Browsing by Author "Jayasinghearachchi, V"

Filter results by typing the first few letters
Now showing 1 - 5 of 5
  • Thumbnail Image
    ItemEmbargo
    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.
  • Thumbnail Image
    PublicationEmbargo
    Children's Behavior Analysis Through Smart Toys
    (Institute of Electrical and Electronics Engineers Inc., 2022-11) Ramesha, M. D. D; Kavindi, M. V; Somawansa, R.P; Yadav, A; Samarasinghe, P; Wedasinghe, N; Jayasinghearachchi, V
    Analyzing children's behavior is a major part of pediatric psychological studies. Here we are going to use the hand movements of the child to understand the behavioral pattern with the help of IoT-based toys. © 2022 IEEE.
  • Thumbnail Image
    ItemEmbargo
    Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture
    (Institute of Electrical and Electronics Engineers Inc., 2025) Kariyawasam K.P.W.D.V.; Kahandagamage P.N.; Fernando M.R.R.; Fernandopulle J.M; Jayasinghearachchi, V; Dissanayaka, K
    In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments.
  • Thumbnail Image
    ItemEmbargo
    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.
  • Thumbnail Image
    ItemEmbargo
    OrchiZen: Hybrid Integrated Smart Farming System for Orchid Plantations
    (Institute of Electrical and Electronics Engineers Inc., 2025) Wijendra, D; Jayasinghearachchi, V; Dilshan O.A.P.; Herath H.M.K.C.B; Yapa Y.M.T.N.S; Rathnasiri K.D.M.M.
    OrchiZen is a hybrid integrated smart farming system designed for orchid cultivation, leveraging Machine Learning (ML) and Internet of Things (IoT) technologies to address key horticultural challenges, including irrigation, disease treatment, choice of species, lighting, and nutrients. The OrchiZen has smart irrigation advisory, species recommendation, Ultraviolet (UV) based disease treatment, light optimization, and fertilizer advisory. The priorities are given to specific species such as Dendrobium, Vanda, and Phalaenopsis. The realities of telemonitoring, data processing, and forecasting increase organizational productivity and contribute to better environmental management. The outcomes illustrate that existing modern technologies can enhance the output and ecology of the orchid production to a significant extent, redefining the conventional technologies.