Semantic-Driven Approach for Validation of IoT Streaming Data in Trustable Smart City Decision-Making and Monitoring Systems

• الحاوية / القاعدة: Big Data and Cognitive Computing
• المؤلفون الرئيسيون: Oluwaseun Bamgboye, Xiaodong Liu, Peter Cruickshank, Qi Liu
• التنسيق: مقال
• اللغة: الإنجليزية
• منشور في: MDPI AG 2025-04-01
• الموضوعات: IoT streaming data; internet of things; stream quality validation; semantic technology; smart city model; RDF
• الوصول للمادة أونلاين: https://www.mdpi.com/2504-2289/9/4/108

Ensuring the trustworthiness of data used in real-time analytics remains a critical challenge in smart city monitoring and decision-making. This is because the traditional data validation methods are insufficient for handling the dynamic and heterogeneous nature of Internet of Things (IoT) data streams. This paper describes a semantic IoT streaming data validation approach to provide a semantic IoT data model and process IoT streaming data with the semantic stream processing systems to check the quality requirements of IoT streams. The proposed approach enhances the understanding of smart city data while supporting real-time, data-driven decision-making and monitoring processes. A publicly available sensor dataset collected from a busy road in Milan city is constructed, annotated and semantically processed by the proposed approach and its architecture. The architecture, built on a robust semantic-based system, incorporates a reasoning technique based on forward rules, which is integrated within the semantic stream query processing system. It employs serialized Resource Description Framework (RDF) data formats to enhance stream expressiveness and enables the real-time validation of missing and inconsistent data streams within continuous sliding-window operations. The effectiveness of the approach is validated by deploying multiple RDF stream instances to the architecture before evaluating its accuracy and performance (in terms of reasoning time). The approach underscores the capability of semantic technology in sustaining the validation of IoT streaming data by accurately identifying up to 99% of inconsistent and incomplete streams in each streaming window. Also, it can maintain the performance of the semantic reasoning process in near real time. The approach provides an enhancement to data quality and credibility, capable of providing near-real-time decision support mechanisms for critical smart city applications, and facilitates accurate situational awareness across both the application and operational levels of the smart city.