A Modular Intelligent Resource Architecture: Optimizing QoS in Edge-Cloud Fusion Systems for Smart Agriculture
Received: 24 June 2025 | Revised: 29 July 2025 and 3 August 2025 | Accepted: 14 August 2025 | Online: 6 October 2025
Corresponding author: Tri Kuntoro Priyambodo
Abstract
Smart agriculture systems increasingly adopt edge–cloud fusion to improve the responsiveness and reduce the data overload. However, many existing models still depend on static or centralized transmission, resulting in high latency and inefficient communication. This paper proposes a Modular Intelligent Resource Architecture (MIRA) that integrates a three-layer fusion model to optimize Quality of Service (QoS) in greenhouse monitoring. The system applies Probabilistic Neural Networks (PNN) at the edge for anomaly filtering and Adaptive Neuro-Fuzzy Inference System (ANFIS) at the cloud for contextual aggregation. Using Network Simulator-3 (NS-3) simulations with 50–300 virtual sensor nodes, three transmission scenarios, namely serial, parallel, and modular, were evaluated. The results show that the modular model significantly reduces the end-to-end delay (35–54 ms), latency (32–50 ms), and improves the response time and Packet Delivery Ratio (PDR) (up to 95%) compared to conventional approaches. The proposed architecture demonstrates superior scalability and reliability under dense network conditions. Its modular design enables efficient, real-time data processing and selective communication, making it well-suited for precision agriculture in resource-constrained environments.
Keywords:
smart agriculture, edge–cloud fusion, modular architecture, quality of service, PNN, ANFISDownloads
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Copyright (c) 2025 Khodijah Amiroh, Tri Kuntoro Priyambodo, Danang Lelono
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