Introduction

This IoT-based system was developed for South Asia Gateway Terminals (SAGT), Port of Colombo, Sri Lanka, to address the challenges of manual working hour tracking for Prime Mover vehicles. The solution implements real-time, automated monitoring of engine hours using ESP32 microcontrollers, MQTT protocol, and a comprehensive MERN stack dashboard, eliminating manual recording errors and significantly improving operational efficiency.

Problem Statement

Prime movers are critical assets in container terminal operations. Traditionally, their working hours were recorded manually from mechanical or local electronic meters, which was:

• Time-Consuming: Required manual inspections of each vehicle
• Error-Prone: Susceptible to human recording mistakes
• Labor-Intensive: Needed dedicated staff for data collection
• Not Real-Time: Delayed maintenance scheduling and reporting

Project Objectives

• Automated Tracking: Implement real-time tracking of engine working hours using IoT technology
• Centralized Dashboard: Create a web-based dashboard for maintenance teams to view all units simultaneously
• Data Accuracy: Eliminate manual recording errors and ensure data persistence
• Scalability: Design a low-cost solution deployable across the entire Prime Mover fleet

Technical Implementation

Hardware Architecture:

• Microcontroller: ESP32 Development Board (selected for built-in Wi-Fi and cost-effectiveness)
• Signal Detection: Monitors 24V DC signal from Prime Mover's alternator to determine engine status
• Isolation & Protection: PC817 Optocoupler or 24V DC Relay for signal isolation from ESP32
• Power Management: 24V to 5V DC-DC Buck Converter for powering ESP32 from vehicle battery
• Data Persistence: EEPROM storage for cumulative hours, preventing data loss during power cycles

Software & Communication:

• Firmware: C++/Arduino with Wi-Fi auto-reconnection and signal debouncing
• Protocol: MQTT (Mosquitto) for lightweight, real-time data transmission
• Backend: Node.js & Express.js for API and business logic
• Frontend: ReactJS for responsive, interactive dashboard
• Database: MongoDB for historical data and unit configurations

Tech Stack

Dashboard Features

• Real-Time Status: Live ON/OFF status indicators for each Prime Mover
• Unit Cards: Display unit name, total hours, last update timestamp, and initial readings
• Advanced Filtering: Search by PM number, range, or operational status
• Data Export: Excel/CSV export capability for administrative reporting
• Favorites System: Star/prioritize specific units for quick access
• Secure Access: User authentication for authorized maintenance staff only
• Historical Tracking: Time-series visualization of working hour trends

Outcomes & Impact

The system was successfully piloted at South Asia Gateway Terminals (SAGT), delivering significant operational improvements:

• Eliminated manual data collection workload for maintenance department
• Enabled predictive maintenance scheduling based on accurate hour tracking
• Reduced equipment downtime through timely maintenance interventions
• Provided real-time fleet visibility for management decision-making

Future Enhancements: Integration with Terminal Operating System (TOS) and predictive maintenance algorithms for breakdown prediction.

Introduction

This project was completed as the final design project for EE4340 - Microgrids module at the University of Moratuwa. The objective was to design, size, and simulate a grid-connected (on-grid) rooftop solar PV system for a residential location in Hokandara, Sri Lanka. The project demonstrates the complete engineering workflow from site analysis and system sizing to dynamic simulation and performance validation.

Project Philosophy

With the increasing adoption of renewable energy in Sri Lanka, grid-connected solar systems offer homeowners an opportunity to reduce electricity costs while contributing to sustainable energy generation. This project explores the technical and economic feasibility of implementing a rooftop PV system, emphasizing optimal design, accurate energy estimation, and grid-compliant operation.

System Specifications

• System Capacity: 15.6 kW
• Solar Panels: 52 units of Triana Solar 300W modules
• Configuration: 4 strings of 13 modules each (optimized for gable-type roof area)
• Inverters: Huawei SUN2000-4KTL-L1 / 5KTL-L1 (with early sizing using ABB PVI-3.6kW)
• Tilt Angle: 15° (optimized for self-cleaning and Sri Lankan latitude)
• Azimuth: 0° (panels facing North)
• Installation Type: Rooftop mounting on residential building

Design Methodology

Site Analysis:

• Google Earth: Precise rooftop area measurements and orientation analysis
• Location: Hokandara, Sri Lanka (6.9°N, 79.9°E)
• Available Roof Area: Gable-type roof structure with optimal South-facing orientation

Energy Estimation \u0026 Analysis:

• PVSyst Software: Professional solar PV simulation tool for monthly energy generation forecasting
• Derating Factors: Accounted for wiring losses, inverter efficiency, soiling, and temperature effects
• Yield Analysis: Comprehensive performance ratio calculations and loss breakdown
• Validation: Hand calculations verified against PVSyst simulation results

PSCAD Dynamic Simulation

A comprehensive dynamic model was developed in PSCAD (Power Systems Computer Aided Design) to simulate the real-time behavior of the grid-connected solar system:

• PV Array Modeling: Accurate representation of 52 panels with temperature and irradiance-dependent characteristics
• Maximum Power Point Tracking (MPPT): Implemented using Incremental Conductance Algorithm for optimal power extraction under varying conditions
• DC-DC Boost Converter: Voltage step-up and power conditioning with PI control loops
• Grid-Tie Inverter: Three-phase inverter with current control and grid synchronization
• Grid Model: 630kVA fault level with realistic impedance parameters
• Control Systems: PI controllers for voltage regulation and current injection

Tech Stack

Key Findings \u0026 Results

• Successful Grid Integration: System demonstrated stable power injection into the grid under varying irradiance and temperature conditions
• MPPT Performance: Incremental Conductance algorithm achieved efficient maximum power point tracking with rapid convergence
• Energy Yield Validation: Comparative analysis between hand calculations and PVSyst reports confirmed design reliability
• Dynamic Response: PSCAD simulations verified proper transient behavior during irradiance changes
• Economic Viability: System sizing optimized for cost-effectiveness and available roof space

Technical Achievements

• Complete end-to-end design workflow from site survey to dynamic simulation
• Integration of multiple engineering tools (PVSyst, PSCAD, Google Earth)
• Advanced control algorithm implementation (MPPT with Incremental Conductance)
• Grid compliance and power quality analysis
• Comprehensive documentation suitable for real-world implementation

Introduction

This project explores the development of a snake robot prototype aimed at potential applications in disaster response. The focus lies on its motion control and image classification capabilities, setting a foundation for future advancements in autonomous navigation and casualty detection.

Project Philosophy

By fusing robotics with computer vision, this project seeks to create efficient and adaptable solutions for emergency situations. Snake robots, with their inherent flexibility and maneuverability, offer significant potential for navigating disaster-stricken environments, making them invaluable assets in search and rescue efforts.

Tech Stack

Summary

• Manual Motion Control: The current prototype features user-controlled joint angles for movement, simulating the agility of natural snakes.
• Image Classification Integration: The robot analyzes captured images and video in real-time to identify potential casualties, utilizing the Viola-Jones algorithm to enhance its situational awareness.
• Solid Foundation for Future Advancements: This project serves as a stepping stone toward developing autonomous control algorithms and more robust image classification systems.

Documentation

The project report includes comprehensive details on:

• Hardware Design: Considerations and processes using SolidWorks.
• Software Development: Implemented in MATLAB & Simulink.
• Image Classification Algorithms: Techniques employed for casualty detection.
• Simulation and Results:Analysis and outcomes of the project.

Introduction

This project focuses on developing an intelligent autonomous maze-solving robot capable of navigating through complex maze environments. The robot combines advanced sensor technology, embedded systems programming, and intelligent pathfinding algorithms to successfully complete three progressive challenges of increasing difficulty.

Project Challenges

The maze-solving task is divided into three distinct challenges, each testing different aspects of autonomous navigation:

1. Small Maze Navigation (4×4): The robot must autonomously explore and solve a compact 4×4 grid maze, establishing fundamental pathfinding capabilities.
2. Line Following: Demonstrates precision control by accurately following a designated path using IR sensor arrays, essential for guided navigation tasks.
3. Large Maze Navigation (9×9): The ultimate challenge requiring the robot to map and navigate a complex 9×9 grid maze, utilizing sophisticated algorithms for optimal path determination.

Hardware Components

Technical Implementation

• Microcontroller: Arduino Mega serves as the brain, processing sensor data and executing navigation algorithms
• Wall Detection: Three ultrasonic sensors positioned front, left, and right for real-time obstacle mapping
• Line Following: 8-sensor IR array provides high-precision line detection and tracking
• Motor Control: H-bridge motor drivers enable precise speed control and directional changes
• Power Management: Voltage regulator ensures stable operation across all electronic components
• Custom Chassis: 3D-printed design optimized for sensor placement and weight distribution

Navigation Algorithms

• Maze Exploration: Implements systematic exploration strategies (e.g., wall-following, tremaux algorithm)
• Path Optimization: Utilizes shortest path algorithms for efficient maze solving
• Sensor Fusion: Combines ultrasonic and IR sensor data for robust navigation decisions
• PID Control: Ensures smooth and accurate motor control for precise movements

Key Features

• Autonomous navigation without external guidance
• Real-time obstacle detection and avoidance
• Adaptive pathfinding for varying maze complexities
• High-precision line following capability
• Custom 3D-printed mechanical design
• Scalable from simple to complex maze environments

Introduction

This project introduces a Smart Fan that intelligently adapts its rotation and speed based on real-time environmental data and user presence. Leveraging computer vision for distance measurement and rotation angle optimization, LabVIEW automation, the system enhances user comfort while conserving energy.

Project Philosophy

The aim of this project is to integrate modern sensing technology into smart systems that dynamically respond to environmental changes. By utilizing computer vision, data acquisition technologies, and sensors (including one custom-built), this Smart Fan offers an innovative approach to personalized comfort and energy efficiency.

Tech Stack

Features

• Distance-Based Speed Control: Generates PWM signals based on the user's distance, enabling precise fan speed adjustments.
• Adaptive Rotation: Adjusts the fan's rotation angle according to the user's location within a 4-meter range.
• Environmental Sensing: Monitors and adjusts fan operation based on temperature and humidity.
• User-Friendly Interface: Provides manual control through LabVIEW for customizable settings.
• Energy Efficiency: Reduces energy consumption by dynamically adjusting fan operations.

Operation

The project report includes comprehensive details on:

• LabVIEW Integration: Processes inputs from three sensors using NI DAQ mx to adjust the fan speed, maintains connection with the programmable DC power supply, and provides a dashboard for real-time data and system status monitoring.
• Distance Measurement: The area of the bounding box around detected faces is used to estimate the distance to the user and determine the appropriate angles for fan rotation.
• Face Detection: Utilizes a Mediapipe model for face detection, processed in real-time with OpenCV using a webcam connected to the Raspberry Pi.

Introduction

This project involves the frontend development of the Inventory Management Subsystem for Company A, an enterprise focused on selling electronic items. The subsystem plays a crucial role in overseeing and controlling inventory-related activities, interfacing with other key components of the company's operations.

Project Philosophy

The aim of this project is to integrate modern sensing technology into smart systems that dynamically respond to environmental changes. By utilizing computer vision, data acquisition technologies, and sensors (including one custom-built), this Smart Fan offers an innovative approach to personalized comfort and energy efficiency.

Tech Stack

Features

• Distance-Based Speed Control: Generates PWM signals based on the user's distance, enabling precise fan speed adjustments.
• Adaptive Rotation: Adjusts the fan's rotation angle according to the user's location within a 4-meter range.
• Environmental Sensing: Monitors and adjusts fan operation based on temperature and humidity.
• User-Friendly Interface: Provides manual control through LabVIEW for customizable settings.
• Energy Efficiency: Reduces energy consumption by dynamically adjusting fan operations.

Operation

The project report includes comprehensive details on:

• LabVIEW Integration: Processes inputs from three sensors using NI DAQ mx to adjust the fan speed, maintains connection with the programmable DC power supply, and provides a dashboard for real-time data and system status monitoring.
• Distance Measurement: The area of the bounding box around detected faces is used to estimate the distance to the user and determine the appropriate angles for fan rotation.
• Face Detection: Utilizes a Mediapipe model for face detection, processed in real-time with OpenCV using a webcam connected to the Raspberry Pi.

Introduction

The ESP32 Smart Medibox is an innovative IoT-based system designed to improve adherence to prescribed medication schedules. This project leverages the ESP32 microcontroller along with various sensors, actuators, and communication modules to ensure timely reminders, environmental monitoring, and user interaction. The Medibox monitors environmental conditions such as temperature, humidity, and light intensity, while providing scheduled alerts for medication intake. With WiFi connectivity and customizable settings, this system enhances medication management through automation and remote access.

Project Philosophy

The ESP32 Smart Medibox integrates IoT technology with healthcare to enhance medication adherence and daily living. By automating medication reminders and environmental monitoring, the project promotes user health with minimal manual intervention. It aims to create a responsive, user-centric system that adapts to individual needs, offering a flexible and convenient solution for modern healthcare management.

Tech Stack

Features

• WiFi Connectivity: Enables remote monitoring and control via MQTT.
• Temperature & Humidity Monitoring: Utilizes the DHT22 sensor to measure environmental conditions.
• Light Intensity Measurement: Uses LDRs to adjust a servo motor based on ambient light.
• Servo Motor Control: Automatically adjusts a shaded window according to light conditions.
• Buzzer Notifications: Alerts users when it’s time to take medication.
• User Interaction: Includes buttons for confirming actions and canceling alarms.
• Customizable Settings:Allows parameters like the servo motor's angle to be adjusted via MQTT.

Operation

• Environmental Monitoring: The ESP32 continuously monitors temperature, humidity, and light intensity. If the temperature or humidity exceeds predefined thresholds, a buzzer alerts the user. Light intensity data is used to adjust the servo motor, which controls a shaded window to maintain optimal light conditions.
• Medication Alerts: Scheduled alerts for medication intake are managed by the ESP32, which triggers a buzzer at the specified times. Users can interact with the Medibox using buttons to confirm actions or cancel alarms.
• Remote Access & Control: With WiFi connectivity, users can remotely manage the Medibox, monitor its status, and adjust settings via MQTT. The Node-RED dashboard provides an intuitive interface for interacting with the Medibox from anywhere.

Introduction

This management system is designed for a private educational institution to streamline administrative tasks. Developed using C# Windows Forms in Visual Studio and backed by an SQL database, the system allows the institution to efficiently manage student, teacher, and class records. The primary goal is to automate and enhance administrative operations, ensuring accurate and secure record-keeping while improving overall efficiency.

Project Philosophy

The Student, Teacher, and Class Management System is designed to enhance efficiency and accuracy in educational administration. By automating routine tasks and centralizing record management, the system reduces manual errors and frees up staff to focus on educational quality. This project reflects the belief that streamlined data management leads to better decision-making and a more organized educational environment for all.

Tech Stack

Features

• Student Management:Add, update, and delete student records.
• Teacher Management: Add, update, and delete teacher records.
• Class Management:Add, update, and delete class details.
• Attendance Tracking: Maintain and monitor student attendance records.