blocks.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Configuring a Grafana visualization -- Graph panel -- Visualizing the outcomes of the analytics as alerts -- Configuring email notifications -- Configuring notifications via Slack -- Configuring alerts in Grafana -- Summary -- Chapter 7: Deployment, Scale, and Security -- IIoT security practices -- Key principles of securing IIoT applications -- Phase 1 -- third-party and architecture risk assessments -- Phase 2 -- technical security assessments -- Static analysis security testing (SAST) -- Dynamic analysis security testing (DAST) -- Open source scans -- Phase 3 -- secure by design -- Phase 4 -- penetration testing -- IIoT device security design and architecture -- IIoT device and IIoT device management -- IIoT device communication and privacy controls -- IIoT device communication and encryption -- IIoT device user privacy controls -- IIoT device placement in the network -- IIoT Gateway security principles -- TPM -- TEE -- IIoT Gateway network security -- IIoT Gateway authentication -- IIoT cloud security architecture and design -- IIoT API security -- IIoT access control -- IIoT identity store -- IIoT security analytics -- IIoT application deployment -- IIoT applications at scale -- Capacity planning -- Testing for load/performance -- Measure and identify bottlenecks -- Scale individual components -- X-scaling or horizontal duplication -- Y-axis scaling -- Z-axis scaling -- Summary -- Chapter 8: Reliability, Fault Tolerance, and Monitoring IIoT Applications -- Complexity of an IIoT system -- Art of building reliable and resilient IIoT applications -- Designing for reliability on the cloud -- Programming for network latency using the circuit breaker pattern -- Issues and considerations -- When to use this pattern -- Example -- Handling for bandwidth constraints and transport costs using the API Gateway pattern -- Issues and considerations.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Industrial Internet is the integration of complex physical machines and networked sensors and software. Increasing the number of sensors in industrial equipment is going to increase the data being captured, which needs to be analyzed. 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| id | ZDB-4-EBA-on1056907457 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:29:11Z |
| institution | BVB |
| isbn | 9781788297585 178829758X |
| language | English |
| oclc_num | 1056907457 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (405 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Packt, |
| record_format | marc |
| spelling | Traukina, Alena. Industrial internet application development : simplify IIoT development using the elasticity of public cloud and native cloud services. Birmingham : Packt, 2018. 1 online resource (405 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from PDF title page (EBSCO, viewed October 17, 2018) Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: IIoT Fundamentals and Components -- IIoT fundamentals and components -- Impact of the IoT -- Overview of the IoT technology components -- IoT business models -- How the IoT changes business models -- IIoT use cases -- Healthcare -- Manufacturing -- Aviation (quality control) -- Summary -- Chapter 2: IIoT Application Architecture and Design -- IIoT applications -- an introduction -- The challenges of building an IIoT application -- IIoT system architecture -- Tier 1 -- IIoT machines and sensors -- Tier 2 -- Edge Gateway and cloud connectivity -- Edge Gateway -- Cloud Connectivity -- MQTT Communication -- WebSocket communication -- MQTT over WebSockets -- Event/Message hub-based connectivity -- Tier 3 -- Cloud (IIoT application, data, and analytics) -- Microservice-based application design for IIoT cloud applications -- Platform as a Service (PaaS) -- Overview of Cloud Foundry -- The Hello world application using Cloud Foundry -- Data design for IIoT applications -- Data ingestion -- Timeseries/telemetry data -- The in-memory, Blob, and OLTP data stores -- Analytics for IIoT -- Descriptive analytics -- insight into device health -- Predictive analytics -- understanding the future failure modes of the device -- Prescriptive analytics: advice on possible outcomes -- The anatomy of our first IIoT application -- Edge Gateway triggering alerts -- Cloud connectivity using WebSockets -- Cloud microservices aggregating the alerts -- IIoT/IoT platforms overview -- Predix IIoT architecture -- AWS IoT application architecture -- Google IoT application architecture -- Summary -- Chapter 3: IIoT Edge Development -- Hardware for prototypes -- Variety and cost -- Modifications -- Comparing options -- Supported sensors -- Choosing hardware. Community -- Choosing a data exchange protocol -- Application-level protocols -- HTTP -- Assembling a device -- Preparing an SD card -- Running a sensor application on an RPi -- Running a receiver application on a PC -- Application-level protocols -- WebSocket -- Assembling a device -- Preparing an SD card -- Running a sensor application on an RPi -- Running a receiver application on a PC -- Industrial M2M protocols -- Modbus -- Preparing an SD card -- Running a hub application on an RPi -- Running a simulator application on an RPi -- Running a receiver application on a PC -- Running a receiver application in Predix -- Industrial M2M protocols -- OPC UA -- Preparing an SD card -- Running a simulator application on an RPi -- Running a receiver application on a PC -- Running a receiver application in Predix -- Running a hub application on an RPi -- Getting statistics -- Data management options in Predix -- Asset -- Event Hub -- Time series -- Database as a Service -- Blobstore -- Message Queue -- Predix cache -- Predix Functions -- Predix Message Queue -- Predix-Search -- Predix Insights -- Predix Columnar Store -- Summary -- Chapter 4: Data for IIoT -- Data for IIoT -- Challenges in handling IIoT data -- Data architecture for IIoT -- Technology stack to handle data for IIoT -- Best practices and standards -- Sample code and frameworks for handling data -- Summary -- Chapter 5: Advanced Analytics for the IIoT -- IIoT business use cases and analytics -- Power plant performance using heat rate -- Manufacturing process -- IIoT analytics types -- Reliable analytics -- Efficient analytics -- Profitable analytics -- Digital twins -- What-if -- analysis and simulations -- Recommendation, notifications, and alarms -- Analytics catalog and market opportunity -- IIoT analytics -- cloud and edge -- Cloud-based analytics -- Edge-based analytics. Cloud and edge-analytics combined -- IIoT data for analytics -- Time series data -- Asset data -- Process, recipes, and steps -- Manufacturing Execution System (MES) data -- IIoT analytics -- architecture -- Big data and analytics -- technology stack -- Automation and cloud provisioning -- Big data and analytics -- architecture -- Data ingestion -- Data streaming -- Data computing -- Data persistence -- Data search -- Applications -- Analytics definition -- Streaming and batch analytics -- Event-driven analytics -- ETL pipelines -- Analytics orchestration -- Advanced analytics -- artificial intelligence, machine learning, and deep learning -- Building a model -- Exploratory data analysis -- Analytics life cycle -- Machine learning model life cycle -- Training a model -- Testing a model -- Validating a model -- Predictions using a model -- Retraining a model -- Model performance -- Hypertuning parameters, or the optimization of model parameters -- Model performance metrics -- Determining outliers and offset management -- Continuous training of a model -- ML pipelines and orchestration -- IIoT data -- ETL -- Feature extraction process -- Model generation process -- Storing the model -- Developing an ML pipeline -- IIoT data variety -- Spatial analytics -- Image analysis -- Acoustics -- based analytics -- Machine learning types -- Supervised learning -- Unsupervised learning -- PMML for predictive analytics -- Event -- driven machine learning model -- Event -- driven model architecture -- Building models in offline mode -- Reference architecture -- Real-time model tuning and deployment -- Machine learning as a service -- Creating an ML model endpoint -- Step 1 -- Step 2 -- Step 3 -- Containerization of machine learning models -- Legacy analytics and challenges -- Containerization for legacy analytics -- Data for legacy analytics. Analytic Orchestration -- Architecture -- Analytics orchestration -- Data flow -- Pros and cons of this approach -- Time series data-based analytics -- Windows-based calculations -- Forecasting of time series data points -- Developing a neural network using Keras and TensorFlow using Jupyter -- Environment setup -- Developing the neural network -- Developing an analytics for analyzing time series data using Spark -- Environment setup -- Creating a Spark-based Notebook and creating the Spark session -- Developing streaming analytics using Spark -- Environment setup -- Developing the streaming analytics -- Summary -- Chapter 6: Developing Your First Application for IIoT -- Developing and modeling assets using the S95 standard -- ISA-95 control levels -- Exchange of asset data as represented in S95 -- Selecting a storage -- Relational DBMS -- Key-value stores -- Advanced forms -- Document stores -- Graph DBMS -- Time series DBMS -- RDF stores -- Object-oriented DBMS -- Search engines -- MultiValue DBMS -- Wide column stores -- Native XML DBMS -- Content stores -- Event stores -- Navigational DBMS -- Blockchain -- Important considerations -- Time series storage -- Using InfluxDB as a time series storage -- Creating instances of assets and adding time series data -- Understanding the analytics -- Exploring descriptive analytics with InfluxDB -- Example -- count the field values associated with a field key -- Example -- calculate the mean field value associated with a field key -- Deploying your first analytics -- Examples of queries with InfluxDB analytical functions -- Example -- select all fields and tags from a single measurement -- Example -- group query results by a single tag -- Running a query -- Visualizing time series data and charts -- Visualizing time series data with Highcharts -- Visualizing time series data with Grafana -- Grafana building blocks. Configuring a Grafana visualization -- Graph panel -- Visualizing the outcomes of the analytics as alerts -- Configuring email notifications -- Configuring notifications via Slack -- Configuring alerts in Grafana -- Summary -- Chapter 7: Deployment, Scale, and Security -- IIoT security practices -- Key principles of securing IIoT applications -- Phase 1 -- third-party and architecture risk assessments -- Phase 2 -- technical security assessments -- Static analysis security testing (SAST) -- Dynamic analysis security testing (DAST) -- Open source scans -- Phase 3 -- secure by design -- Phase 4 -- penetration testing -- IIoT device security design and architecture -- IIoT device and IIoT device management -- IIoT device communication and privacy controls -- IIoT device communication and encryption -- IIoT device user privacy controls -- IIoT device placement in the network -- IIoT Gateway security principles -- TPM -- TEE -- IIoT Gateway network security -- IIoT Gateway authentication -- IIoT cloud security architecture and design -- IIoT API security -- IIoT access control -- IIoT identity store -- IIoT security analytics -- IIoT application deployment -- IIoT applications at scale -- Capacity planning -- Testing for load/performance -- Measure and identify bottlenecks -- Scale individual components -- X-scaling or horizontal duplication -- Y-axis scaling -- Z-axis scaling -- Summary -- Chapter 8: Reliability, Fault Tolerance, and Monitoring IIoT Applications -- Complexity of an IIoT system -- Art of building reliable and resilient IIoT applications -- Designing for reliability on the cloud -- Programming for network latency using the circuit breaker pattern -- Issues and considerations -- When to use this pattern -- Example -- Handling for bandwidth constraints and transport costs using the API Gateway pattern -- Issues and considerations. Industrial Internet is the integration of complex physical machines and networked sensors and software. Increasing the number of sensors in industrial equipment is going to increase the data being captured, which needs to be analyzed. This book is a one-stop guide for software professionals to design, build, manage, and operate IIoT applications. Computer software Development. http://id.loc.gov/authorities/subjects/sh85029535 Application software. http://id.loc.gov/authorities/subjects/sh90001980 Information technology Management. http://id.loc.gov/authorities/subjects/sh2008006980 Logiciels d'application. Technologie de l'information Gestion. Computer programming software development. bicssc Network security. bicssc Web programming. bicssc COMPUTERS Software Development & Engineering General. bisacsh Application software fast Computer software Development fast Information technology Management fast Thomas, Jayant. Tyagi, Prashant. Reddipalli, Kishore. has work: Industrial Internet Application Development (Text) https://id.oclc.org/worldcat/entity/E39PCXBTDG7pYHDwKKWBkmdpHd https://id.oclc.org/worldcat/ontology/hasWork FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1905976 Volltext |
| spellingShingle | Traukina, Alena Industrial internet application development : simplify IIoT development using the elasticity of public cloud and native cloud services. Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: IIoT Fundamentals and Components -- IIoT fundamentals and components -- Impact of the IoT -- Overview of the IoT technology components -- IoT business models -- How the IoT changes business models -- IIoT use cases -- Healthcare -- Manufacturing -- Aviation (quality control) -- Summary -- Chapter 2: IIoT Application Architecture and Design -- IIoT applications -- an introduction -- The challenges of building an IIoT application -- IIoT system architecture -- Tier 1 -- IIoT machines and sensors -- Tier 2 -- Edge Gateway and cloud connectivity -- Edge Gateway -- Cloud Connectivity -- MQTT Communication -- WebSocket communication -- MQTT over WebSockets -- Event/Message hub-based connectivity -- Tier 3 -- Cloud (IIoT application, data, and analytics) -- Microservice-based application design for IIoT cloud applications -- Platform as a Service (PaaS) -- Overview of Cloud Foundry -- The Hello world application using Cloud Foundry -- Data design for IIoT applications -- Data ingestion -- Timeseries/telemetry data -- The in-memory, Blob, and OLTP data stores -- Analytics for IIoT -- Descriptive analytics -- insight into device health -- Predictive analytics -- understanding the future failure modes of the device -- Prescriptive analytics: advice on possible outcomes -- The anatomy of our first IIoT application -- Edge Gateway triggering alerts -- Cloud connectivity using WebSockets -- Cloud microservices aggregating the alerts -- IIoT/IoT platforms overview -- Predix IIoT architecture -- AWS IoT application architecture -- Google IoT application architecture -- Summary -- Chapter 3: IIoT Edge Development -- Hardware for prototypes -- Variety and cost -- Modifications -- Comparing options -- Supported sensors -- Choosing hardware. Community -- Choosing a data exchange protocol -- Application-level protocols -- HTTP -- Assembling a device -- Preparing an SD card -- Running a sensor application on an RPi -- Running a receiver application on a PC -- Application-level protocols -- WebSocket -- Assembling a device -- Preparing an SD card -- Running a sensor application on an RPi -- Running a receiver application on a PC -- Industrial M2M protocols -- Modbus -- Preparing an SD card -- Running a hub application on an RPi -- Running a simulator application on an RPi -- Running a receiver application on a PC -- Running a receiver application in Predix -- Industrial M2M protocols -- OPC UA -- Preparing an SD card -- Running a simulator application on an RPi -- Running a receiver application on a PC -- Running a receiver application in Predix -- Running a hub application on an RPi -- Getting statistics -- Data management options in Predix -- Asset -- Event Hub -- Time series -- Database as a Service -- Blobstore -- Message Queue -- Predix cache -- Predix Functions -- Predix Message Queue -- Predix-Search -- Predix Insights -- Predix Columnar Store -- Summary -- Chapter 4: Data for IIoT -- Data for IIoT -- Challenges in handling IIoT data -- Data architecture for IIoT -- Technology stack to handle data for IIoT -- Best practices and standards -- Sample code and frameworks for handling data -- Summary -- Chapter 5: Advanced Analytics for the IIoT -- IIoT business use cases and analytics -- Power plant performance using heat rate -- Manufacturing process -- IIoT analytics types -- Reliable analytics -- Efficient analytics -- Profitable analytics -- Digital twins -- What-if -- analysis and simulations -- Recommendation, notifications, and alarms -- Analytics catalog and market opportunity -- IIoT analytics -- cloud and edge -- Cloud-based analytics -- Edge-based analytics. Cloud and edge-analytics combined -- IIoT data for analytics -- Time series data -- Asset data -- Process, recipes, and steps -- Manufacturing Execution System (MES) data -- IIoT analytics -- architecture -- Big data and analytics -- technology stack -- Automation and cloud provisioning -- Big data and analytics -- architecture -- Data ingestion -- Data streaming -- Data computing -- Data persistence -- Data search -- Applications -- Analytics definition -- Streaming and batch analytics -- Event-driven analytics -- ETL pipelines -- Analytics orchestration -- Advanced analytics -- artificial intelligence, machine learning, and deep learning -- Building a model -- Exploratory data analysis -- Analytics life cycle -- Machine learning model life cycle -- Training a model -- Testing a model -- Validating a model -- Predictions using a model -- Retraining a model -- Model performance -- Hypertuning parameters, or the optimization of model parameters -- Model performance metrics -- Determining outliers and offset management -- Continuous training of a model -- ML pipelines and orchestration -- IIoT data -- ETL -- Feature extraction process -- Model generation process -- Storing the model -- Developing an ML pipeline -- IIoT data variety -- Spatial analytics -- Image analysis -- Acoustics -- based analytics -- Machine learning types -- Supervised learning -- Unsupervised learning -- PMML for predictive analytics -- Event -- driven machine learning model -- Event -- driven model architecture -- Building models in offline mode -- Reference architecture -- Real-time model tuning and deployment -- Machine learning as a service -- Creating an ML model endpoint -- Step 1 -- Step 2 -- Step 3 -- Containerization of machine learning models -- Legacy analytics and challenges -- Containerization for legacy analytics -- Data for legacy analytics. Analytic Orchestration -- Architecture -- Analytics orchestration -- Data flow -- Pros and cons of this approach -- Time series data-based analytics -- Windows-based calculations -- Forecasting of time series data points -- Developing a neural network using Keras and TensorFlow using Jupyter -- Environment setup -- Developing the neural network -- Developing an analytics for analyzing time series data using Spark -- Environment setup -- Creating a Spark-based Notebook and creating the Spark session -- Developing streaming analytics using Spark -- Environment setup -- Developing the streaming analytics -- Summary -- Chapter 6: Developing Your First Application for IIoT -- Developing and modeling assets using the S95 standard -- ISA-95 control levels -- Exchange of asset data as represented in S95 -- Selecting a storage -- Relational DBMS -- Key-value stores -- Advanced forms -- Document stores -- Graph DBMS -- Time series DBMS -- RDF stores -- Object-oriented DBMS -- Search engines -- MultiValue DBMS -- Wide column stores -- Native XML DBMS -- Content stores -- Event stores -- Navigational DBMS -- Blockchain -- Important considerations -- Time series storage -- Using InfluxDB as a time series storage -- Creating instances of assets and adding time series data -- Understanding the analytics -- Exploring descriptive analytics with InfluxDB -- Example -- count the field values associated with a field key -- Example -- calculate the mean field value associated with a field key -- Deploying your first analytics -- Examples of queries with InfluxDB analytical functions -- Example -- select all fields and tags from a single measurement -- Example -- group query results by a single tag -- Running a query -- Visualizing time series data and charts -- Visualizing time series data with Highcharts -- Visualizing time series data with Grafana -- Grafana building blocks. Configuring a Grafana visualization -- Graph panel -- Visualizing the outcomes of the analytics as alerts -- Configuring email notifications -- Configuring notifications via Slack -- Configuring alerts in Grafana -- Summary -- Chapter 7: Deployment, Scale, and Security -- IIoT security practices -- Key principles of securing IIoT applications -- Phase 1 -- third-party and architecture risk assessments -- Phase 2 -- technical security assessments -- Static analysis security testing (SAST) -- Dynamic analysis security testing (DAST) -- Open source scans -- Phase 3 -- secure by design -- Phase 4 -- penetration testing -- IIoT device security design and architecture -- IIoT device and IIoT device management -- IIoT device communication and privacy controls -- IIoT device communication and encryption -- IIoT device user privacy controls -- IIoT device placement in the network -- IIoT Gateway security principles -- TPM -- TEE -- IIoT Gateway network security -- IIoT Gateway authentication -- IIoT cloud security architecture and design -- IIoT API security -- IIoT access control -- IIoT identity store -- IIoT security analytics -- IIoT application deployment -- IIoT applications at scale -- Capacity planning -- Testing for load/performance -- Measure and identify bottlenecks -- Scale individual components -- X-scaling or horizontal duplication -- Y-axis scaling -- Z-axis scaling -- Summary -- Chapter 8: Reliability, Fault Tolerance, and Monitoring IIoT Applications -- Complexity of an IIoT system -- Art of building reliable and resilient IIoT applications -- Designing for reliability on the cloud -- Programming for network latency using the circuit breaker pattern -- Issues and considerations -- When to use this pattern -- Example -- Handling for bandwidth constraints and transport costs using the API Gateway pattern -- Issues and considerations. Computer software Development. http://id.loc.gov/authorities/subjects/sh85029535 Application software. http://id.loc.gov/authorities/subjects/sh90001980 Information technology Management. http://id.loc.gov/authorities/subjects/sh2008006980 Logiciels d'application. Technologie de l'information Gestion. Computer programming software development. bicssc Network security. bicssc Web programming. bicssc COMPUTERS Software Development & Engineering General. bisacsh Application software fast Computer software Development fast Information technology Management fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85029535 http://id.loc.gov/authorities/subjects/sh90001980 http://id.loc.gov/authorities/subjects/sh2008006980 |
| title | Industrial internet application development : simplify IIoT development using the elasticity of public cloud and native cloud services. |
| title_auth | Industrial internet application development : simplify IIoT development using the elasticity of public cloud and native cloud services. |
| title_exact_search | Industrial internet application development : simplify IIoT development using the elasticity of public cloud and native cloud services. |
| title_full | Industrial internet application development : simplify IIoT development using the elasticity of public cloud and native cloud services. |
| title_fullStr | Industrial internet application development : simplify IIoT development using the elasticity of public cloud and native cloud services. |
| title_full_unstemmed | Industrial internet application development : simplify IIoT development using the elasticity of public cloud and native cloud services. |
| title_short | Industrial internet application development : |
| title_sort | industrial internet application development simplify iiot development using the elasticity of public cloud and native cloud services |
| title_sub | simplify IIoT development using the elasticity of public cloud and native cloud services. |
| topic | Computer software Development. http://id.loc.gov/authorities/subjects/sh85029535 Application software. http://id.loc.gov/authorities/subjects/sh90001980 Information technology Management. http://id.loc.gov/authorities/subjects/sh2008006980 Logiciels d'application. Technologie de l'information Gestion. Computer programming software development. bicssc Network security. bicssc Web programming. bicssc COMPUTERS Software Development & Engineering General. bisacsh Application software fast Computer software Development fast Information technology Management fast |
| topic_facet | Computer software Development. Application software. Information technology Management. Logiciels d'application. Technologie de l'information Gestion. Computer programming software development. Network security. Web programming. COMPUTERS Software Development & Engineering General. Application software Computer software Development Information technology Management |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1905976 |
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