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Author: search.filters.author.Perera, JHas files: Yes

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Now showing 1 - 7 of 7
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    A Scalable Microkernel Inspired Architecture for Transparent and Adaptive Supply Chain Management
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Vithanage H.D; Dehipola H.M.S.N; Manditha K.D.R.; Weedagamaarachchi K.S.; Jayasinghearachchi, V; Perera, J
    Supply Chain Management (SCM) in industries such as coconut peat production increasingly demands systems that are not only efficient but also transparent and adaptable to rapid changes. Traditional monolithic architectures often fail to scale effectively and struggle to integrate emerging technologies such as IoT and blockchain, limiting their usefulness in dynamic environments. To address these limitations, this research proposes a microkernel-inspired architecture tailored for SCM. The architecture separates core system functions from domain-specific processes through the use of dynamically loadable plugins, allowing for modularity, fault isolation, and real-time adaptability. The impact of this study lies in its integration of multiple technologies to enhance transparency and operational flexibility. The system incorporates IoT sensors for real-time data acquisition, blockchain for immutable traceability, gRPC for high-performance communication, and K3S for lightweight container orchestration. A workflow customization tool allows non-technical users to define or modify supply chain processes without altering core logic. We employed the Architectural Trade-off Analysis Method (ATAM) and Cost-Benefit Analysis Method (CBAM) to evaluate architectural decisions and conducted runtime performance testing using simulated workloads. Results showed improved scalability, flexibility, and fault tolerance, with moderate latency introduced by inter-process communication and CGo overhead. Despite some limitations in performance variability, the architecture maintained high availability and was able to recover from plugin failures seamlessly. These findings suggest that the proposed model provides a viable foundation for next-generation SCM systems. The contributions include a modular, resilient framework that effectively integrates advanced technologies to support adaptable and transparent supply chain operations across various industries.
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    Intelligent Water Quality Monitoring and Prediction System
    (Institute of Electrical and Electronics Engineers Inc., 2025-06-25) Shiraz, S; Karunasena, K; Mudelige, H; Kumarasinghe, O; Nawinna, D; Perera, J
    The paper aims to develop an integrated approach to improve water treatment processes using predictive modeling and SCADA integration in order to meet the specific needs of water purification systems in Sri Lanka. The current systems utilized for this need are outdated since these systems are based on traditional technologies and do not have the means for predictions or real-time data accessibility outside the system. The proposed solution will focus on raw water quality prediction, optimization of chemical usage to bring in efficiency, sustainability, and resource management, ensuring seamless access to all the relevant data required to manage and monitor. In order to achieve this, past data provided by the Meewatura water plant in Sri Lanka, sourced from the Mahawali river, is utilized for the relevant predictions alongside of the data gathered through the SCADA system. The data is not directly accessible since the SCADA system is mainly built for monitoring, and in order to get the data, a MODBUS connection through the PLC is utilized alongside of an IOT device. In addition to the extracted data, past data that was provided by the water plant is also incorporated. The combined data set is utilized for the predictions while continuously improving itself with new data. The present study contributes to the establishment of sustainable and adaptable water treatment frameworks for a wide range of operational needs within the water plants by addressing the gaps in the existing water quality management systems and improving upon them.
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    Optimizing Gas Efficiency in Ethereum Smart Contracts using Design Pattern Refinement and Complexity Reduction
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Weedagamaarachchi K.S; Vithanage H.D; Dehipola H.M.S.N; Manditha K.D.R; Jayasinghearachchi, V; Perera, J
    Smart contracts on the Ethereum blockchain enable automation and transparency in decentralized applications; however, their scalability is often constrained by high gas costs resulting from inefficient code design. This research investigates the relationship between cyclomatic code complexity and gas consumption, proposing a design pattern-based approach to optimize gas efficiency in Ethereum smart contracts. Three common Solidity patterns Factory, Registry, and State Machine were optimized using both existing and novel techniques, including variable packing, the use of fixed-size data types (uint256, bytes32), immutable variables, and mapping simplification. These optimizations were implemented and tested within a real-world coco peat supply chain management system to measure their impact on gas usage. Experimental results showed that deployment gas costs decreased by approximately 19% and runtime execution gas by around 14%, confirming that design-level optimization can significantly reduce costs without affecting functionality. The findings demonstrate that structured refinement of contract design can enhance scalability, making blockchain-based enterprise solutions more efficient and economically sustainable.
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    Evaluating Genetic, Reinforcement Learning, and Colony Optimization Algorithms for Scalable University Timetable Scheduling
    (Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Weerasinghe K.D.E.I.; Wijayawardhana G.L.C.N.D.; Udayantha D.M.S.; De Silva K.H.P.N.; Perera, J; Hathurusinghe, S
    University timetable scheduling, a complex optimization task, often lacks comparative algorithmic analysis under standardized, resource-scarce conditions. This research introduces an integrated framework to benchmark Genetic Algorithms (GA), Reinforcement Learning (RL), and Colony Optimization (CO) under identical, extreme resource scarcity (4 rooms for 195 activities), a key differentiator. Using standardized metrics, results revealed distinct trade-offs: GAs offer rapid initial solutions but lack flexibility; RL shows efficient resource use but requires extensive training; CO demonstrates adaptability. All algorithms struggled to assign every activity due to imposed resource limits, highlighting performance under severe contention. The developed platform, with configurable scoring and role-based access, is a practical contribution, offering data-driven insights for algorithm selection. This study offers systematic three-way comparison of these techniques under uniform, high-pressure criteria, informing future hybrid solutions.
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    Smart Agricultural Platform for Sri Lankan Farmers with Price Prediction, Blockchain Security, and Adaptive Interfaces
    (Institute of Electrical and Electronics Engineers Inc., 2025-06-25) Kuruppu K.A.G.S.R; Kandambige S.T; Perera W.H.T.H.; Cooray N.T.L; Nawinna, D; Perera, J
    Improper management of seed demand in Sri Lanka's agricultural sector can result in market imbalances, affecting farmers' decision-making and supply chain efficiency. This research introduces an integrated system for monitoring vegetable seed demand using digital technologies. The proposed system utilizes machine learning techniques to predict vegetable prices, a blockchain network for secure transactions, and a reward-based system to encourage user engagement. It also incorporates an adaptive user interface to accommodate different levels of digital literacy, ensuring accessibility for all farmers, especially senior citizens. Furthermore, the system features an AI Chatbot powered by Langchain and Pinecone, offering domain-specific responses and real-time support for farmers. The solution aims to combine advanced technology with agricultural practices to improve seed demand forecasting, promote transparency in transactions, and ensure a more efficient supply chain. This paper presents a multi-component agricultural platform that integrates predictive analytics, blockchain-secured transactions, gamified incentives, and adaptive user interfaces to support farming decision-making. The system combines machine learning for price forecasting, dynamic reward mechanisms to drive user engagement, and personalized UI/UX optimizations tailored for diverse user groups, including senior farmers. A multilingual AI-powered chatbot enhances accessibility and real-time support, enabling a robust, transparent, and inclusive digital solution for agricultural supply chain management.
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    Intelligent Water Quality Monitoring and Prediction System
    (Institute of Electrical and Electronics Engineers Inc., 2025) Shiraz, S; Karunasena, K; Mudelige, H; Kumarasinghe, O; Nawinna, D; Perera, J
    The paper aims to develop an integrated approach to improve water treatment processes using predictive modeling and SCADA integration in order to meet the specific needs of water purification systems in Sri Lanka. The current systems utilized for this need are outdated since these systems are based on traditional technologies and do not have the means for predictions or real-time data accessibility outside the system. The proposed solution will focus on raw water quality prediction, optimization of chemical usage to bring in efficiency, sustainability, and resource management, ensuring seamless access to all the relevant data required to manage and monitor. In order to achieve this, past data provided by the Meewatura water plant in Sri Lanka, sourced from the Mahawali river, is utilized for the relevant predictions alongside of the data gathered through the SCADA system. The data is not directly accessible since the SCADA system is mainly built for monitoring, and in order to get the data, a MODBUS connection through the PLC is utilized alongside of an IOT device. In addition to the extracted data, past data that was provided by the water plant is also incorporated. The combined data set is utilized for the predictions while continuously improving itself with new data. The present study contributes to the establishment of sustainable and adaptable water treatment frameworks for a wide range of operational needs within the water plants by addressing the gaps in the existing water quality management systems and improving upon them.
  • Thumbnail Image
    ItemEmbargo
    Smart Agricultural Platform for Sri Lankan Farmers with Price Prediction, Blockchain Security, and Adaptive Interfaces
    (Institute of Electrical and Electronics Engineers Inc., 2025) Kuruppu K.A.G.S.R.; Kandambige S.T; Perera W.H.T.H; Cooray N.T.L; Nawinna, D; Perera, J
    Improper management of seed demand in Sri Lanka's agricultural sector can result in market imbalances, affecting farmers' decision-making and supply chain efficiency. This research introduces an integrated system for monitoring vegetable seed demand using digital technologies. The proposed system utilizes machine learning techniques to predict vegetable prices, a blockchain network for secure transactions, and a reward-based system to encourage user engagement. It also incorporates an adaptive user interface to accommodate different levels of digital literacy, ensuring accessibility for all farmers, especially senior citizens. Furthermore, the system features an AI Chatbot powered by Langchain and Pinecone, offering domain-specific responses and real-time support for farmers. The solution aims to combine advanced technology with agricultural practices to improve seed demand forecasting, promote transparency in transactions, and ensure a more efficient supply chain. This paper presents a multi-component agricultural platform that integrates predictive analytics, blockchain-secured transactions, gamified incentives, and adaptive user interfaces to support farming decision-making. The system combines machine learning for price forecasting, dynamic reward mechanisms to drive user engagement, and personalized UI/UX optimizations tailored for diverse user groups, including senior farmers. A multilingual AI-powered chatbot enhances accessibility and real-time support, enabling a robust, transparent, and inclusive digital solution for agricultural supply chain management.