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The novel IoT-based data sensing and service mode promotes the booming development of crowdsensing-based mobile communication services (MCSs). MCS facilitates people’s daily lives by providing appropriate services according to the user’s mobile travels. These traveling trajectories, combined with open-source network information, reveal multimodal semantic information implicit in user mobility. Mining these mobile semantics contributes to understanding user mobility more sufficiently. It covers a wide spectrum of applications in mobile scenarios. For service providers, it improves the quality of their services. For mobile users, it helps to design a more rigorous privacy-preserving mechanism. For third-party platforms, such mobility analysis enhances their data management, analysis, and reusage. It has always been an open research issue in mobile computing. We are motivated to conduct a complete and comprehensive survey on semantic mining within the scope of MCS, forming a complete overview of mobile semantic perception. Specifically, we first review existing research works on feature selection. We classify them into five categories, depending on their representation forms. Then, we summarize the research on mobile semantic perception and cluster them to be three groups according to the digging depth of the represented semantics. To complete the overview, we also review the applications of learning algorithms and discuss the open opportunities and challenges for future works.
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Stored only when both papers are in the corpus — 2 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.