Abstract
The Social Internet of Things (SIoT) paradigm incorporates social networking concepts with the Internet of Things (IoT) solutions to support novel services. The massive amount of data (big data) produced by SIoT necessitates efficient information processing frameworks to exploit social relationships and comprehend actionable information from real-world observations. Data from AI-enabled sensors (AIS) is typically geo-tagged, thus demanding geo-textual processing for information retrieval and analysis. Social media applications are the main source of geo-textual data as mobile users connect with millions of posts daily. The processing of big geo-textual data requires resource-efficient algorithms and frameworks. Clustering algorithms are often applied to geo-textual data to examine spatial, textual, and temporal information for event detection, sentiment analysis, and search query response. Clustering algorithms on big data are resource-hungry requiring comparisons among all data points to calculate similarity and distance metrics. Existing hybrid clustering techniques execute algorithms collectively on geo-textual data resulting in a enormous footprint for big data. We propose a resource-efficient clustering framework for AIS that hierarchically performs geo-textual clustering without significantly lowering the clustering quality. The proposed framework achieves substantial time and memory efficiency while reducing the overall resource requirements for constrained end-user and edge devices compared to the standard hybrid geo-textual clustering framework. Moreover, we augment the research work by developing open-source scripts for both hierarchical and hybrid clustering frameworks.