A Scalable Cloud Architecture for Intelligent Automation in Distributed IoT Sensor Networks for Semiconductor Test Environments
DOI:
https://doi.org/10.15662/IJARCST.2026.0904004Keywords:
Cloud Computing, Edge Computing, Internet of Things, Automation, Multi-Tenancy, Data Management, Data Analytics, Device and Resource Orchestration, Cloud-native automation, Distributed IoT sensor networks, Smart manufacturing systems, Semiconductor chip testing, Edge–cloud orchestration, Real-time data analytics, Automated test equipment (ATE) integration, Scalable microservices architecture, Predictive fault detection, Industrial IoT (IIoT) security and reliabilityAbstract
Cloud-based smart automation architecture is proposed for managing a distributed network of low-cost, heterogeneous, redundancy-provisioned Internet-of-Things (IoT) sensors deployed in semiconductor chip testing environments. Technology discovery research is guided by the need for continuous physical experimentation of automated testing procedures in dedicated engineering laboratories that require expensive chip-testing equipment, not always available at use time. Chip test automation without on-demand access to the dedicated equipment is also pursued during University Business and Industry collaboration, being made available when dedicated equipment for traditional tests is either busy being operated, maintained, or under repair. However, such dedicated equipment has not been made available for node testing.
The architecture comprises edge-cloud components, and these interact with a sensor network that includes a large number of devices for monitoring a variety of physical variables. Process control and monitoring devices connected to the equipment operate under an edge-cloud architecture; processing piles of data, and supervision and control signals are exchanged with the cloud. At an appropriate processing stage, data is available for incorporation into an IoT-sensor constellation and is distributed to support Smart City and other applications. The design decisions and technical choices assist several cloud management and automation functions and address requirements and constraints such as service level agreement compliance, data quality and governance, device onboarding and admission control, action triggering and execution, plug-in/play capability, process and playback recording, and budget-friendly maintenance of heterogeneous multi-device clouds.
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