A Fog-Based Smart Semantic Web Approach to Support Heterogeneity and Quality of Service in IoT
Hafizur Rahman, Kishore Medhi, Md. Iftekhar Hussain · IETE Journal of Research · 2025
Internet of Things (IoT) is a paradigm that collects raw data from the surrounding environment and analyzes them to extract knowledge that supports decision-making. Heterogeneity is considered one of the key characteristics of IoT. Data heterogeneity hinders the achievement of interoperability among different IoT devices and applications. Semantic web technology is highly adopted to convert such heterogeneous raw sensor data into knowledgeable information and identify real-life events. Collecting data from heterogeneous IoT environments and sending them to the remote cloud requires significant processing time and memory consumption in large-scale IoT. To address these challenges, we proposed a lightweight Fog-based Semantic web approach for supporting Heterogeneity in the IoT gateway (FSH-IoT). The proposed scheme supports heterogeneous devices and data to provide a suitable solution for diverse IoT environments. Furthermore, machine learning algorithm is used to classify events for providing quality of service to critical IoT applications. The proposed FSH-IoT is compared with an open-source semantic sensing middleware for the web of things called Extended Global Sensor Network (XGSN), and a lightweight semantic web approach for data annotation on IoT gateways, Lightweight Semantic Web (L-SW). Simulation results show that FSH-IoT outperforms XGSN and L-SW in terms of data heterogeneity, accuracy by 15% and response time by 30% approximately.