Iot-Based Automated Security Architecture With Real-Time Detection, Comparative Performance Analysis, and Mobile Alerts for Institutional Asset Protection

Security systems have rapidly evolved with the rise of the Internet of Things (IoT), sensor technologies, and mobile computing. This study presents the design, development, and evaluation of an IoT-powered automated security architecture intended for modern establishments. The system integrates motion detection, real-time video monitoring, mobile alerts, and automated alarm responses to enhance security readiness and reduce risks associated with theft, intrusion, and unauthorized access. Using an IoT microcontroller as the core processor, the system collects sensor data, triggers automated alarms, and sends mobile notifications when abnormal activity is detected. A mobile application enables remote activation, monitoring, and video retrieval through cloud storage. Results show that the system achieved a “Very Highly Acceptable” overall rating (mean = 4.60/5.00) in safety, motion accuracy, alarm responsiveness, and notification reliability, outperforming traditional CCTV in detection accuracy and notification speed. While the system proved effective, limitations such as reliance on stable power and network connectivity were identified. Future work will focus on enhancing scalability, integrating advanced analytics, and strengthening data privacy measures. The findings demonstrate that IoT-driven integrated security systems can effectively strengthen establishment protection, increase situational awareness, and support rapid response during potential security breaches.

IoT, Security System, Motion Detection, GSM Notification, Mobile Application, Asset Protection

1. K. R. Pontiveros, et al., “Development of Multi-Home Alarm System Based on GSM Technology,” International Journal of Electronics and Electrical Engineering, vol. 4, no. 4, Aug. 2016. [Google Scholar] [Crossref]

2. O. G. Eseosa, et al., “GSM Based Intelligent Home Security System for Intrusion Detection,” International Journal of Engineering and Technology, vol. 4, no. 10, Oct. 2014. [Google Scholar] [Crossref]

3. Gupta, “Intelligent Home Security Using GSM Communication Module,” International Journal of Innovation and Scientific Research, vol. 13, no. 1, pp. 239–242, Jan. 2015. [Google Scholar] [Crossref]

4. Mudgiil, et al., “Design and Development of Sensor Based Home Automation and Security System Using GSM Module and Locking System,” International Journal of Advanced Engineering Research and Science (IJAERS), vol. 1, issue 4, Sept. 2014. [Google Scholar] [Crossref]

5. J. E. Presado, “The Level of Security Management in the University of Eastern Philippines,” International Conference on Research in Social Sciences, Humanities and Education (SSHE-2016), May 20–21, 2016, Cebu, Philippines. [Google Scholar] [Crossref]

6. D. Cortez, et al., “Development of Home Alarm System Based on GSM Technology,” Computer Department, Centro Escolar University, Manila, Philippines, Aug. 2016. [Google Scholar] [Crossref]

7. B. Alb Is, Jr., et al., “A Study On The Effectivity Of The Philippine Prison System,” PLJ, vol. 52, no. 1-03, June 2016. [Google Scholar] [Crossref]

8. Quezon City Ordinance No SP-2139, S-2012, Jan. 13, 2014. [Online]. Available: (Accessed Nov. 22, 2017). [Google Scholar] [Crossref]

9. Parker, “The Advancement of New Technology. Positive or Negative?” Apr. 12, 2015. [Online]. Available: (Accessed Aug. 31, 2017). [Google Scholar] [Crossref]

10. R. Fiddis, “Public Safety: The Impact of Technology,” Aug. 8, 2016. [Online]. Available: (Accessed Aug. 31, 2017). [Google Scholar] [Crossref]

11. J. Byrne and G. Marx, “Technological Innovations in Crime Prevention and Policing,” Nov. 2013. [Online]. Available: (Accessed Aug. 31, 2017). [Google Scholar] [Crossref]

12. ISO/IEC 25010:2011, “Systems and software engineering Systems and software Quality Requirements and Evaluation (SQuaRE) System and software quality models.” [Google Scholar] [Crossref]

13. ISO/IEC 27001:2013, “Information technology Security techniques Information security management systems Requirements.” [Google Scholar] [Crossref]

14. L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [Google Scholar] [Crossref]

15. T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21–27, 1967. [Google Scholar] [Crossref]

16. H. Zhang, “The Optimality of Naive Bayes,” Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, 2004. [Google Scholar] [Crossref]

17. I. C. O. De Leon, R. A. Elandag, D. W. L. Enojo, J. G. Ferrer, and A. I. Lianza, “Developing Automated Security System for PhilHealth Quezon City,” Capstone Project, Institute of Computer Studies, Colegio de Montalban, Rodriguez, Rizal, 2018. [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet