IPE Libraray Catalogue Hyderabad

Preface
Chapter 1: Defining IoT Analytics and
The situation
Defining IoT analytics
Defining analytics
Defining the Internet of Things
The concept of constrained
IoT analytics challenges
The data volume
Problems with time
Problems with space
Data quality
Analytics challenges
Business value concerns
Summary
Chapter 2: IoT Devices and Networking Protocols
IoT devices
The wild world of IoT devices
Healthcare
Manufacturing
Transportation and logistics
Retail
Oil and gas
Home automation or monitoring
Wearables
Sensor types
Networking basics
IoT networking connectivity protocols
Connectivity protocols (when the available power is limited)
Bluetooth Low Energy (also called Bluetooth Smart)
6LoWPAN
ZigBee
Advantages of ZigBee
Disadvantages of ZigBee
Common use cases
NFC
Common use cases
Sigfox
Connectivity protocols (when power is not a problem)
Wi-Fi
Common use cases
Cellular (4G/LTE)
Common use cases
IoT networking data messaging protocols
Message Queue Telemetry Transport (MQTT)
Topics
Advantages to MQTT
Disadvantages to MQTT
QoS levels
QoS 0
QoS 1
QoS 2
Last Will and Testament (LWT)
Tips for analytics
Common use cases
Hyper-Text Transport Protocol (HTTP)
Representational State Transfer (REST) principles
HTTP and IoT
Advantages to HTTP
Disadvantages to HTTP
Constrained Application Protocol (CoAP)
Advantages to CoAP
Disadvantages to CoAP
Message reliability
Common use cases
Data Distribution Service (DDS)
Common use cases
Analyzing data to infer protocol and device characteristics
Summary. Chapter 3: IoT Analytics for the Cloud
Building elastic analytics
What is cloud infrastructure?
Elastic analytics concepts
Design with the endgame in mind
Designing for scale
Decouple key components
Encapsulate analytics
Decoupling with message queues
Distributed computing
Avoid containing analytics to one server
When to use distributed and when to use one server
Assuming that change is constant
Leverage managed services
Use Application Programming Interfaces (API)
Cloud security and analytics
Public/private keys
Public versus private subnets
Access restrictions
Securing customer data
The AWS overview
AWS key concepts
Regions
Availability Zones
Subnet
Security groups
AWS key core services
Virtual Private Cloud (VPC)
Identity and Access Management (IAM)
Elastic Compute (EC2)
Simple Storage Service (S3)
AWS key services for IoT analytics
Amazon Simple Queue Service (SQS)
Amazon Elastic Map Reduce (EMR)
AWS machine learning
Amazon Relational Database Service (RDS)
Amazon Redshift
Microsoft Azure overview
Azure Data Lake Store
Azure Analysis Services
HDInsight
The R server option
The ThingWorx overview
ThingWorx Core
ThingWorx Connection Services
ThingWorx Edge
ThingWorx concepts
Thing templates
Things
Properties
Services
Events
Thing shapes
Data shapes
Entities
Summary
Chapter 4: Creating an AWS Cloud Analytics Environment
The AWS CloudFormation overview
The AWS Virtual Private Cloud (VPC) setup walk-through
Creating a key pair for the NAT and bastion instances
Creating an S3 bucket to store data
Creating a VPC for IoT Analytics
What is a NAT gateway?
What is a bastion host?
Your VPC architecture
The VPC Creation walk-through. How to terminate and clean up the environment
Summary
Chapter 5: Collecting All That Data
Strategies and Techniques
Designing data processing for analytics
Amazon Kinesis
AWS Lambda
AWS Athena
The AWS IoT platform
Microsoft Azure IoT Hub
Applying big data technology to storage
Hadoop
Hadoop cluster architectures
What is a Node?
Node types
Hadoop Distributed File System
Parquet
Avro
Hive
Serialization/Deserialization (SerDe)
Hadoop MapReduce
Yet Another Resource Negotiator (YARN)
HBase
Amazon DynamoDB
Amazon S3
Apache Spark for data processing
What is Apache Spark?
Spark and big data analytics
Thinking about a single machine versus a cluster of machines
Using Spark for IoT data processing
To stream or not to stream
Lambda architectures
Handling change
Summary
Chapter 6: Getting to Know Your Data
Exploring IoT Data
Exploring and visualizing data
The Tableau overview
Techniques to understand data quality
Look at your data
au naturel
Data completeness
Data validity
Assessing Information Lag
Representativeness
Basic time series analysis
What is meant by time series?
Applying time series analysis
Get to know categories in the data
Bring in geography
Look for attributes that might have predictive value
R (the pirate's language ... if he was a statistician)
Installing R and RStudio
Using R for statistical analysis
Summing it all up
Solving industry-specific analysis problems
Manufacturing
Healthcare
Retail
Summary
Chapter 7: Decorating Your Data
Adding External Datasets to Innovate
Adding internal datasets
Which ones and why?
Customer information
Production data
Field services
Financial
Adding external datasets
External datasets
geography
Elevation. SRTM elevation
National Elevation Dataset (NED)
Weather
Geographical features
Planet.osm
Google Maps API
USGS national transportation datasets
External datasets
demographic
The U.S. Census Bureau
CIA World Factbook
External datasets
economic
Organization for Economic Cooperation and Development (OECD)
Federal Reserve Economic Data (FRED)
Summary
Chapter 8: Communicating with Others
Visualization and Dashboarding
Common mistakes when designing visuals
The Hierarchy of Questions method
The Hierarchy of Questions method overview
Developing question trees
Pulling together the data
Aligning views with question flows
Designing visual analysis for IoT data
Using layout positioning to convey importance
Use color to highlight important data
The impact of using a single color to communicate importance
Be consistent across visuals
Make charts easy to interpret
Creating a dashboard with Tableau
The dashboard walk-through
Hierarchy of Questions example
Aligning visuals to the thought process
Creating individual views
Assembling views into a dashboard
Creating and visualizing alerts
Alert principles
Organizing alerts using a Tableau dashboard
Summary
Chapter 9: Applying Geospatial Analytics to IoT Data
Why do you need geospatial analytics for IoT?
The basics of geospatial analysis
Welcome to Null Island
Coordinate Reference Systems
The Earth is not a ball
Vector-based methods
The bounding box
Contains
Buffer
Dilation and erosion
Simplify
Vector summary
Raster-based methods
Storing geospatial data
File formats
Spatial extensions for relational databases
Storing geospatial data in HDFS
Spatial indexing
R-tree
Processing geospatial data
Geospatial analysis software
ArcGIS
QGIS. Ogr2ogr
PostGIS spatial functions
Geospatial analysis in the big data world
Solving the pollution reporting problem
Summary
Chapter 10: Data Science for IoT Analytics
Machine learning (ML)
What is machine learning?
Representation
Evaluation
Optimization
Generalization
Feature engineering with IoT data
Dealing with missing values
Centering and scaling
Time series handling
Validation methods
Cross-validation
Test set
Precision, recall, and specificity
Understanding the bias-variance tradeoff
Bias
Variance
Trade-off and complexity
Comparing different models to find the best fit using R
ROC curves
Area Under the Curve (AUC)
Random forest models using R
Random forest key concepts
Random forest R examples
Gradient Boosting Machines (GBM) using R
GBM key concepts
The Gradient Boosting Machines R example
Ensemble
Anomaly detection using R
Forecasting using ARIMA
Using R to forecast time series IoT data
Deep learning
Use cases for deep learning with IoT data
A Nickel Tour of deep learning
Setting up TensorFlow on AWS
Summary
Chapter 11: Strategies to Organize Data for Analytics
Linked Analytical Datasets
Analytical datasets
Building analytic datasets
Linking together datasets
Managing data lakes
When data lakes turn into data swamps
Data refineries
Developing a progression process
The data retention strategy
Goals
Retention strategies for IoT data
Reducing accessibility
Reducing the number of fields
Reduce the number of records
The retention strategy example
Summary
Chapter 12: The Economics of IoT Analytics
The economics of cloud computing and open source
Variable versus fixed costs
The option to quit
Cloud costs can escalate quickly
Monitoring cloud billing closely

We start with the perplexing task of extracting value from huge amounts of barely intelligible data. The data takes a convoluted route just to be on the servers for analysis, but insights can emerge through visualization and statistical modeling techniques. You will learn to extract value from IoT big data using multiple analytic techniques. Next we review how IoT devices generate data and how the information travels over networks. You'll get to know strategies to collect and store the data to optimize the potential for analytics, and strategies to handle data quality concerns. Cloud resources are a great match for IoT analytics, so Amazon Web Services, Microsoft Azure, and PTC ThingWorx are reviewed in detail next. Geospatial analytics is then introduced as a way to leverage location information. Combining IoT data with environmental data is also discussed as a way to enhance predictive capability. We'll also review the economics of IoT analytics and you'll discover ways to optimize business value. By the end of the book, you'll know how to handle scale for both data storage and analytics, how Apache Spark can be leveraged to handle scalability, and how R and Python can be used for analytic modeling.