Research Publications
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Publication Open Access Analysis and optimization of OLSR and AODV routing protocols for highly mobile autonomous aerial vehicle networks: experimental performance evaluation in various application scenarios(Faculty of Engineering, 2026-01) Duc Tu, N; Gorbacheva, L; Muthanna, ADriven by the rapid evolution of Autonomous Aerial Vehicle (AAV) technology, ad-hoc AAV networks are becoming increasingly significant in diverse domains such as telecommunications, security surveillance, search-and-rescue operations, emergency management, precision agriculture, and cargo transportation. Owing to their flexible deployment and tight coordination capabilities, AAVs enable real-time data exchange, unlocking considerable potential for tasks that demand high accuracy and swift response. Nevertheless, their three-dimensional mobility and continuously changing topology impose substantial challenges on the design of suitable routing solutions, because conventional protocols originally developed for Mobile Ad-hoc Networks (MANETs) are seldom optimized for aerial characteristics. This discrepancy underscores the necessity for a comprehensive analysis and optimal configuration that satisfy the stringent requirements of low latency, high throughput, and reliability in mission-critical AAV applications. Against this backdrop, the present study focuses on the analysis and modelling of two widely adopted routing protocols—Optimized Link State Routing (OLSR) and Ad hoc On-Demand Distance Vector (AODV)—in various deployment contexts of ad-hoc AAV networks. By evaluating the performance of these protocols, we identify their advantages, limitations, and possible enhancement directions, and subsequently propose configuration guidelines that deliver improved link quality.Publication Open Access Development Of An Ai-Based Model With Low Computational Complexity For Accurate Solar Energy Forecasting(Faculty of Engineering, 2025-09-09) Chandrasinghe, S; Fernando, NThis paper introduces a short-term solar energy forecasting model that is designed with a focus on low computational complexity and addresses the challenges posed by fluctuations in solar energy generation, which are significantly influenced by environmental factors. These fluctuations can lead to instability when solar power generation systems are integrated into national energy grids, creating difficulties in maintaining a balanced supply and demand. If solar energy generation can be accurately forecasted before fluctuations occur, potential issues can be identified in advance, allowing for better management of the energy system, including optimizing storage facilities when energy generation is high. Current solar energy forecasting systems face significant challenges due to their high computational complexity, which results in increased power consumption and lower accuracy. To address these issues, this study focuses on the development of an artificial intelligence (AI)-based forecasting model using an Artificial Neural Network (ANN). The goal is to reduce the computational complexity of the model while maintaining high accuracy. To achieve this, various data analysis and complexity reduction techniques, such as variable reduction, pruning, and quantization, were applied. The performance of the optimized AI model was evaluated by comparing the forecasted values to actual solar energy generation data. The results demonstrate that the proposed model successfully reduces computational complexity while maintaining a satisfactory level of accuracy. This optimization makes the model more suitable for real-time forecasting, particularly in resource-constrained environments, and provides a more efficient approach to solar energy management. The findings of this study suggest that AI-based forecasting models can play a critical role in enhancing the integration of solar energy into national grids, ensuring a more reliable and sustainable energy supply. Further research could explore additional optimization techniques and the introduction of generalization techniques to improve transferability of the model and applicability across diverse geographical regions. Additionally, focus on utilizing AI techniques that minimize computational complexity without compromising the accuracy of the model, aiming to maintain high forecasting precision while optimizing the efficiency of the system.Publication Open Access Optimization of VISSIM Driver Behavior Parameter Values Using Genetic Algorithm(Creative Commons Attribution, 2023-02-13) Gunarathne, D; Amarasingha, N; Kulathunga, A; Wicramasighe, VModeling effective vehicular traffic is a highly contested topic, especially in developing countries like Sri Lanka, which has a wide range of driving conditions. VISSIM microsimulation software is currently used by Road Development Authority (RDA) and relevant authorities to perform traffic management solutions in Sri Lanka. However, it is required to do modifications to the existing driver behavior parameter values to effectively reflect the realistic traffic conditions observed in the real-world in the simulated model. The main purpose of this study is to calibrate the VISSIM driver behavior parameter values using a genetic algorithm (GA). The methodology and results of the VISSIM model’s sensitivity analysis and calibration, which was developed for the Malabe three-legged signalized intersection, are presented in this study. A sensitivity analysis was used to find the most sensitive driver behavior parameters. Using the multi-objective GA optimization tool in the MATLAB software's optimization toolbox, the optimum driver behavior parameter values for these identified most sensitive driver behavior parameters were determined. The findings revealed that GA optimization is effective in reducing the difference between observed and simulated results.Publication Embargo AgroPro: Optimizer for Traditional Agricultural System in Sri Lanka(IEEE, 2022-12-09) De Silva, D.I.; Suriyawansa, G.M. T. K. D. S.; Senevirathna, M.R. U. M. T.; Balasuriya, I.D. I.; Deshapriya, A. G. S. P.; Gadiarachchi, G. A. D. K. M.Today, in many countries around the world, big data analysis and machine learning methods are used for industrial development. However, such techniques are rarely used in Sri Lankan agricultural industry. The success of agriculture depends heavily on the selection of the right crop. Choosing the right crop depends primarily on predicting future yields. Machine learning methods can be used very successfully to make future predictions about crop yields. Crop prediction mainly depends on the soil, geography, and climate of the growing location. Hence historical data with agricultural facts such as temperature, humidity, pH, and rainfall are used to predict yield as parameters in the machine learning function. Sri Lanka uses a traditional approach to distribute fertilizers among farmers. Not having an organized way to distribute fertilizers to the needed areas leads to many abnormalities along the way. As a result, the country is facing economic losses and resource wastage. Having an optimized distribution network is the key to overcoming those abnormalities. This research assesses the efficiency of the fertilizer distribution system and consists of time-series predictions on fertilizer usage to gain future value. The aim is to identify performance gaps in distribution management that lead to delayed fertilizer distribution affecting agricultural productivity.Publication Open Access Sustainable manufacturing: application of optimization to textile manufacturing plants(Global Journals, 2020-10-21) Liyanage, I; Nuwanga, S; Anjana, R; Rankothge, W; Gamage, NThe main goal of manufacturing industry is to produce the end products on time with good quality and keep the resource wastage low. However, manufacturing industry face several challenges such as bottle necks in the workflow, unsynchronized production, and sudden increase in product demands.In this paper, we are proposing a management platform for textile manufacturing plants with following modules: (1) sewing workflow optimization (2) quality assurance workflow optimization and (3) finishing workflow optimizations. We have used Genetic Programming (GP) approach, to optimize the workflows, considering different factors that affect each workflow. Our results show that, using our proposed platform, the manufacturing workflows can be optimized and reduce the bottle necks in the workflows and resource wastage in the manufacturing plant.Publication Open Access Three Layer Super Learner Ensemble with Hyperparameter optimization to improve the performance of Machine Learning model(Faculty of Technology, USJ, 2021-03-13) Kasthuriarachchi, K. T. S; Liyanage, S. RA combination of different machine learning models to form a super learner can definitely lead to improved predictions in any domain. The super learner ensemble discussed in this study collates several machine learning models and proposes to enhance the performance by considering the final meta- model accuracy and the prediction duration. An algorithm is proposed to rate the machine learning models derived by combining the base classifiers voted with different weights. The proposed algorithm is named as Log Loss Weighted Super Learner Model (LLWSL). Based on the voted weight, the optimal model is selected and the machine learning method derived is identified. The meta- learner of the super learner uses them by tuning their hyperparameters. The execution time and the model accuracies were evaluated using two separate datasets inside LMSSLIITD extracted from the educational industry by executing the LLWSL algorithm. According to the outcome of the evaluation process, it has been noticed that there exists a significant improvement in the proposed algorithm LLWSL for use in machine learning tasks for the achievement of better performances.Publication Embargo IGOE IoT framework for waste collection optimization(IEEE, 2017-01-27) Lokuliyana, S; Jayakody, A; Rupasinghe, L; Kandawala, SWaste management has become a major issue in all the part of the world and tends to grow day by day. Mismanagement in waste has become one of the key environmental and health issue. With the increase of population, especially in the urban areas waste collection, categorization, and disposal has become a major hazard for the government authorities. An IoT based waste collection framework is proposed to automate the solid waste identification, localization and collection process. The authors are involved in the identifying key impact factors in the waste collection process and provide systematic and automated solution to optimize the process to achieve higher efficiency. A layered architecture is introduced to handle the waste collection process and an optimization algorithm is derived for the existing business process based on the proposed evaluation criteria. The final outcome is a complete framework which compromises the Inputs, Outputs, Guide and Enables. The main objective is to implement an optimized automated waste collection system with the use of a vast sensor network capable of gathering waste data and by implementing an optimization algorithm in waste collection.Publication Embargo IGOE IoT framework for waste collection optimization(IEEE, 2017-01-27) Lokuliyana, S; Jayakody, J. A. D. C. A.; Rupasinghe, L; Kandawala, SWaste management has become a major issue in all the part of the world and tends to grow day by day. Mismanagement in waste has become one of the key environmental and health issue. With the increase of population, especially in the urban areas waste collection, categorization, and disposal has become a major hazard for the government authorities. An IoT based waste collection framework is proposed to automate the solid waste identification, localization and collection process. The authors are involved in the identifying key impact factors in the waste collection process and provide systematic and automated solution to optimize the process to achieve higher efficiency. A layered architecture is introduced to handle the waste collection process and an optimization algorithm is derived for the existing business process based on the proposed evaluation criteria. The final outcome is a complete framework which compromises the Inputs, Outputs, Guide and Enables. The main objective is to implement an optimized automated waste collection system with the use of a vast sensor network capable of gathering waste data and by implementing an optimization algorithm in waste collection.Publication Open Access Multi-objective optimization of urban wastewater systems(2012-07) Rathnayake, U. S; Tanyimboh, T. I. K. U .TCombined sewer overflows (CSOs) are one of the environmental problems in many cities. Damage to the natural environment by these CSOs is considerable. Controlling urban wastewater systems is one possible way of addressing the environmental issues from CSOs. However, controlling urban sewer systems optimally is still a challenge, when considering the receiving water quality effects. In this study, a multi-objective optimization approach was formulated considering the pollution load to the receiving water from CSOs and the cost of the wastewater treatment. The optimization model was tested using an interceptor sewer system. Results from the study show some promising findings.Publication Embargo Maximum mutual information design for MIMO systems with imperfect channel knowledge(IEEE, 2010-09-13) Ding, M; Blostein, S. DNew results on maximum mutual information design for multiple-input multiple-output (MIMO) systems are presented, assuming that both transmitter and receiver know only an estimate of the channel state as well as the transmit and receive correlation. Since an exact capacity expression is difficult to obtain for this case, a tight lower-bound on the mutual information between the input and the output of a MIMO channel has been previously formulated as a design criterion. However, in the previous literature, there has been no analytical expression of the optimum transmit covariance matrix for this lower-bound. Here it is shown that for the general case with channel correlation at both ends, there exists a unique and globally optimum transmit covariance matrix whose explicit expression can be conveniently determined. For the special case with transmit correlation only, the closed-form optimum transmit covariance matrix is presented. Interestingly, the optimal transmitters for the maximum mutual information design and the minimum total mean-square error design share the same structure, as they do in the case with perfect channel state information. Simulation results are provided to demonstrate the effects of channel estimation errors and channel correlation on the mutual information.