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<span style="font-size:200%; font-weight:bold;">TE AIDS MDM331AID : Internet of Things Lab</span>
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<span style="font-size:120%;">Cognifront IoT Kit for MDM331AID : Internet of Things Lab</span>
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== About the Laboratory ==

The Internet of Things (IoT) laboratory provides hands-on experience in connecting physical devices, sensors, microcontrollers and cloud platforms to develop intelligent, connected systems.

This laboratory manual is designed for students of Third Year B.E. Artificial Intelligence and Data Science Engineering, covering fundamental IoT concepts, embedded programming, sensor interfacing, network communication and cloud-based applications.

== Laboratory Objectives ==

* Understand the architecture and fundamental concepts of the Internet of Things.
* Interface sensors and actuators with embedded development boards.
* Develop programs for acquiring and processing sensor data.
* Implement communication protocols used in IoT systems.
* Connect IoT devices with cloud platforms for data visualization and monitoring.
* Design and implement real-world IoT applications.

{| class="wikitable"
|-
! #
! Experiment Title
|-
! colspan="2" | Part A
|-
| 1
| [[MDM331AID/Experiment 1|Develop an application for the connectivity of the Arduino UNO/Raspberry Pi circuit with sensors.]]
|-
| 2
| [[MDM331AID/Experiment 2|Implement an application to detect obstacles and notify users using LEDs to understand the connectivity of Raspberry Pi / Arduino with IR sensor.]]
|-
| 3
| [[MDM331AID/Experiment 3|Implement a simple IoT application using an Arduino board to control an LED through GPIO pins in order to understand the physical design of IoT devices and device actuation.]]
|-
| 4
| [[MDM331AID/Experiment 4|Develop a program to detect gas leakage in the surrounding environment.]]
|-
| 5
| [[MDM331AID/Experiment 5|Configure and implement an IoT device (Arduino UNO/ESP module/Raspberry Pi) to connect to a Wi-Fi network and transmit sensor data to a remote computer using TCP/IP communication.]]
|-
| 6
| [[MDM331AID/Experiment 6|Implement an edge computing model where sensor data is processed locally on an edge device (Raspberry Pi) before being transmitted to the cloud to reduce latency and bandwidth usage.]]
|-
| 7
| [[MDM331AID/Experiment 7|Design an RFID-based identification system that reads tag information and displays the ID on a monitoring system to demonstrate short-range communication technologies in IoT.]]
|-
! colspan="2" | Part B
|-
| 8
| [[MDM331AID/Experiment 8|Develop an IoT application that uses a communication protocol to transmit sensor data between an IoT device and a server.]]
|-
| 9
| [[MDM331AID/Experiment 9|Perform data cleaning, normalization, and visualization on an IoT sensor dataset (any dataset can be considered).]]
|-
| 10
| [[MDM331AID/Experiment 10|Implement an IoT application to collect real-time temperature data using an IoT sensor and apply a machine learning model to predict future temperature values (any machine learning model).]]
|-
| 11
| [[MDM331AID/Experiment 11|Design and implement a password-protected smart sensor system that allows access to sensor data only after successful authentication, demonstrating basic IoT security concepts (any security algorithm can be used).]]
|-
| 12
| [[MDM331AID/Experiment 12|Implement secure data transmission in an IoT system using a sensor and any communication security protocol.]]
|-
<!--
| 12/1
| [[MDM331AID/Experiment 12_BLE|(BLE)Implement secure data transmission in an IoT system using a sensor and any communication security protocol.]]
|-
| 12/2
| [[MDM331AID/Experiment 12_ESPNOW|(ESPNOW) Implement secure data transmission in an IoT system using a sensor and any communication security protocol.]]
|-
-->
| 13
| [[MDM331AID/Experiment 13|Develop and implement a machine learning model to perform classification (e.g., activity recognition or device state detection) and anomaly detection (e.g., fault detection in industrial IoT sensors).]]
|-
| 14
| [[MDM331AID/Experiment 14|Case study for data minimization using smart threshold alerts by providing Privacy-by-Design.]]
|-
! colspan="2" | Part C
|-
| 15
| [[MDM331AID/Experiment 15|Develop a Privacy-Aware Intelligent Smart IoT Application: Design and implementation of an AIoT-based smart sensor system with cloud-based data preprocessing and intelligent decision-making using machine learning techniques, incorporating secure communication protocols with built-in security and privacy mechanisms, with complete deliverables including a working demonstration and technical report.]]
|}