Browsing by Author "Daundasekara D.M.P.D"

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    A Wireless Communication System for Hyper-localized Pest Management using Autonomous Micro-Robot Swarms in Sri Lankan Rice Paddy Fields
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Daundasekara D.M.P.D; Perera K.R.L; Parindya H.S.T; Senarathna K.G.G
    This paper presents a wireless communication framework for hyper-localized pest management in Sri Lankan rice paddy fields using autonomous micro-robots warms. The proposed system integrates Internet of Things (IoT) based sensing units with low-power wireless communication to enable real-time monitoring and coordinated response among micro-robots. A layered architecture combining data acquisition, swarm coordination, and centralized control is introduced to improve responsiveness and minimize pesticide use. Communication reliability, latency, and coverage were analyzed under realistic field constraints, demonstrating the feasibility of the proposed design. Although a Convolutional Neural Network (CNN)-based pest identification module is proposed as part of the future extension, this study primarily focuses on the IoT communication design and coordination aspects. The outcomes indicate that the proposed approach can serve as a scalable and energy-efficient foundation for intelligent pest management systems in precision agriculture.
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    Evaluating Performance and User Perceptions of Multi-Cloud Database-as-a-Service Solutions: A Mixed-Methods Study
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Daundasekara D.M.P.D; Malsha K.K.S; Ilangasinghe R.Y; Ranasinghe R.A.K.D; Haddela, P. S.
    Multi-cloud adoption has accelerated the use of managed Database-as-a-Service (DBaaS) platforms, yet limited research provides provider-agnostic, empirical insight into how performance, scalability, and cost differ across vendors or how these objective outcomes align with practitioner perceptions. This paper presents a mixed-methods evaluation of three major cloud database platforms: Amazon RDS, Azure SQL Database, and Google Cloud SQL. Quantitative benchmarking experiments were conducted using identical workloads on the benchANT framework to measure latency, throughput, and scalability, while a parallel survey collected perception data from academic lecturers, IT employees, and undergraduates. The study's novelty lies in triangulating benchmark results with user perceptions to reveal alignment gaps. Empirical findings show that AWSRDS and Google Cloud SQL deliver strong horizontal scalability, while Azure SQL favors vertical scaling but exhibits higher latency. A moderate correlation (r = 0.62) was found between perceived performance and measured throughput, but perceived reliability showed a weaker relationship to actual latency (r = 0.34), indicating notable perception divergences. Cost analysis further highlights trade-offs involving egress fees, storage tiers, and replica placement. The results provide actionable guidance for workload placement, elasticity planning, and multi-cloud governance, and demonstrate the value of a mixed-methods approach for evaluating cloud database services.