IOT & Robotic

IOT & Robotic

Course Objective:

The course has been designed for professional with basic knowledge of electronic circuit design, microcontrollers and programming languages. The course introduces you to advance concepts and design methodologies to design IoT systems and developing IoT applications programming languages and tools optimized for IoT domain. It also exposes participants to communication technologies and legacy protocols as well as newly developed IoT specific application and physical layer protocols. The course covers python languages in great detail with set of packages which makes it obvious choice as a leading IoT language

Participants will have:

• Expert level of knowledge of IoT technology, tools and trends.

• Sound understanding of core concepts, background technologies and sub-domains of IoT.

• Knowledge and skills of sensors, microcontrollers, and communication interfaces to design and build IoT devices.

• Knowledge and skills to design and build network based on client-server and publish-subscribe to connect, collect data, monitor and manage assets.

• Knowledge and skill to write device, gateway and server side scripts and apps to aggregate and analyze sensor data

• Knowledge and skills to select application layer protocols and web services architectures for seamless integration of various components of an IoT ecosystem.

• Knowledge of standard development initiatives and reference architectures.

• Understanding of deploying various types of analytics on machine data to define context, find faults, ensure quality, and extract actionable insights.

• Understanding of cloud infrastructure, services, APIs, and architectures of commercial and industrial cloud platforms.

• Understanding of prevalent computing architectures – distributed, centralized, edge and Fog.

Content

Introduction to Internet of Things

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 & Industrie 4.0

IoT Adoption

• Market statistics, Early adopters, Roadmap

Business opportunities: Product + Service model

• Development, deployment and monetization of applications as service

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 IoTendpoints 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

o Case study/Use case:

o Environment Monitoring

o Health monitoring (Wearable)

o Asset performance monitoring