CO 1: To understand and master basic knowledge, theories and methods in Internet of Things
CO2: To identify, formulate and solve problems in Internet of Things.
CO3: To analyse, evaluate and examine existing case studies of successful IoT based systems
CO 4: To develop practical skills in designing and implementing IoT based solutions like Environment Monitoring, Assistive living, Activity Monitoring, Transportation System
Introduction to IoT and Sensing: Introduction to IoT, Sensing, Edge computing, Data processing, Learning, Basic principles of Physics. Design of a Sensor (like Touch Sensor) using resistance, Capacitor & Inductor, Different type of sensors, working principle of some sensors like (a) ultrasonic sensor (b) humidity and Temperature (c)IMU (Accelerometer, Gyroscope, Compass) (d)Sound Sensor & Camera (e) Pollutant Sensors (f) Flex Sensor (g) sEMG Sensor (h) Touch sensor. [8 Hours]
Physical Layer Protocols: Inter-Integrated Circuit, or I2C Protocol, I2S (I2C Sound) Protocol, Universal Asynchronous Receiver/Transmitter (UART), Serial peripheral interface (SPI), CAN Protocol. [3 Hours]
Play with Sensors & Basic Programming in Microcontroller/TinyML Boards : Open source hardware, Introduction to microcontrollers and microcomputers, Getting to know the domain-specific terminology, Architecture and specification of multiple microcontroller development boards. Play with Sensors using micro-python /C, Local data processing using Raspberry Pi Pico W, ESP32 S3, Raspberry Pi Zero 2 W, Milk V, Play with different Network Modules (Bluetooth, WiFi). [6 Hours]
Building a device Driver: Introduction to sensor datasheets, building a driver using the information of datasheets of Basic sensors such as temperature sensor and dust particle sensor. [3 Hours]
Communication in IoT (10 Hours): Concept of TCP/IP protocol Stack, 802.11 Protocol (WiFi Network), Bluetooth Network (802.15), Bluetooth Communication Protocol, Bluetooth Low Energy, LoRa Network, Delay Tolerant Network, MQTT Protocol, HTTP Protocol, COAP Protocol, Various tools and techniques for developing a companion IoT application, Socket Programming, and Wireshark Tool. [6 Hours]
Basic ML Algorithms & Exploration of TinyML Frameworks (6 Hours): Basic Data Science Algorithms (Regression, Decision Tree, Random Forest), Basic Deep Neural Networks: Neural Network, Convolution Neural Network, Model development using Tensorflow, Quantization Aware Training, Introduction to Tensorflow Lite Micro, Deploying models onto microcontrollers using Tensorflow Lite Micro . [8 Hours]
Case Study: (a) (activity Identification) Human Activity using Ultrasonic Sensors/Thermal Sensors, (b)(Environment Monitoring) Pollution Monitoring and Forecasting in Indoor and Outdoor, (c)(Road Transportation System) Important PoIs using GPS trails, Road Speed Identification, Street Light Monitoring (d) (Challenged Networks) offline Crisis Mapper Design, (e) (Disaster Management) offline Crisis Mapper for Post Disaster Management (f) (Telemedicine) IoT enabled rural telemedicine Framework, (g) (Noise Classification) System Design for Edged People using Indoor Sounds/Noise, (h) Implementation of Hand Gestures in TinyML board. (8 Hours)