2023-24

Module 1: Introduction to IoT and Sensing      (9 Hours)

Introduction to IoT, Sensing, Edge computing, Data processing, Learning.

Different type of sensors,

working principal of some sensors like

·         Ultrasonic sensor

·         Humidity and Temperature

·         IMU (Accelerometer, Gyroscope, Compass)

·         Sound Sensor & Camera

·         Pollutant Sensors

·         Flex Sensor

·         sEMG Senor

Physical Layer Connections/Protocols: Inter-Integrated Circuit, or I2C Protocol, Universal Asynchronous Receiver/Transmitter (UART), Serial peripheral interface  (SPI), CAN Protocol

Module 2: Play with Sensors & Basic Programming in Microcontroller/TinyML Boards (6 Hours)

Open source hardware, Play with Sensors using Arduino Programming/micro-python, Local data processing using Raspberry Pi Pico W/Arduino Nano 33 Blu sense Rev2/Arduino Nao Rp2024 Connect, Play with different Network Modules (Bluetooth, WiFi)

Coding Skill & Some Assignments on Sensors, Arduino Board/ Raspberry Pi pico:

• Objective: Physical Layer Connectivity (I2C, UART, SPI)

  • • ·         Multiple LEDs “ON” and “OFF” in periodic fashions

    • ·         Connect “Humidity & Temperature”, “IMU” Sensor using Arduino Board/Pico

    • ·         Connect Multiple Ultrasonic Sensors (Heterogeneous) with Arduino Board/Pico

    • ·         Connect Sound Sensor  with Arduino Board/Raspberry Pi Pico

    • ·         Connect Environment Sensor (CO2, Dust Particle, No2+Co)   with Arduino Board/Pico

    • ·         LCD (or LED) Screen

    • ·         Store the sensing data in SD card

    • ·         Send the sensing data to Cloud using WiFi and Bluetooth

    • ·         LiPo Battery Connectivity 

Module 3: Communication in IoT (10 Hours)     

• Concept of TCP/IP protocol Stack

•  802.11 Protocol (WiFi Network)

•  Bluetooth Network (802.15)

•  LoRa Network

• Delay Tolerant Network

• Socket Programming, and Wireshark Tool.

Module 4: Basic ML Algorithms & TinyML (6 Hours)

• Basic Data Science Algorithms (Regression, Decision Tree, Random Forest)

•  Basic Deep Neural Networks: Neural Network, Convolution Neural Network

• Basic Architecture of TinyML Boards like Raspberry Pi Pico w, Arduino Nano 33 Blu sense Rev2, Arduino Nano RP 2040 Connect, Coral Dev Board, Syntiant TinyML board, ESP32-S3 Sense Boards

• Training a models for  TinML boards

Module 5: Case Study              (11 Hours)

• Case Study 1: (activity Identification) Human Activity using Ultra sonic Sensors/Thermal Sensors,

• Case Study 2: (Environment Monitoring) Pollution Monitoring and Forecasting in Indoor and Outdoor,

• Case Study 3: (Road Transportation System) Important PoIs using GPS trails, Road Speed Identification, Street Light Monitoring

• Case Study 4: (Challenged Networks) offline Crisis Mapper Design

• Case Study 5: (Disaster Management) offline Crisis Mapper for Post Disaster Management

• Case Study 6: (Telemedicine) IoT enabled rural telemedicine Framework

• Case Study 7: (Noise Classification) System Design for Edged People using Indoor Sounds/Noise

• Case Study 8: Implemenation of Hand Gestures in TinyML board