IoT & Embedded projects
ESP32, Arduino and connected devices.
3D Printer Enclosure Air Quality Monitor with Ventilation Fan Control
This project keeps watch over an enclosed 3D printer — a hot box where temperature, humidity and filament fumes all matter. A...
6-Zone Home Intrusion Alarm Panel with Keypad and Web Dashboard
A real alarm panel is a zone machine — every door, window and room is a supervised zone with an arm state, an entry/exit dela...
Agricultural Crop-Spraying Drone with Precision Flow Control
This project builds a quadcopter drone fitted for agricultural spraying: a spray tank, 12 V diaphragm pump and boom nozzles a...
AI-Based Smart Traffic Management System
A YOLO-based vision system that counts vehicles per lane from traffic-camera feeds, dynamically retimes signals by lane densi...
Arduino Elevator Model with Floor Display and Up Down Control
This project builds a tabletop Arduino elevator model that moves a small car up and down a shaft. A motor at the top winds a...
Arduino Function Generator using DAC
A low-cost function generator built on an Arduino Uno driving an MCP4725 12-bit I2C DAC. It produces sine, square, triangle a...
Arduino SD Card Data Logger
This project builds an Arduino Uno data logger that reads temperature and humidity from a DHT22 sensor and writes every readi...
Arduino Tetris Game Console with LCD and Joystick
This project builds a Tetris game console on an Arduino Uno: a 128 by 64 LCD shows the falling pieces, the level, the lines c...
Arduino Weather Station using DHT11 and LCD Display
This project builds a tabletop Arduino weather station that reads room temperature and humidity with a DHT11 sensor and shows...
IoT & Embedded guides
All guidesHow Thermal Drones See Heat: LiDAR, mmWave Radar and Thermal Imaging for Inspection
A thermal inspection drone finds overheating joints on power lines from the air, using three different sensors: a thermal camera that reads heat, a LiDAR that holds altitude, and a mmWave radar that keeps working in fog. This guide explains how each sensor works — microbolometers and emissivity, time-of-flight ranging and beam geometry, FMCW radar and distance gates — and how a student team can honestly build the same architecture at lab scale, using the DJI Matrice 5T launch as the reference.
Read guideHow Multi-Tool 3D Printers Work: Tool-Changers, Docking and Fast Material Swaps
A multi-tool 3D printer carries several complete print heads and swaps them mid-print, so each material keeps its own nozzle, heater and filament path. This guide explains how docking, per-tool heaters, pre-heating and Z-offset compensation make that work, why single-nozzle printing wastes time and filament on purge towers, and what a student can honestly build with three tools instead of nine, using the Bambu Lab H3C launch as the reference.
Read guideThermocouple vs RTD vs Thermistor: Choosing a Temperature Sensor
Thermocouple, RTD and thermistor work on different physics and each wins in a different corner of the range, accuracy and cost triangle. This guide compares them, gives a four-question selection routine, and shows the wiring and code for reading each one on a microcontroller.
Read guideHow Semi-Submersible Drones Work: Buoyancy, Radio Waves and Pressure Depth Sensing
A semi-submersible drone keeps its hull below the waterline and its antenna mast above it, so it can be driven over Wi-Fi while the camera and sensors work underwater. This guide explains the buoyancy trim, the radio physics and the pressure-based depth sensing behind the shape, using the K600 unveiling at Euronaval 2026 as the reference and a test-tank student build as the working example.
Read guideHow Wrist Motion Sensors Track Parkinson's Symptoms: Tremor Bands, Rest Gates, and Dose-Time Plots
A wrist sensor only feels movement, so every Parkinson's symptom index is signal processing plus rules about trustworthy data. This guide explains the IMU, the 4 to 6 Hz FFT tremor band, the rest gate that keeps walking out of the score, tap-test and actigraphy proxies, and dose-time plots — and where honest student measurement stops and clinical measurement begins.
Read guideGPS Modules for Student Projects: Reading NMEA Sentences with Arduino and ESP32
A GPS module streams NMEA 0183 sentences over serial, and reading two of them unlocks every tracker and logger project. This guide decodes GGA and RMC field by field, shows the degrees-minutes conversion, covers safe 3.3 V wiring, and gives a minimal ESP32 parser and a Python parser with checksum checking.
Read guideFrequently Asked Questions
Do I get the actual hardware or just the design?
You get everything needed to build it: firmware, wiring diagrams, component list with part numbers, and assembly guidance. Hardware can be procured through us or sourced locally.
Which microcontrollers do you work with?
ESP32, ESP8266, Arduino (Uno, Nano, Mega), Raspberry Pi and STM32, plus common sensors — DHT22, ultrasonic, PIR, soil moisture, current sensors and more.
Can the project connect to a mobile app or cloud dashboard?
Yes — many listings include Blynk, Firebase or MQTT dashboard integration. Custom app requirements can be quoted.
How long does a built-to-order IoT project take?
Typical delivery is 2–4 weeks depending on component availability and complexity. The exact timeline is confirmed in your quotation.
About IoT & Embedded projects
IoT and embedded projects are the most hands-on option for a final-year build. A typical project connects physical sensors and actuators to a microcontroller, moves the data over Wi-Fi to a cloud service, and shows it on a phone app or web dashboard. You end up learning a little of everything: electronics, firmware programming, networking, and basic backend work. That breadth is exactly why these projects are respected, but it is also why they take longer than they look on paper.
What IoT students usually build
The most common builds fall into a few families. Agriculture projects like IoT Smart Irrigation with Soil Moisture read soil moisture levels and switch a water pump automatically, sometimes with a manual override from the app. Surveillance and robotics builds like the ESP32-CAM Surveillance Robot stream video while moving on a small chassis. Access and automation projects like the RFID Smart Toll Collection System read RFID tags to simulate toll deduction and barrier control. City infrastructure ideas like IoT Smart Street Light with Auto Intensity dim LED street lights based on ambient light and motion, which is a neat demonstration of closed-loop control.
Almost all of these share the same skeleton: a microcontroller reads one or more sensors, decides what to do, drives an actuator like a relay or motor driver, and publishes readings to the cloud. If you understand that loop properly, you can adapt it to dozens of problem statements.
Technologies and tools worth learning
Three boards cover nearly every student project. The ESP32 is the workhorse: built-in Wi-Fi and Bluetooth, plenty of GPIO pins, low cost, and strong documentation. Arduino Uno or Nano is friendlier for beginners and perfectly fine when you do not need wireless connectivity. The Raspberry Pi is a small computer rather than a microcontroller, and it earns its place when you need a camera, image processing, or a local server. Our guide ESP32 vs Arduino vs Raspberry Pi: Which Is Best for Final-Year IoT Projects walks through this choice in detail.
On the sensing side, learn a handful of sensors properly rather than ten superficially: soil moisture, DHT22 for temperature and humidity, PIR for motion, ultrasonic for distance, and an LDR for light. The guide How to Choose the Right Sensor for Your IoT Project covers operating voltage, accuracy limits, and calibration, which are the things that actually cause demo-day failures. For communication, MQTT is the standard lightweight protocol for sensor data, while plain HTTP is fine for occasional updates. Two more guides, MQTT for Final-Year IoT Projects: A Practical Student Guide and How to Connect Your IoT Project to the Cloud (MQTT, HTTP, Firebase, Blynk), take you from a bare board to a working dashboard.
Hardware and software, in what proportion
Expect roughly half your time on hardware and half on software. Hardware means wiring on a breadboard or perfboard, a stable power supply, and a presentable enclosure. Plan the power design early: USB power banks sag under motor loads, and brownouts cause the exact kind of intermittent failure that is hardest to debug the night before a demo. Software means firmware in the Arduino IDE or PlatformIO, plus whatever app or dashboard displays the data.
The classic mistake is adding sensors faster than you can debug them. Get one sensor reading reliably, one actuator responding, and the cloud link stable before you add the second feature. A small working system always scores better than a large half-working one.
Choosing the right scope
Be honest about four things: timeline, budget, team size, and skills. Most teams have three to four months of real working time alongside other subjects. A single-ESP32 project with two or three sensors, a relay, and a Blynk dashboard fits that window comfortably. Adding a Raspberry Pi with camera processing roughly doubles the software work, so only take that on if someone on the team is comfortable with Linux and Python.
Component budgets for these builds are usually modest, but order parts early because shipping delays are the most common reason IoT projects slip. Teams of two to four work well: one person owns hardware and wiring, one owns firmware, one owns the app or dashboard, and everyone shares documentation. If nobody on the team has debugged a circuit before, keep the hardware simple and put the complexity in software, where mistakes are cheaper to fix.
A note on how Projectech works
Projectech builds final-year IoT and embedded projects to order, with the hardware assembled, firmware written, and the cloud dashboard configured for your specific topic. Every build comes with a clear explanation of how it works, so you can answer questions about your own project with confidence, along with report and presentation support for your submission.