Jose Arturo Garza-Reyes, University of Derby, UK

The semantic location model plays a crucial role in facilitating more intelligent and adaptive indoor Location-Based Services (LBSs), allowing services to be tailored to users' specific contexts and needs. As this model gains traction, it has evolved into a widely adopted framework for defining and managing various types of location information, ensuring that data is not just available, but also contextually relevant and meaningful. To effectively tackle the emerging challenges and opportunities presented by indoor LBSs, this paper explores the application of hybrid semantic methods and theoretical approaches. These innovative solutions aim to enhance the accuracy and responsiveness of LBSs within complex indoor environments, such as shopping malls, airports, and office buildings, where traditional GPS systems may fall short. In our analysis, we delve into several representative semantic location models that incorporate mathematical frameworks and ontological principles. This dual approach allows us to comprehensively assess how different models manage and interpret location data, ultimately influencing their performance in real-world applications. Furthermore, we focus on the conceptual modeling of indoor spaces and investigate how these models can extract and represent the semantics associated with relevant location information in a multilayered framework. By doing so, we shed light on the essential roles these semantics play in enriching the user experience and improving the accuracy of indoor navigation and services. Finally, we identify and discuss future directions for the development of semantic location models specifically tailored for indoor spaces. This includes potential advancements in technology, innovative data integration techniques, and the incorporation of user feedback to create even more robust and adaptable LBSs that meet the evolving demands of users in various indoor environments.

APA:
Garza-Reyes, J. A. (2021). Investigating Semantic Location Models is essential for advancing Indoor Location-Based Services. Journal of Science and Engineering Management, 2(1), 35-44. https://doi.org/10.33832/jsem.2021.2.1.04