McKinsey Global Institute predicts Internet of Things (IoT) could generate up to $11.1 trillion a year in economic value by 2025. Gartner Research Company expects 20 billion inter-connected devices by 2020 and, as per Gartner, the IoT will have a significant impact on the economy by transforming many enterprises into digital businesses and facilitating new business models, improving efficiency and increasing employee and customer engagement. It’s clear from above and our research that the IoT is a game changer and will have huge positive impact in foreseeable future.
In order to harvest the benefits of IoT revolution, the traditional software development paradigms must be fully upgraded. The mission of our book, is to prepare current and future software engineering teams with the skills and tools to fully utilize IoT capabilities. The book introduces essential IoT concepts from the perspectives of full-scale software development with the emphasis on creating niche blue ocean products. It also:
- Outlines a fundamental full stack architecture for IoT
- Describes various development technologies in each IoT layer
- Explains IoT solution development from Product management perspective
- Extensively covers security and applicable threat models as part of IoT stack
The book provides details of several IoT reference architectures with emphasis on data integration, edge analytics, cluster architectures and closed loop responses.
Table of Contents:
SECTION I – THE INTERNET OF THINGS (IOT)
Introduction
Industry 4.0
The Man and the Machine – Robots may guide collaboration with Humans
The Five Forces that shape Industry Competition and Smart Connected Objects
Digital Twin
Enterprise IoT Platforms
Human Touch – artificial intelligence infused Mobile Companion
The CRISP-PM Process
References
SECTION II - END TO END ARCHITECTURES
Foundation Architectures
IoT Platform
IoT Platform Types
Connectivity Platform
Use Case: Artik Connectivity Platform
Technical Case Study: Using IoT to detect water leakages with Powel
IoT & KDD
5Vs and IoT
Intel IoT Data Flow Diagram
CAP Theorem
IoT Streams and Reference Architecture
Data at Rest
SECTION III - HARDWARE
Hardware Design
Arduino IDE Installation
Arduino Uno Pinout Diagram and Guide
Tinyduino Humidity Sensor (SI7021)
Embedded System Architecture
Hanumayamma Dairy IoT Sensor
SECTION IV – DATA
IoT Data Sources
Enterprise IoT Data Sources
Physical Asset Perspective – "Things that Spin"
Industrial IoT Data Sources
Sensors
Use Case: Investment in IoT is investment in our future generations’ Safety & Security
Industrial Use Case: Connected Bus and Mass Transportation
Sensors Performance and Characteristic definitions
Case study
Type of Sensors
Basic Sensors
Motion Sensors
Accelerometer App
Accelerometer Play App – a precursor to Digital Twin Apps
Magnetometer App
The Role of Sensors in Healthcare & Wellness
Use Case: A SYSTEM TO DETECT MENTAL STRESS USING MACHINE LEARNING AND MOBILE DEVELOPMENT
Health Fitness IoT App
Step Counter Value
Android Batch Step Sensor Sample
Full Sensor & Data List
Core Motion Framework in iOS
Core Motion iOS Code
Pedometer App
Reservoir Use Case
Audio Sensors
Video Sensors
Image Texture Data
IRES Image Retina Data
Geospatial Data
References
IoT Data Collectors
Use Case: Note to my great-great Grand Kids: I am Sorry
Data Collector Algorithms
Audio Signal Data
Image Texture Extraction – Histograms
References
Data Storage
Data in Motion: Data representations
Files in C
EPROM Data Storage
Android Data Storage
SQLite
SQLite and Embedded C App
SQL Storage
iOS Data Storage
Tensors as Storage
References
SECTION V: DATA SCIENCE
Machine Learning at the Edge
Use Case: Intelligent Dispenser for Long Tail Venues
Supervised Learning Techniques useful for small form factor devices
Clustering
Use Case: Smart City – Intelligent Dispenser
Sliding Window
Model Equation – Regression Analysis
Kalman Filter
K-Means Clustering
Use Case: Sensor Signal and Data Interference & Machine Learning
Fuzzy Logic (FL)
Reinforcement Learning (RL)
Neural Networks
Voice Detection (Neural Networks)
Tensor Flow execution on embedded Microcontroller Units (MCUs)
Edge to Cloud Amalgamation – Traffic Light Sensors as law enforcement devices
Hotels and Entertainment and Dynamic Pricing (Harmonious dancing for calculating
the best price – both for industry and Patrons – a Win-Win)
References
SECTION VI: CONNECTIVITY
Connectivity
5G Network
Use Case: Low Power Wide Area (LPWA) and Cellular IoT
REST
CoAP (Constrained Application Protocol)
Bluetooth Low Energy (BLE)
iOS App – IoT made easy - Sensor Tag
Android
Hanumayamma Dairy IoT Design
Use Case: Dairy Application – Cow Necklace
MQTT
IoT and Hardware Clocks
MQTT Signals
MQTT Client
MQTT Wireshark Captures
ECG or Electrocardiogram Sesnors
References
SECTION VII – CLOUD COMPUTING
Middleware
Message Architectures
Apache Kafka
Installation of Kafka
Apache Spark
References
IoT Data Analytics Platform
Data Processing Architecture
Data Acquisition Systems
Insight Value Chain
References
Future
Dedication
Acknowledgement
Appendix
Biography
Chandra is a Software IT Executive with diverse experience in Software Technologies, Enterprise Software Architectures, Cloud Computing, Big Data Business Analytics, Internet Of Things (IoT), and Software Product & Program Management. Chandra held engineering and Product leadership roles at GE Healthcare, Cisco Systems, St. Jude Medical, and Lucent Technologies, a Bell Laboratories Company. Chandra teaches Software Engineering, Mobile Computing, Cloud Technologies, and Web & Data Mining for Masters program in San Jose State University. Additionally, Chandra held market research, strategy and technology architecture advisory roles in Cisco Systems, Lam Research and performed Principal Investigator role for Valley School of Nursing where he connected Nursing Educators & Students with Virtual Reality technologies. Chandra has functioned as Chair in numerous technology and advanced computing conferences such as: IEEE Oxford, UK, IEEE Big Data Services 2017, San Francisco USA and Future of Information and Communication Conference 2018, Singapore. Chandra graduated from San Jose State University Masters Program, specializing Software Engineering, and completed his Master of Business Administration from Santa Clara University, Santa Clara, California, USA