This paper presents Sem4EDA, an ontology-driven and rule-based framework for automated fault diagnosis and energy-aware optimization in Electronic Design Automation (EDA) and Internet of Things (IoT) environments. The escalating complexity of modern hardware systems, particularly within IoT and embedded domains, presents formidable challenges for traditional EDA methodologies. While EDA tools excel at design and simulation, they often operate as siloed applications, lacking the semantic context necessary for intelligent fault diagnosis and system-level optimization. Sem4EDA addresses this gap by providing a comprehensive ontological framework developed in OWL 2, creating a unified, machine-interpretable model of hardware components, EDA design processes, fault modalities, and IoT operational contexts. We present a rule-based reasoning system implemented through SPARQL queries, which operates atop this knowledge base to automate the detection of complex faults such as timing violations, power inefficiencies, and thermal issues. A detailed case study, conducted via a large-scale trace-driven co-simulation of a smart city environment, demonstrates the framework’s practical efficacy: by analyzing simulated temperature sensor telemetry and Field-Programmable Gate Array (FPGA) configurations, Sem4EDA identified specific energy inefficiencies and overheating risks, leading to actionable optimization strategies that resulted in a 23.7% reduction in power consumption and 15.6% decrease in operating temperature for the modeled sensor cluster. This work establishes a foundational step towards more autonomous, resilient, and semantically-aware hardware design and management systems.

Smart cities increasingly rely on Internet of Things (IoT) ecosystems, cyber-physical infrastructures, and Artificial Intelligence (AI) to manage transportation, energy distribution, environmental sustainability, public safety, and governance. Deep learning techniques provide high predictive accuracy but often lack transparency, explainability, and semantic awareness. These limitations create major concerns in safety-critical urban environments where legal, ethical, and physical constraints must be respected. Neuro-symbolic Artificial Intelligence (NeSy AI) has emerged as a hybrid paradigm combining neural learning with symbolic reasoning, ontologies, and logical inference. By integrating data-driven perception with structured knowledge representation, neuro-symbolic systems offer interpretable and trustworthy urban intelligence. This review investigates state-of-the-art neuro-symbolic AI applications in smart city ecosystems. Following a structured literature selection process, it examines works published between 2020 and 2026 across major scientific databases. Neuro-symbolic architectures are categorized into sequential pipelines, differentiable reasoning models, and knowledge graph-driven frameworks. Furthermore, applications are analyzed across transportation, smart grids, environmental monitoring, and urban governance. The review highlights ontology integration strategies, explainable AI mechanisms, edge intelligence considerations, and computational limitations. To directly address the identified scalability and semantic drift bottlenecks, this paper proposes a novel, unified Edge-to-Cloud conceptual framework. Finally, future research directions are outlined toward cognitive urban infrastructures capable of explainable and semantically grounded decisionmaking.

The Social Internet of Things (SIoT) has emerged as a promising paradigm for addressing interoperability, adaptability, and intelligent collaboration challenges in smart city environments. However, existing solutions often provide only partial support for semantic interoperability, dynamic social relationships, and context-aware service coordination across heterogeneous IoT ecosystems. This paper presents SEMIWARE, a semantic social network-oriented middleware designed to support collaborative, interoperable, and context-aware SIoT applications. SEMIWARE adopts a layered architecture that combines a FIWARE-based middleware backbone with modular services for context management, semantic annotation, semantic reasoning, service discovery, social relationship management, profiling, security, and ontology alignment. Its semantic backbone is provided by an OWL2 ontology that models IoT entities, users, services, contextual information, and trust-aware social relationships. The middleware is validated through two representative applications in distinct domains: smart mobility, where semantic reasoning supports adaptive eco-friendly route computation, and healthcare, where semantically integrated wearable and environmental data support health-event detection for people with dementia. Experimental evaluation further examines the performance of semantic annotation, semantic reasoning, and context management services under increasing workloads. The results provide prototype-level evidence that SEMIWARE supports semantic interoperability, cross-domain adaptability, and graph-based processing under controlled workloads, indicating its potential suitability for complex, data-intensive SIoT applications.

This paper presents Sem4EDA, an ontology-driven and rule-based framework for automated fault diagnosis and energy-aware optimization in Electronic Design Automation (EDA) and Internet of Things (IoT) environments. The escalating complexity of modern hardware systems, particularly within IoT and embedded domains, presents formidable challenges for traditional EDA methodologies. While EDA tools excel at design and simulation, they often operate as siloed applications, lacking the semantic context necessary for intelligent fault diagnosis and system-level optimization. Sem4EDA addresses this gap by providing a comprehensive ontological framework developed in OWL 2, creating a unified, machine-interpretable model of hardware components, EDA design processes, fault modalities, and IoT operational contexts. We present a rule-based reasoning system implemented through SPARQL queries, which operates atop this knowledge base to automate the detection of complex faults such as timing violations, power inefficiencies, and thermal issues. A detailed case study, conducted via a large-scale trace-driven co-simulation of a smart city environment, demonstrates the framework’s practical efficacy: by analyzing simulated temperature sensor telemetry and Field-Programmable Gate Array (FPGA) configurations, Sem4EDA identified specific energy inefficiencies and overheating risks, leading to actionable optimization strategies that resulted in a 23.7% reduction in power consumption and 15.6% decrease in operating temperature for the modeled sensor cluster. This work establishes a foundational step towards more autonomous, resilient, and semantically-aware hardware design and management systems.