ABSTRACT
The growing demand for intelligent campus environments has propelled the development of Smart Campus applications that enhance academic, administrative, and social experiences. However, many existing systems are resource-intensive and ill-suited for mobile users, especially in developing regions where infrastructure limitations and device constraints pose significant challenges. This paper proposes a lightweight, mobile-first conceptual model for Smart Campus applications, specifically designed using the Android Software Development Kit (SDK) and integrated with edge computing capabilities. The model aims to deliver real-time services such as attendance tracking, smart notifications, facility access, and environmental monitoring while minimizing latency and reducing dependence on centralized cloud servers. By leveraging Android SDK’s robust mobile development framework, the model supports efficient offline functionality, optimized user interfaces, and hardware integration with mobile sensors. Edge computing is introduced to facilitate localized data processing and enhance responsiveness, ensuring that essential operations can be executed at the network edge even during internet disruptions. The proposed architecture features modular components that allow flexible scaling and integration with legacy systems and IoT devices, making it ideal for resource-constrained educational institutions. A prototype system was developed and tested on Android devices across multiple university environments. Experimental evaluations demonstrated reduced network bandwidth usage, improved application responsiveness, and higher energy efficiency compared to cloud-dependent systems. Usability assessments confirmed strong user engagement and satisfaction, indicating the model’s practical relevance and adaptability. This research contributes a scalable and accessible solution to the Smart Campus domain by combining mobile-first design principles with edge computing, tailored to meet the evolving needs of students, faculty, and administrators. The model promotes digital inclusion by enabling under-resourced institutions to deploy smart solutions without extensive infrastructure investment. Future work will focus on extending cross-platform compatibility, enhancing security protocols, and integrating machine learning capabilities for predictive analytics and decision support.
References
- [1] Adepoju, P. A., Oladosu, S. A., Ige, A. B., Ike, C. C., Amoo, O. O., & Afolabi, A. I. (2022). Next-generation network security: Conceptualizing a Unified, AI-Powered Security Architecture for Cloud-Native and On-Premise Environments. International Journal of Science and Technology Research Archive, 3(2), 270–280. https://doi.org/10.53771/ijstra.2022.3.2.0143
- [2] Adepoju, P. A., Sule, A. K., Ikwuanusi, U. F., Azubuike, C., & Odionu, C. S. (2024). Enterprise architecture principles for higher education: Bridging technology and stakeholder goals. International Journal of Applied Research in Social Sciences, 6(12), 2997-3009. https://doi.org/10.51594/ijarss.v6i12.1785
- [3] Adewoyin, M. A. (2021). Developing frameworks for managing low-carbon energy transitions: Overcoming barriers to implementation in the oil and gas industry.
- [4] Adewoyin, M. A. (2022). Advances in risk-based inspection technologies: Mitigating asset integrity challenges in aging oil and gas infrastructure.
- [5] Adewoyin, M. A., Adediwin, O., & Audu, J. A. (2025). Artificial intelligence and sustainable energy development: A review of applications, challenges, and future directions. International Journal of Multidisciplinary Research and Growth Evaluation, 6(2), 196–203. All Multi Disciplinary Journal.
- [6] Adewoyin, A., Onyeke, F. O., Digitemie, W. N., & Dienagha, I. N. (2025). Holistic Offshore Engineering Strategies: Resolving Stakeholder Conflicts and Accelerating Project Timelines for Complex Energy Projects.
- [7] Adewuyi, A. Y., Anyibama, B., Adebayo, K. B., Kalinzi, J. M., Adeniyi, S. A., & Wada, I. (2024). Precision agriculture: Leveraging data science for sustainable farming. International Journal of Scientific Research Archive, 12(2), 1122-1129.
- [8] Adigun, O. A., Falola, B. O., Esebre, S. D., Wada, I., & Tunde, A. (2024). Enhancing carbon markets with fintech innovations: The role of artificial intelligence and blockchain. World Journal of Advanced Research and Reviews, 23(2).
- [9] Adikwu, E., Ozobu, C. O., Odujobi, O., Onyeke, F. O., & Nwulu, E. O. (2025). A Comprehensive Review of Health Risk Assessments (HRAs) and Their Impact on Occupational Health Programs in Large-Scale Manufacturing Plants.
- Adikwu, F. E., Ozobu, C. O., Odujobi, O., Onyekwe, F. O., & Nwulu, E. O. (2023). Advances in EHS Compliance: A Conceptual Model for Standardizing Health, Safety, and Hygiene Programs Across Multinational Corporations.
- Afolabi, A. I., Chukwurah, N., & Abieba, O. A. (2025). Agile Software Engineering Framework For Real-Time Personalization In Financial Applications.
- Afolabi, A. I., Chukwurah, N., & Abieba, O. A. (2025). Harnessing Machine Learning Techniques for Driving Sustainable Economic Growth and Market Efficiency.
- Afolabi, A. I., Chukwurah, N., & Abieba, O. A. (2025). Implementing cutting-edge software engineering practices for cross-functional team success.
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