A Survey of Smart Campus Resource Information Management in Internet of Things
The rapid development of information technology, especially the advancements in fields such as the Internet of Things (IoT), is driving significant changes in campuses. This change has a wide impact, covering many fields, such as educational resource allocation, student services, and campus security...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10960407/ |
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| Summary: | The rapid development of information technology, especially the advancements in fields such as the Internet of Things (IoT), is driving significant changes in campuses. This change has a wide impact, covering many fields, such as educational resource allocation, student services, and campus security. In this context, smart campus resource information management (SCRIM) has emerged as a key strategy to facilitate these transformations. SCRIM leverages IoT to collect resource information on campus space, environment, transportation, energy, emissions, water, and various events. By comprehensively analyzing this resource information, SCRIM can implement measures such as energy-saving control, optimized space management and allocation, and disaster management to achieve the best utilization of campus resources. Although it has gradually gained popularity, current research lacks a comprehensive research summary. This study mainly selected SCRIM-related literature for investigation between 2019 and 2024. Evaluation criteria were established through campus resource information, security and privacy, and SCRIM framework. This paper introduces SCRIM, detailing its models and technologies, and emphasizes the importance of occupancy detection and the integration of knowledge graphs to facilitate more efficient data analysis. It provides a detailed comparison, analysis, and discussion of various SCRIM frameworks, highlighting their current challenges and issues. Finally, this paper provides improvement suggestions for SCRIM frameworks and outlines future work directions. |
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| ISSN: | 2169-3536 |