Rule-Based Reasoning for Hardware Fault Detection in IoT Systems Using Electronic Design Automation Tools

In the rapidly evolving landscape of hardware design, particularly within Internet of Things (IoT) and embedded systems, the detection and diagnosis of hardware faults pose significant challenges. This paper introduces Sem4EDA, an ontology tailored for enhancing fault detection in Electronic Design Automation (EDA) tools. By leveraging the power of Semantic Web technologies, Sem4EDA models hardware components, faults, and operational behaviors, creating a structured knowledge base that can be queried for fault identification. We utilize SPARQL (Protocol and RDF Query Language) queries to extract meaningful insights from the ontology, providing a systematic approach to fault detection. A use case scenario using temperature sensors in a smart city environment demonstrates how this methodology can not only identify hardware issues but also suggest optimizations, such as energy efficiency improvements. This approach, combining ontologies and EDA tools, has the potential to streamline fault detection processes and improve overall system reliability.