Python overview

• Syntax and structure
• Comparisons to other languages (C, C++, Java, etc)
• Available Python Resources
• Whitespace, Indentation and program formatting
• Variables and Naming Conventions
• Operators
• Statement structure
• Comments
• Program Construction

Data Types

• Built-in Types
• Strings and Numbers
• Formatting Data, Numbers, Dates
• Using Lists/Arrays
• Tuples
• Dictionaries
• Understanding Dynamic Typing
• Working with Functions
• Python Code Execution
• Basic Input / Output
• String Operations
• Working with Tuples and Lists
• Introducing Control Flow Statements

Functions

• Variable Scope
• Variable Parameters
• Default Values
• Positional Parameters
• Keyword Parameters
• Introducing Lambdas
• Exception Handling

Classes in Python

• Creating Classes in Python
• Classes are Namespaces
• Constructors
• Self and Instances
• Class Variables
• List Comprehensions
• Advance Python Modules
• Default Values
• Positional Parameters
• Keyword Parameters
• Introducing Lambdas
• Exception Handling

Learning Objectives:

1. Expert-level knowledge of IoT technology, tools, and trends
2. Sound understanding of core concepts, background technologies, and sub-domains of IoT
3. Knowledge and skills of sensors, microcontrollers, and communication interfaces to design and build IoT devices
4. Knowledge and skills to design and build a network based on client-server and publish-subscribe to connect, collect data, monitor, and manage assets
5. Knowledge and skill to write device, gateway, and server-side scripts and apps to aggregate and analyze sensor data
6. Knowledge and skills to select application layer protocols and web services architectures for seamless integration of various components of an IoT ecosystem
7. Knowledge of standard development initiatives and reference architectures
8. Understanding the deployment of various types of analytics on machine data to define context, find faults, ensure quality, and extract actionable insights
9. Understanding of cloud infrastructure, services, APIs, and architectures of commercial and industrial cloud platforms
10. Understanding of prevalent computing architectures – distributed, centralized, edge and Fog

Content

• Introduction to Internet of Things (IoT)
  • Concept and definitions
    • Embedded Systems, Computer Networks, M2M (Machine to Machine Communication), Internet of Everything (IoE), Machine Learning, Distributed Computing, Artificial Intelligence, Industrial automation
    • Interoperability, Identification, localization, Communication, Software Defined Assets
  • Understanding IT and OT convergence: Evolution of IIoT & Industry 4.0
  • IoT Adoption
    • Market statistics, Early adopters, Roadmap
  • Business opportunities: Product + Service model
    • Development, deployment and monetization of applications as service
  • Use cases
• Concept of Data, Information, Knowledge and Wisdom
  • Knowledge discovery process
  • DIKW pyramid and relevance with IoT
  • Microcontrollers: cost, performance, and power consumption
    • Commercial microcontroller based development boards
    • Selection criteria and tradeoffs
  • Industrial networks, M2M networks
• Sensor Data Mining and Analytics
  • Transducer: Sensor and Actuator
    • Sensors – Types of sensors, sampling, analog to digital conversion, selection criteria of sensor and ADC
  • Data acquisition, storage and analytics
  • Signals and systems
    • Signal processing, systems classification, sampling theorem, ensuring quality and consistency of data
  • Real-time analytics
    • Understanding fundamental nuances between IoT and Big Data
    • Usage of IoT data in various business domains to gain operational efficiency
  • Edge analytics
    • Data Aggregation on Edge gateway
  • Wireless Sensor Area Networks (WSAN): Evolution of M2M and IoT networks and technologies
    • Sensor nodes
      • Sensor node architecture
    • WSN/M2M communication technologies
      • Bluetooth, Zigbee and WiFi communication technologies
      • Cellular communication and LPWAN (LoRa and LoRaWAN) technologies
    • Topologies
    • Applications
  • Design and Development of IoT systems
    • IoT reference architectures
      • Standardization initiatives
      • Interoperability issues
    • IoT design considerations
      • Architectures Device, Network and Cloud
      • Centralized vs distributed architectures
    • Networks, communication technologies and protocols
    • Smart asset management: Connectivity, Visibility, Analytics, Alerts
  • Cloud Computing and Platforms
    • Public, Private and Hybrid cloud platforms and deployment strategy
    • Industrial Gateways
      • Commercial Gateways solutions from various vendors
      • Cloud-based Gateway solutions
    • IaaS, SaaS, PaaS models
    • Cloud components and services
      • Device Management, Databases, Visualization, Reporting, Notification/Alarm management, Security management, Cloud resource monitoring and management
    • Example Platforms: ThingSpeak, Pubnub, AWS IoT
      • AWS IoT Services
        • Device Registry
        • Authentication and Authorization
        • Device Gateway
        • Rules Engine
        • Device Shadow
      • IoT security
        • Standards and Best practices
          • Common vulnerabilities
          • Attack Surfaces
          • Hardware and Software solutions
          • Open source initiatives
        • Analytics
          • Descriptive, Diagnostic, Predictive and Prescriptive
          • Analytics using Python advance packages: NumPy, SciPy, Matplotlib, Pandas, and Sci-kit learn
        • Case studies and roadmap
          • Cold chain monitoring
          • Asset tracking using RFID and GPRS/GPS

Hands-on/Practical Exercises:

• Programming microcontrollers (Arduino, NodeMCU)
• Building HTTP and MQTT based M2M networks
• Interfacing Analog and Digital sensors with microcontroller to learn real-time data acquisition, storage and analysis on IoT endpoints and edges
• Interfacing SD card with microcontroller for data logging on IoT end devices using SPI protocol
• Interfacing Real-time clock module with microcontrollers for time and date stamping using I2C protocol
• Python exercises to check quality of acquired data
• developing microcontroller based applications to understand event based real time processing and in- memory computations
• Setting up Raspberry Pi as Gateway to aggregate data from thin clients
• Python programming on Raspberry Pi to analyze collected data
• GPIO programming using Python and remote monitoring/control
• Pushing collected data to cloud platforms
• Designing sensor nodes to collect multiple parameters (Temperature, Humidity, etc.)
• Uploading data on local gateway as cache
• Uploading data on cloud platforms
• Monitoring and controlling devices using android user apps and Bluetooth interfaces
• Building wireless sensor networks using WiFi
• Sensor data uploading on cloud using GSM/GPRS
• Device to device communication using LoRa modules
• Remote controlling machines using cloud based apps
• Remote controlling machines using device based apps through cloud as an intermediate node
• Interfacing Raspberry Pi with AWS IoT Gateway service to exchange messages
• Interfacing Raspberry Pi with PUBNUB cloud to understand publish/subscribe architecture and MQTT protocol
• Data cleaning, sub setting and visualization
• Set of python exercises to demonstrate descriptive and predictive analytics
• Case study/Use case:

1. Environment Monitoring
2. Health monitoring (Wearable)
3. Asset performance monitoring

• IoT concepts revision
• Introduction to information and cybersecurity
• Basic terminologies
• Standards and open source initiatives
• CIA triads: effectively addressing security and privacy concerns
• Attack surfaces and vulnerabilities: Device, network, Gateway and Cloud
• Risk assessment and management
• Cryptography: Applications of Cryptography in IoT communication and data security
• Threat modelling
• Device security
  • Application hardening
  • OS/platform hardening
  • Physical security
• Gateway security
• Communication protocols and network security
  • Data link layer – Wireless communication technology security provisions
    • WiFi, Bluetooth, Zigbee and 802.15.4 protocols
  • Application layer security
    • MQTT and HTTP protocols
  • Network hardening
• IoT cloud platforms
  • API and endpoint security
  • Security of data at rest
  • Standard security frameworks
  • Example platforms: AWS and Microsoft Azure

Hardware kit consists of following:

• Development Boards
  • Raspberry Pi 3
  • Arduino Mega (ATMega2560) with USB cable
  • ESP8266 NodeMcu
• Electronic Components
  • Sensors – Analog temperature sensor(LM35)
  • IR Proximity Sensor
  • Switches – Push Button (10)
  • Breadboard
  • LEDs (10)
  • Resistors (10)
  • Connecting leads (25)
  • Memory Card (16 GB)
  • HDMI – VGA Converter
  • 1A Power Adapter
• Communication Modules
  • WiFi – ESP01
  • Bluetooth – HC05

Concept of machine learning, Introduction to R programming, Regression – Linear and non-linear, Algorithms – MLR, Logistics and non-linear regression, Classification, Algorithms – SVM, Decision trees, boosted decision trees, Naïve Bayes, Quality of classification – Concepts of ROC, hit rate, kappa statistics and K-S statistics, Feature selection – Learn feature selection methods for regression- Ridge and LASSO

Feature selection methods for classification methods- Information value based, filter based and wrapper based, Algorithms and techniques for marketing analytics – Conjoint analysis, Hidden Markov models