(untitled) (Computer systems and information technologies, 2025)
Today, many cyber-physical systems (CPS) rely on local decision-making frameworks that often fail to address both precise thresholds and the ambiguity inherent in sensor data. There is a need to develop a scalable, cloud-based decision support system (DSS) that unifies crisp rule evaluation with fuzzy logic to improve decision accuracy and responsiveness across diverse applications. The aim of this paper is to design and implement a cloud-hosted crisp-fuzzy rule management system that supports centralized rule administration and asynchronous processing for multiple CPS domains. Our approach employs a microservices architecture within a Microsoft Azure environment, comprising three core APIs: User Management, Knowledge Management, and Decision Support. The system integrates secure multi-tenant access using external identity providers and leverages a PostgreSQL database with a multi-tenant schema. Sensor data from various devices are transmitted via HTTP and queued through Azure Service Bus, thereby decoupling data ingestion from intensive rule evaluation. A background worker, known as the Decision Relay Consumer, processes each incoming message by applying direct threshold comparisons for crisp rules and linear interpolation for fuzzy membership functions, thus handling uncertain sensor readings effectively. Experimental validation using a smart garden simulation demonstrates that the integration of crisp and fuzzy rule evaluations enhances the system’s ability to prioritize and trigger appropriate actions in real time. The results confirm that the proposed architecture not only improves decision-making reliability under ambiguous conditions but also reduces on-device computational burdens, facilitating centralized management and scalability. The novelty of this work lies in its unified framework that seamlessly combines crisp thresholds with fuzzy logic in a cloud-based environment, enabling cross-domain applicability and adaptive rule management. The practical significance extends to various industries—including agriculture, manufacturing, and smart buildings—where timely and robust decision-making is essential.