Research Publications

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Now showing 1 - 10 of 25
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    IoT-Based Smart Hydroponics for Automated Nutrient, Climate, Irrigation, and Health Monitoring
    (Institute of Electrical and Electronics Engineers Inc., 2025) Ashik M.A.M; Bogahawatta C.A; Perera M.R.D; Dassanayake D.R.I.P; Jayakody, A; Gamage, N
    This study presents HydroNutraLeaf as a selfgoverning hydroponic tower system built with Internet of Things technology to automate the critical aspects of hydroculture farming by uniting water supply management with environmental control and watering systems and plant health monitoring capabilities. The system unites multiple essential components to operate as one unit. The system incorporates an automatic plant disease detection system through real-time image acquisition which uses Convolutional Neural Network (CNN) algorithms and cloud-based warning protocols for classification purposes. An automated system comprising Raspberry Pi actuators, NPK sensors, and machine learning functions delivers nutrients at proper stages during plant growth. A reinforcement learning system directs the management of climate factors including temperature and humidity together with Light Emitting Diode (LED) spectrums to achieve superior yield production and product quality. The system includes a self-operated irrigation system with Electrical Conductivity (EC), potential of hydrogen (pH) regulation features which utilizes SVM-based prediction methods in combination with real-time monitoring to achieve optimum root environment conditions. Users can access a dashboard in Grafana to monitor and control the system by using cloud platforms which include Firebase and AWS. The experimental findings reveal water consumption decreased by 30% along with improved nutritional efficiency reaching 25% and enhanced crop yield reaching 15% with better health performance. The sustainable farming operations and commercial greenhouse implementation benefit from HydroNutraLeaf solution which operates through a scalable model based on data analysis and requires minimal human intervention.
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    "Cropmaster" - Real-Time Coordination of Multirobot Systems for Autonomous Crop Harvesting: Design and Implementation
    (Institute of Electrical and Electronics Engineers Inc., 2025) Pramod, I; Arachchi, A.M; Rashen, C; Chinthaka, G; Pandithage, D; Gamage, N
    The CropMaster is an autonomous rover system designed to enhance Scotch Bonnet production by improving disease management, crop sorting, autonomous navigation, and real-time environmental monitoring. Equipped with sensors to measure sunlight, humidity, pH, NPK content, and soil moisture, the rover securely transmits analyzed data to a web-based dashboard. LIDAR technology enables efficient autonomous navigation, allowing the rover to move around fields and avoid obstacles. The MQTT protocol facilitates communication between multiple rovers, preventing duplicate measurements and ensuring data is sent to the dashboard for comprehensive data collection across large areas. TensorFlow's machine learning models allow the rover to accurately assess crop health and detect early-stage diseases, followed by automated pesticide and fertilizer application through a spraying system. To maintain reliability, the rover's operations, including data transfer and task execution, are continuously monitored for Quality of Service (QoS). All collected data is stored in the cloud for long-term access. Built with a lightweight aluminum and plastic chassis and robotic arms, the rover is designed for adaptability and operational efficiency, aiming to improve crop management and increase yields across extensive agricultural fields.
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    Dynamic Bandwidth Allocation in Enterprise Network Architecture: A Real-Time Optimization Approach
    (Institute of Electrical and Electronics Engineers Inc., 2025) Wickramasinghe T.M.L.D; Costa M.M.R.S; Dissanayake S.C.W.; Abayakoon A.M.W.Y.; Lokuliyana, S; Gamage, N
    Enterprise networks increasingly rely on cloud platforms, remote collaboration tools, and real-time communication, placing high demands on bandwidth availability and responsiveness. Static bandwidth allocation approaches often fail to adapt to dynamic traffic conditions, leading to congestion, inefficiency, and degraded Quality of Service (QoS) for critical services such as VoIP and video conferencing. This research introduces a novel real-time bandwidth allocation system that integrates Deep Packet Inspection (DPI), supervised machine learning, and Linux traffic control (tc). Unlike prior solutions that focus only on classification or simulation, our system actively enforces bandwidth policies based on live predictions. Traffic is captured and analyzed in the WAN, while adaptive policies are deployed in the LAN. A web dashboard offers real-time traffic and bandwidth visibility. The proposed system addresses realworld enterprise challenges by enabling intelligent, responsive bandwidth management without requiring costly infrastructure changes, achieving measurable improvements in latency, throughput, and application-level prioritization.
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    UrbanGreen - E-Waste Detection and Analysis using YOLOv5
    (Institute of Electrical and Electronics Engineers Inc., 2025) Madusanka A.R.M.S; Nawaratne D.M.R.S.; Gamage, N; Attanayaka, B
    E-waste has become a global concern that challenges environmental sustain ability. The disposal of electronic devices is often poorly managed, especially in urban areas. This research aims to develop an innovative e-waste management system suitable for urban areas, focusing on accurately identifying electronic devices and their harmful components through advanced image processing techniques. (Y olov5) The system identifies various electronic devices, harmful components and materials and assesses their recyclability, improper disposal's environmental and health impacts, empowering users to make informed decisions about disposal and recycling. The system will integrate tools to identify E-waste, promote the reuse of electronic devices, educate the public through interactive educational platforms, and locate nearby e-waste collection centers. By addressing these critical aspects of e-waste management, the project aims to provide a useful platform to manage e-waste effectively in urban areas. This paper was developed to discuss E-waste detection and analysis using YOLOv5 object detection model.
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    Evaluating the Success of Digital Learning in Sri Lankan Tertiary Education
    (IEEE, 2022-12-09) Weerapperuma, J; Nawinna, D; Gamage, N
    This paper takes a social capital perspective to explain the underlying mechanisms that drive the success of digital learning in tertiary education in an emerging economy. It is crucial to explore ways in which the success of tertiary education can be maximized since these students will immediately contribute to the economy. Although digital-learning initiatives have advanced in developed countries, it is still in its early phases in many developing countries, including Sri Lanka. This study focuses on structural, relational, and cognitive dimensions of social capital and provides a new theoretical framework to examine its relationship to digital educational success. The study uses a quantitative approach where the data is collected from University students in Sri Lanka using a survey deployed online. The model is validated using the structural equation modeling technique. The findings of this study indicated that the three dimensions of social capital positively influence the success of digital education at the tertiary level. Further, this paper contributes to the existing literature on Social Capital Theory and provides valuable insights and recommendations for policymakers in the educational sector on improving digital learning achievements.
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    Solution to Measure Employee Productivity with Employee Emotion Detection
    (IEEE, 2022-12-09) De Silva, T.R.S.; Dayananda, K.Y.; Galagama Arachchi, R.C.; Amerasekara, M.K.S.B.; Silva, S; Gamage, N
    Health and safety of workers has become a top priority in modern businesses. The reason is that it will have an impact on both individual and team output. In the last few decades, automatic facial expression analysis using machine learning has emerged as a promising and bustling field of study. In this study, the system primarily evaluates the efficiency of workers and, through the detection of their emotional states, determines their levels of motivation. The task completion rate of employees is measured by the system in the first component, and the system predicts the level of satisfaction that the employees will have. In place of linear regression, this component makes use of random forest regression, which boasts a higher degree of precision than its counterpart. The performance of workers on their tasks will be evaluated periodically, about once every fifteen minutes, and the results will be shown on a dashboard. The system will pick up on the emotions of the staff members throughout the second phase of the process. These characteristics will be used to assess the level of motivation inside the organization, with the end goal of increasing overall productivity. The accuracy of this emotion detection will also be checked periodically, namely once every fifteen minutes. The following part of the process monitors the use of the PC and calculates the level of productivity. It will be possible to get an increase in productivity if one monitors and keeps track of the application usage of each employee. The final components monitor the websites that employees visit and how they use the network. This component makes it easier to generate reports based on the utilization of the internet and the network, as well as information on performance and reports that summarize website traffic. When it is fully operational as an integrated system, most businesses will rely on this system as their primary driver of success.
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    Mobile Base Solution for Individuals with Limited Knowledge About Cars
    (IEEE, 2022-12-09) Nammunige, H; Chamuditha, T; Udara, S; Athapaththu, D; Gamage, A; Gamage, N
    Different modes of transportation were discovered by our ancestors from ancient times. Currently, the majority of people choose to purchase a personal automobile for transport needs. However, the vast majority of people are not automobile industry experts. As a result, the majority of people have trouble when recognizing cars. Due to numerous variations of a single vehicle model, even an expert has trouble correctly identifying a certain car model. People must take into account a number of factors before purchasing a specific automobile. Some of crucial factors are service costs and future market prices. Ordinary people require the assistance of a professional when estimating the market price of a car and calculating the cost of servicing a car. Accidents can also occur at any time when driving a car often. In similar circumstances, consumers require the assistance of an insurance agent or a technician to estimate the cost of damage repair. In this study, we provide a way for non-automotive experts to use their smartphones to identify car models, forecast future market prices, determine and forecast servicing costs, and estimate minor damage repair costs. This paper demonstrates how we accomplished aforementioned tasks using YOLO V4, Multiple Linear Regression, Random Forest Classifier and Faster RCNN.
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    LAWSUP - A Smart Platform to Assist Stakeholders of Business Law
    (IEEE, 2022-12-09) Sulakshi, U L H; Opatha, S D; De Silva, K S D; Sandeepa, M M A D N; Nawinna, D; Harasgama, H; Gamage, N
    Corporate law, sometimes known as business law, is the body of law that governs the rights, relationships, and behavior of persons, corporations, organizations, and businesses. Business Organizations, employees/laborers, and the public are involved in this area of the law accompanying lawyers, and legal advisors. Business organizations need legal advice. Employees face many difficulties and injustices at their workplaces. People who wish to start a new business, search for legal guidance. When one of these parties needs support, they must seek a lawyer, go to the lawyer, and get legal support. When delivering legal support to clients, lawyers are still going through a manual process. There are very few systems that have been implemented for the law domain so far, and those only search engine types of systems that are unable to support every stakeholder of this domain. There is no common platform for all these stakeholders to find solutions, connect with a good lawyer and get support. We have identified the main issues faced by business organizations, employees that need legal support, the general public, and lawyers, and developed a web solution by implementing Machine Learning, Classification Algorithms, Text mining, Natural Language Processing, and Web Crawlers.
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    An Approach of Enhancing the Quality of Public Transportation Service in Sri Lanka using IoT
    (Institute of Electrical and Electronics Engineers, 2022-10-15) Weligamage, H. D; Wijesekara, S. M; Chathwara, M.D.S.; Isuru Kavinda, H.G.; Amarasena, N; Gamage, N
    Traveling is one of the necessary and common behavior of any society. Thus, there are many ways of human travel. Due to the fact that Sri Lanka is still a developing country, the vast majority of the population rely on public transit as opposed to private transportation options. In this situation, public and private bus services are the most common means of transportation for people. People who use bus service for daily traveling face lot of issues due to the delays in bus arrivals, missing the bus or excessive crowd in the bus. This proposed system is intended to make bus travel more efficient and convenient for those who rely on buses as their primary means of public transit. This system provides a mobile application for passengers to utilize in order to observe the real-time position of the buses, as well as their anticipated arrival time, current passenger count, and a visualization of the available seat locations within the vehicle prior to the arrival of the bus. Besides, traditional manual ticketing procedure also cause many difficulties for the passengers like need of carrying changed money each time they travel. To avoid this serious problem, this system introduces a non-interactive automated ticketing system which has a smart card that can be tracked in a RFID zone and an automated fee calculating system using a logical conceptual algorithm considering environmental factors. Along with this a digital ticket is issued including all the required details of a journey. In addition to that, this system has a two-factor authentication process that makes use of face recognition to validate the user's identity before granting access to their smart card. The goal of this application is to provide a systematic solution for the typical challenges that public transportation users face in order to improve the service quality by using IoT-based technologies and image processing.
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    A Smart Aquaponic System for Enhancing The Revenue of Farmers in Sri Lanka
    (IEEE, 2022-10-19) Ekanayake, D; de Alwis, P; Harshana, P; Munasinghe, D; Jayakody, A; Gamage, N
    Sri Lanka's agricultural sector confronts serious challenges from fertilizer shortages and agriculture-related chemical scarcity. Innovations comparable to aquaponic systems may be offered to Sri Lankan farmers to overcome these difficulties using IoT and ML technology. This research scope is to implement a smart and secure aquaponic environment monitoring system to forecast plant and fish growth factors, provide Sri Lankan farmers with insights into the environment's behaviors, and take measures according to the predictions utilizing control mechanisms. In this research, more exact predictions have been generated by the Random Forest algorithm model rather than the LSTM model, and most of the investigated parameters given good accuracy according to the absolute mean error (Media TDS-1.95, Media pH-0.06, Media Temperature-0.49, Env. Temperature- 0.94, Env. Humidity-2.70) except the environment light intensity (64.11). The ML solution studied in this research paper would increase the quality of traditional agriculture in Sri Lanka for greater productivity and economic benefit.