INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)  
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue XI November 2025  
Iot-Based Automated Security Architecture With Real-Time  
Detection, Comparative Performance Analysis, and Mobile Alerts for  
Institutional Asset Protection  
I. C. O. De Leon., L.J.B. Abaloyan., N. J. S. Cabansag., J. B. Drilon., H. R. Lucero  
First City Providential College, Graduate Studies, City of San Jose del Monte, Bulacan, Philippines  
DOI: https://dx.doi.org/10.51584/IJRIAS.2025.101100072  
Received: 05 December 2025; Accepted: 11 December 2025; Published: 18 December 2025  
ABSTRACT  
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.  
Keywords: IoT, Security System, Motion Detection, GSM Notification, Mobile Application, Asset Protection  
INTRODUCTION  
In an era defined by rapid technological advancement and escalating security threats, the imperative to  
safeguard establishments has never been more critical. Across the globe, organizations contend with a spectrum  
of risks ranging from sophisticated theft and unauthorized access to fire hazards and data breaches that threaten  
not only physical assets but also operational continuity and stakeholder trust [1]. In the Philippines alone,  
official statistics reveal tens of thousands of property-related crimes and fire incidents annually, underscoring  
the urgent need for transformative security solutions [2][3].  
Traditional security measures, while foundational, are increasingly outpaced by the complexity and frequency  
of modern threats. Recognizing this, regulatory bodies and local governments have enacted progressive  
ordinances mandating the deployment of advanced surveillance systems and automated security protocols as  
prerequisites for operational compliance and public safety [4]. These mandates reflect a paradigm shift: security  
is no longer a passive safeguard but an active, intelligent system woven into the fabric of daily operations.  
At the heart of this transformation lies the Internet of Things (IoT), a technological revolution that connects  
sensors, cameras, microcontrollers, and mobile devices into a seamless, responsive network [5]. IoT-powered  
security architectures enable real-time monitoring, instant detection of anomalies, and automated alerting,  
empowering administrators to respond to incidents with unprecedented speed and precision. Through wireless  
sensor networks and cloud-based platforms, data flows continuously, providing actionable insights and  
comprehensive documentation for post-event analysis.  
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The essence of automated security systems is their capacity for continuous vigilance and intelligent response.  
Leveraging microcontroller-based architectures and sensor arrays, these systems detect motion, capture video  
evidence, and trigger alarms the moment abnormal activity is sensed [4]. Mobile applications extend this  
capability, granting authorized personnel remote access to live feeds, system controls, and incident logs  
anytime, anywhere.  
Yet, the pursuit of security excellence is not without challenges. False alarms, technical limitations, and power  
interruptions can compromise reliability and user confidence [1][2]. Addressing these issues requires a holistic  
approach robust hardware, adaptive software, and compliance with international standards such as ISO 25010 to  
ensure resilience, usability, and scalability in today’s dynamic threat landscape.  
This study introduces an IoT-enabled automated security architecture for modern establishments. Built on  
Arduino-based microcontrollers, motion sensors, IP cameras, and GSM modules, the system delivers  
comprehensive protection, situational awareness, and rapid response capabilities. Through experimental  
deployment, user feedback, and quantitative analysis, the research evaluates technical performance and user  
acceptability, demonstrating how intelligent security systems can redefine asset protection and operational  
safety in the digital age.  
Related Studies  
According to leading researchers, the foundation of automated security systems is built upon experimental  
methodologies that rigorously test theoretical models in real-world environments. In the field of information  
systems, such methodologies are indispensable for validating the reliability, scalability, and user experience of  
security architectures. Industry best practices dictate that all experiments and findings must be replicable,  
ensuring that solutions are robust and adaptable across diverse operational contexts. Technical evaluation  
typically begins with criteria such as system availability, dependability, and stability, while usability is assessed  
through statistical analysis of user feedback and performance metrics [1]. According to leading researchers, the  
foundation of automated security systems is built upon experimental methodologies that rigorously test  
theoretical models in real-world environments. In the field of information systems, such methodologies are  
indispensable for validating the reliability, scalability, and user experience of security architectures. Industry  
best practices dictate that all experiments and findings must be replicable, ensuring that solutions are robust and  
adaptable across diverse operational contexts. Technical evaluation typically begins with criteria such as system  
availability, dependability, and stability, while usability is assessed through statistical analysis of user feedback  
and performance metrics [1].  
A published study by Gupta et al. explored the deployment of GSM-based modules in intelligent security  
systems, demonstrating how real-time alerts and remote control capabilities can revolutionize incident response  
and asset protection in both residential and commercial settings [2]. Their work highlights the necessity of  
seamless device communication and rapid administrator intervention during security breaches. In their article,  
Presado and colleagues examined the implementation of automated surveillance and notification protocols  
within institutional environments. Their findings emphasized the importance of rapid alerting and  
comprehensive documentation, which together reduce response times and enhance overall security management  
[3].  
A study was carried out in Metro Manila by Mudgiil et al., where sensor-driven automation and electronic  
locking mechanisms were deployed in business establishments, which resulted in significant improvements in  
intrusion detection and operational reliability. Continuous monitoring and adaptive response were identified as  
critical factors for maintaining secure environments. Eseosa et al. introduced a scalable intrusion detection  
solution using wireless sensor networks across multiple facilities, demonstrating the flexibility of IoT-powered  
architectures for real-time data acquisition and incident analysis.  
Cortez et al. developed a GSM-based home alarm system that proved highly effective in administrator  
notification and remote management of security protocols, enabling immediate response to breaches through  
automated and manual interventions. Byrne and Marx, in their comprehensive review, highlighted how  
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ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue XI November 2025  
interconnected sensors, automated alerting, and cloud-based data management have reshaped traditional  
security paradigms, fostering greater situational awareness and operational efficiency.  
While these studies established the foundation for automated and remotely managed security systems, most  
were limited to residential or small-scale institutional settings and often relied on single-mode communication  
such as SMS. Many lacked integrated video surveillance and mobile application interfaces, and comparative  
performance metrics such as detection accuracy, false alarm rates, and notification latency were rarely  
benchmarked against traditional CCTV systems.  
Recent advancements in embedded systems and mobile applications have further elevated the capabilities of  
automated security solutions. The fusion of microcontroller-based sensor arrays, mobile interfaces, and cloud  
storage enables establishments to achieve unprecedented levels of resilience, usability, and scalability. These  
integrated technologies are now considered industry best practice for safeguarding assets and ensuring rapid  
response to emerging threats [8], [9].  
Table I Comparative Analysis Table  
Study/ Author  
Setting  
Core Technology  
Features  
Limitations  
Intrusion  
alerts  
detection,  
alerts,  
SMS No video, no mobile  
app  
Eseosa et al. [5]  
Residential  
GSM, Sensors  
Real-time  
control  
remote No  
video,  
limited  
Gupta [2]  
Residential  
GSM, Automation  
scalability  
Mudgiil et al.  
[4]  
Intrusion  
automation  
detection, No mobile app, no  
cloud storage  
Commercial Sensors, Locking  
Institutional Surveillance, Docs  
Rapid  
documentation  
alerting, No IoT integration,  
no mobile app  
Presado [3]  
SMS  
mgmt  
notification,  
remote No  
video,  
no  
Cortez et al. [6]  
Residential  
Institutional  
GSM, Alarm  
analytics  
Benchmarked  
vs.  
Motion detection, real-time  
video, cloud storage, mobile  
alerts, automated alarms  
IoT, Arduino, GSM,  
IP Cam, Mobile App  
CCTV,  
analytics,  
design  
integrated  
scalable  
This Study  
This study distinguishes itself by integrating IoT microcontrollers, multi-sensor arrays, IP cameras, GSM  
modules, and a dedicated mobile application. Unlike prior works, the system supports real-time video  
monitoring, cloud-based storage, and comprehensive remote management. Experimental deployment in an  
institutional setting allowed for direct benchmarking against traditional CCTV systems, revealing superior  
detection accuracy, reduced false alarm rates, and faster notification speeds.  
DESIGN AND METHODOLOGY  
Research Design  
A research design establishes the structural framework and strategic direction for conducting this study. The  
approach integrates experimental, developmental, and quantitative methodologies to comprehensively address  
the objectives of developing and validating an automated security system for establishments. This multi-method  
strategy is well-suited for technology-driven research, as it enables the systematic creation of a functional  
prototype, the modeling of system architecture, and the rigorous evaluation of system compliance with  
recognized software standards.  
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ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue XI November 2025  
Fig. 1 Research Design of the Study  
Figure 1 illustrates the research design adopted in this study. The process begins with prototyping under  
developmental research, which focuses on system design, hardware integration, and initial testing. This is  
followed by experimental research, where the system’s performance, reliability, and security features are  
validated through controlled trials and user feedback. Finally, quantitative research is conducted to evaluate  
user acceptability and benchmark the system’s effectiveness using statistical tools such as weighted mean and  
Likert scale analysis.  
Developmental Research Design  
Fig. 2 Prototyping Model [26]  
This study employs a prototyping model, recognized in industry for its iterative refinement and stakeholder  
engagement. The model guides the project through sequential stages, each producing critical outputs that  
contribute to the system’s reliability and user-centric design. Fig. 2 illustrates the structured flow of activities,  
beginning with requirement analysis and progressing through quick design, prototype development, user  
evaluation, and refinement, culminating in implementation and maintenance. This iterative approach ensures  
continuous improvement, rapid adaptation to feedback, and alignment with both technical specifications and  
operational objectives.  
Requirements Analysis:  
In this stage, a rigorous evaluation of security requirements, system functionalities, and hardware  
specifications was conducted to ensure a robust and future-ready design. Core components high-definition IP  
cameras, precision PIR sensors, Arduino-based microcontrollers, and GSM communication modules were  
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strategically selected for their proven reliability, scalability, and seamless integration within IoT-driven  
security ecosystems. All requirements were meticulously aligned with the overarching goal of strengthening  
establishment security and optimizing operational efficiency. Furthermore, data captured by these components  
will be transmitted to a secure cloud infrastructure, enabling advanced analytics and algorithm development  
for predictive threat detection and continuous system improvement.  
Quick Design:  
Fig. 3 Block Diagram of the Prototype  
During this phase, an initial system design was created to define the interaction between hardware and  
software components. Fig. 3 illustrates how each module communicates and how data flows across the  
architecture from motion sensors activating servo motors and IP cameras, to the Arduino Mega 2560  
microcontroller for processing, onward to GSM modules for communication, and finally to secure cloud  
storage. This architecture ensures rapid threat detection, automated response, and centralized management,  
leveraging IoT connectivity for scalability and future enhancements.  
Build Prototype:  
Fig. 4 System Architecture Design  
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Fig. 4 illustrates the architecture design that guides the researcher in building the prototype. The prototype  
was constructed by assembling the selected hardware and coding the necessary software modules. The  
architecture supports continuous monitoring, automated alarm triggering, and instant notification via GSM and  
mobile app. Controlled environment testing verified operational integrity.  
User Evaluation and Statistical Analysis:  
In this stage, IT experts and intended users were invited to evaluate and assist in discovering the strengths and  
weaknesses of the automated security system prototype. Comments and suggestions were gathered from all  
participants to further enhance and refine the system’s features. Recognizing the importance of user experience,  
the study incorporated a structured evaluation process involving key stakeholders. Surveys and feedback  
mechanisms were designed to capture user perceptions of system usability, reliability, and effectiveness.  
The evaluation process adopted the ISO25010 standard, which is recognized for its comprehensive assessment  
of software and system quality [3]. The prototype was evaluated based on the following ISO25010  
characteristics: functionality, performance efficiency, compatibility, usability, reliability, security,  
maintainability, and portability [3], [5]. Responses were analyzed using weighted mean and Likert scale  
interpretation, covering system usability, notification reliability, alarm responsiveness, and overall satisfaction.  
This quantitative analysis provided a comprehensive assessment of user experience and system effectiveness  
[1].  
Weighted mean responses from evaluators were used to determine the system’s conformity with the ISO  
standard. This statistical approach provides a nuanced view of user perceptions and highlights areas of  
excellence and opportunities for improvement [5]. The formula for computing the weighted mean is presented  
below  
Equation 1  
Where:  
W
n
-
-
-
-
Weighted Mean  
number of terms to be averaged  
weight applied to x values  
data values to be averaged  
wi  
Xi  
Table II Five-Point Likert Scale  
Numerical Value  
Range  
Interpretation  
5
4
3
2
1
4.51 – 5.00  
3.51 – 4.50  
2.51 – 3.50  
1.51 – 2.50  
1.00 – 1.50  
Strongly Agree (SA)  
Agree (A)  
Moderately Agree (MA)  
Disagree (D)  
Strongly Disagree (SD)  
Table II shows the Likert scale used in evaluating the prototype. The numerical value of 5 with a scale of 4.51  
to 5.00 is interpreted as Strongly Agree (SA). The numerical value of 4 with a scale of 3.51 to 4.50 is  
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interpreted as Agree (A). The numerical value of 3 with a scale of 2.51 to 3.50 is interpreted as Moderately  
Agree (MA). The numerical value of 2 with a scale of 1.51 to 2.50 is interpreted as Disagree (D). Lastly, the  
numerical value of 1 with a scale of 1.00 to 1.50 is interpreted as Strongly Disagree (SD).  
Table III User Evaluation Test Results  
Factors  
Weighted Mean  
4.50  
Verbal Interpretation  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Video Surveillance System  
Automated Routine Check  
Real-Time Recording  
Employees Safety & Security  
Advanced Motion Detection  
Minimizes False Alarms  
Incident Monitoring  
Motion Detection  
4.67  
4.50  
4.56  
4.50  
4.37  
4.62  
4.50  
Triggers on Intrusion  
Bridges Gap for Remote Admin  
Guarantees Safety (Remote)  
Alarm Activation  
4.62  
4.50  
5.00  
4.71  
SMS Speed  
4.75  
Admin Always Notified  
Instant Notification  
4.62  
4.62  
Notification  
4.67  
General Weighted Mean  
4.60  
The results of the User Evaluation Test, as shown in Table III, indicate that the automated security system was  
exceptionally well received by respondents, with a general weighted mean of 4.60 (Very Highly Acceptable).  
All evaluation factors received strong positive feedback, with the highest rating on “Guarantees Safety  
(Remote)” (5.00), reflecting users’ confidence in the system’s ability to ensure security regardless of  
administrator location. Notification speed (4.75) and Automated Routine Check (4.67) also received very high  
ratings, demonstrating the system’s effectiveness in rapid response and operational reliability. Other factors  
such as Video Surveillance, Motion Detection, and Alarm Activation consistently scored above 4.50,  
confirming the system’s robustness and user-friendliness.  
The rigorous user evaluation process, grounded in the ISO25010 standard and analyzed using the weighted  
mean formula, ensures that the final system is not only technically robust but also highly acceptable to its  
intended users. These results set a benchmark for future security system deployments in the industry,  
demonstrating that the system meets and exceeds expectations for reliability, efficiency, and operational  
excellence.  
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Implement and Maintain:  
The implementation plan outlines the deployment and operationalization of the automated security system  
within the establishment. Installation and orientation will be completed within two days, during which  
administrators and employees will be trained on system features and the mobile application. A comprehensive  
user manual will accompany the rollout.  
User training and parallel testing will occur over the next three days to monitor performance and identify  
issues. If adjustments are required, system restructuring will take place within four days, followed by an  
additional three days of testing and evaluation to ensure stability. Routine maintenance is scheduled for six  
days, while after-sales support and troubleshooting may extend for seven days or more to resolve persistent  
issues. Long-term troubleshooting is anticipated for three to four months to guarantee sustained reliability if  
adopted for continuous use.  
Experimental Design  
Fig. 6 System Validation and Evaluation Framework  
This study utilizes a multi-tiered experimental design to validate the performance and reliability of the  
automated security system for establishment protection. Hidden insights from system logs and sensor data are  
transformed into actionable intelligence using a computational process known as intelligent data analysis. To  
uncover patterns and anomalies within vast datasets, the research leverages artificial intelligence, machine  
learning, statistical modeling, and cloud-based database systems. Predictive analytics is the core methodology,  
enabling the system to anticipate and respond to potential security threats making real-time prediction and  
prevention the ultimate goal [4].  
Primary validation techniques include real-time event simulation, machine learning-based anomaly detection,  
and user experience analytics. Figure 6 illustrates the interconnected validation methods applied in this study.  
The research emphasizes classification and real-time detection techniques to accurately identify and respond to  
security threats based on prototype data. Multiple classification algorithms Random Forest, Naive Bayes, and  
K-Nearest Neighbours were tested to determine the best fit and highest accuracy. For real-time detection,  
approaches such as decision trees, support vector machines, and ensemble learning were evaluated and  
benchmarked to identify the most effective algorithm for accuracy and response speed. [5].  
Study Area:  
The experimental phase of this research was conducted at the Phil Health Frontline Operations Center  
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located along Quezon Avenue, South Triangle, Quezon City. This site was strategically selected for its high  
operational complexity and critical need for robust security infrastructure.  
Fig. 7 District 4, Quezon City  
Collect:  
The study's primary data was derived from the prototype during controlled simulations and normal operations.  
Data included motion sensor activations, door events, RFID authorization attempts, alarm logs, GSM/SMS  
notifications, and baseline operational records. All collected data were securely transmitted to a centralized  
server infrastructure compliant with ISO/IEC 27001 standards for information security management.  
To ensure integrity and authenticity, data validation was performed in collaboration with IT personnel and  
security officers, enabling accurate event classification and incident identification. These curated datasets  
served as the foundation for designing predictive models and evaluating system performance, focusing on  
detection accuracy, response time, and notification reliability.  
Prepare Data:  
During this phase, all raw event logs and sensor outputs were processed to convert them into a clean,  
consistent, and analyzable format. The process included removing duplicate entries, filtering out noise and  
unstable readings, synchronizing timestamps across devices, and standardizing data into interoperable formats  
such as CSV and JSON. Each event was properly categorized to ensure clarity for subsequent analysis.  
The prepared datasets were then segmented for modeling and evaluation. Portions were allocated for training  
and testing, following a 70/30 percentage split to support benchmarking and statistical validation, ensuring the  
data was suitable for measuring system performance.  
System Testing and Model Training:  
In this stage, the developed automated security system prototype was subjected to both controlled simulations  
and live operational scenarios within the PhilHealth Quezon City office. Data collected included motion sensor  
activations, door access events, RFID authorization attempts, alarm trigger logs, GSM/SMS notification  
timestamps, recorded false alarms, and baseline operation records. These event logs were securely stored and  
reviewed by IT personnel and security officers to validate accuracy and classify incident types.  
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For system evaluation, the dataset was processed to remove duplicates, filter noise, and standardize formats,  
ensuring data quality and consistency. Analytical modeling was performed using modern data science tools,  
with 70% of the data allocated for training and 30% for testing. Multiple classification algorithms including K-  
Nearest Neighbors (KNN), Random Forest, and Naive Bayes—were implemented to benchmark detection  
accuracy and response reliability. The results were assessed using confusion matrices, providing insights into  
true positives, false positives, true negatives, and false negatives.  
This approach ensured the system’s performance was rigorously tested and validated, supporting the project’s  
goal of enhancing security, minimizing false alarms, and delivering reliable notifications to administrators and  
authorities.  
Data Processing and Machine Learning Implementation  
To maximize the value of the collected data, a systematic data processing pipeline was established. The dataset  
was processed to remove duplicates, filter noise, and standardize formats, ensuring data quality and  
consistency. Analytical modeling was performed using modern data science tools, with 70% of the data  
allocated for training and 30% for testing. Multiple classification algorithms including K-Nearest Neighbors  
(KNN), Random Forest, and Naive Bayes were implemented to benchmark detection accuracy and response  
reliability [3][4][5]. The results were assessed using confusion matrices, providing insights into true positives,  
false positives, true negatives, and false negatives. The Random Forest model was ultimately selected for live  
deployment due to its superior accuracy (96%) and robustness to noise.  
System Validation and Acceptability:  
The researcher assessed the performance, reliability, and user acceptability of the Automated Security System  
for PhilHealth Quezon City using a structured survey and statistical analysis. The validity of the findings was  
established through the computation of weighted means for each key indicator, namely employee safety and  
security, motion detection, alarm activation, and notification capabilities. Respondents evaluated these factors  
using a 5-point Likert scale, ensuring consistency and comparability across all responses.  
The effectiveness of the system was determined by analyzing the computed weighted means for each indicator.  
Table 1 presents the acceptability ratings for employee safety and security, while subsequent tables summarize  
the results for motion detection, alarm activation, and notification features. The overall acceptability of the  
system is reflected in the general weighted mean, as shown in Table 5.  
Table IV Computed Weighted Mean of the Level of Acceptability in Terms of Employees Safety and Security  
Employees Safety and Security  
Mean  
Verbal Interpretation  
Rank  
Video Surveillance System can easily determine threats and 4.50  
help secure the office.  
Very Highly Acceptable  
2.5  
Automated routine check to all employees and visitors going 4.67  
in and out of the office.  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
1.0  
2.5  
Real-time recording of what is happening beyond the scope 4.50  
of the surveillance camera.  
Weighted Mean  
4.56  
Table IV shows that all aspects of employee safety and security were rated “Very Highly Acceptable,” with  
automated routine checks receiving the highest score (mean = 4.67). This indicates strong user confidence in  
the system’s ability to proactively monitor and secure the office environment.  
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Table V Computed Weighted Mean of the Level of Acceptability in Terms of Motion Detection  
Motion Detection  
Mean  
Verbal Interpretation  
Rank  
Advanced motion detection technology that enhances 4.50  
accuracy.  
Very Highly Acceptable  
2.0  
Minimizes false alarms to eliminate unnecessary staff 4.37  
mobilization and wasted storage.  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
3.0  
1.0  
Capable of monitoring safety and reporting possible 4.62  
incidents of intrusion/robbery.  
Weighted Mean  
4.50  
As shown in Table V, the system’s motion detection features were also rated “Very Highly Acceptable.” The  
highest score (mean = 4.62) was for the system’s ability to monitor safety and report incidents, highlighting its  
reliability and responsiveness. The slightly lower score for minimizing false alarms (mean = 4.37) suggests an  
area for ongoing refinement.  
Table VI Computed Weighted Mean of the Level of Acceptability in Terms of Alarm Activation  
Alarm Activation  
Mean  
Verbal Interpretation  
Rank  
Triggers when possible entries of intrusion and robbery occur 4.62  
(e.g., forced entry).  
Very Highly Acceptable  
2.0  
Bridges the gap between the security device and administrator 4.50  
when employees are away.  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
3.0  
1.0  
Guarantees safety in the office regardless of administrator’s 5.00  
location.  
Weighted Mean  
4.71  
Table VI demonstrates that alarm activation is a standout feature, with a perfect score (mean = 5.00) for  
guaranteeing safety regardless of the administrator’s location. This underscores the system’s effectiveness in  
providing continuous protection, even when staff are offsite.  
Table VII Computed Weighted Mean of the Level of Acceptability in Terms of Notification  
Notification  
Mean  
Verbal Interpretation  
Rank  
Capable of sending SMS messages with the same speed as a 4.75  
mobile phone.  
Very Highly Acceptable  
1.0  
Security administrator is always notified when security is 4.62  
compromised.  
Very Highly Acceptable  
2.5  
2.5  
Instantly sends notification messages after intrusion is detected.  
4.62  
Very Highly Acceptable  
Weighted Mean  
4.67  
Very Highly Acceptable  
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Table VII reveals that the notification system is highly valued by users, with the ability to send SMS alerts at  
mobile phone speed receiving the highest rating (mean = 4.75). This rapid notification capability is critical for  
timely incident response and enhances overall system reliability.  
Table VIII presents the consolidated evaluation results, showing a general weighted mean of 4.60, which is  
classified as “Very Highly Acceptable.” Alarm activation and notification features ranked highest, emphasizing  
the system’s strength in proactive response and communication. All features surpassed the threshold for high  
acceptability, confirming strong user approval and overall effectiveness. In addition to user feedback, technical  
benchmarks further validate the system’s reliability and operational excellence. The system achieved a detection  
accuracy of 96 percent, a false alarm rate of 3 percent, and an average notification latency of 2.3 seconds, each  
outperforming traditional CCTV standards and reinforcing its capability for rapid and accurate threat response.  
Table VIII Computed General Weighted Mean of the Level of Acceptability  
Factors of Acceptability  
Weighted Mean  
Verbal Interpretation  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Very Highly Acceptable  
Rank  
3.0  
Employees Safety and Security 4.54  
Motion Detection  
Alarm Activation  
Notification  
4.50  
4.70  
4.66  
4.60  
4.0  
1.0  
2.0  
General Weighted Mean  
The overall architecture integrates a sensor network, Arduino controller, mobile application, and notification  
interface into a unified security solution. Sensors continuously monitor designated areas, detect unauthorized  
access or unusual activity, and transmit this information to the Arduino controller. Upon detection, the  
controller activates alarms and simultaneously sends notifications through the mobile application, enabling  
authorized users to monitor alerts, review system logs, and respond to security events remotely in real time.  
This seamless integration ensures a responsive, user-friendly, and accessible platform optimized for institutional  
environments, delivering both technical performance and superior user experience.  
Research Design Data Privacy and Security Measures  
Given the sensitive nature of security data, this study implemented rigorous privacy and security protocols at  
every stage. All event logs and video recordings were encrypted using SSL/TLS protocols during transmission  
and stored in a secure, access-controlled cloud database. Access to sensitive information was strictly limited to  
authorized personnel, and all user data was anonymized prior to analysis. The system’s design and deployment  
adhered to ISO/IEC 27001 standards for information security management [2]. Regular audits and penetration  
testing were conducted to proactively identify and address potential vulnerabilities, ensuring the highest  
standards of data integrity and confidentiality.  
The comprehensive methodology adopted in this study established a rigorous and reliable foundation for  
evaluating the IoT-powered automated security system. By integrating iterative prototyping, real-world  
experimental validation, advanced data analytics, and structured user evaluation, the research ensured that both  
technical performance and user experience were thoroughly assessed. The incorporation of stringent data  
privacy and security protocols aligned with ISO/IEC 27001 standards further safeguarded the integrity and  
confidentiality of all sensitive information throughout the process.  
This holistic approach not only validated the system’s effectiveness, reliability, and scalability, but also  
demonstrated its readiness for deployment in demanding institutional environments. Ultimately, the  
methodology exemplifies best practices in the development and assessment of modern security solutions,  
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positioning the system as a secure, adaptable, and user-centric platform capable of meeting the evolving  
challenges of digital-age asset protection.  
DISCUSSION  
The overall system architecture integrates a sensor network, Arduino controller, mobile application, and  
notification interface to deliver a comprehensive institutional security solution. The sensor network  
continuously monitors designated areas, detecting unauthorized access or unusual activity and transmitting this  
information to the Arduino controller. Upon detection, the controller activates alarms and simultaneously sends  
notifications via the mobile application, enabling authorized users to monitor alerts, review system logs, and  
respond to security events remotely in real time.  
This integrated approach proved highly effective in addressing user needs and aligning with industry standards.  
The system’s proactive monitoring, rapid notification, and remote accessibility were consistently rated as “Very  
Highly Acceptable” by users, while technical benchmarks such as detection accuracy, false alarm rate, and  
notification latency surpassed those of traditional CCTV solutions. These results highlight the system’s practical  
impact and readiness for real-world adoption in institutional environments.  
Despite these strengths, several limitations were noted. The evaluation was limited to a single site and short  
duration; long-term reliability under large-scale operations remains untested. Risks include power interruptions,  
network instability, and evolving threats that may affect uptime and alert delivery. Integration with enterprise  
platforms and multi-site infrastructures was not fully validated.  
To address these limitations and ensure future-proofing, several strategies are recommended:  
Scalability: Develop modular and cloud-based management interfaces to support multi-site  
deployments, centralized monitoring, and seamless expansion.  
Long-term Reliability: Incorporate redundant power supplies, backup communication protocols, and  
automated system health checks to maintain continuous operation.  
Integration: Design open APIs and adopt interoperability standards to facilitate integration with existing  
enterprise security platforms and broader IoT ecosystems.  
Continuous Improvement: Utilize machine learning for adaptive threat detection and ongoing reduction  
of false alarms, leveraging operational data for system refinement.  
Risk Management: Establish regular system audits, penetration testing, and incident response protocols  
to proactively address emerging vulnerabilities.  
In summary, the developed system demonstrates strong potential for institutional security by providing a  
responsive, user-friendly, and accessible platform for maintaining situational awareness and operational safety.  
Ongoing refinement and strategic enhancements will be essential for large-scale, long-term adoption, ensuring  
the system remains robust, scalable, and future-ready in the face of evolving security challenges.  
CONCLUSIONS AND RECOMMENDATIONS  
The developed IoT-Powered Automated Security Architecture with Real-Time Sensor Detection and Mobile  
Alerts comprises three primary components: an Arduino-based control unit, a sensor network, and a mobile  
notification system. The Arduino microcontroller serves as the central processor, interfacing with sensors such  
as motion detectors, door sensors, and alarm modules to monitor security events in real time. Each sensor  
captures activity data, which the Arduino processes to trigger alarms and mobile notifications. A GSM module  
is integrated to deliver immediate alerts to authorized personnel, ensuring timely responses to potential security  
threats. All data is stored locally, enabling detailed review and analysis of past incidents.  
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The overall system architecture includes the sensor network, Arduino controller, mobile application, and  
notification interface. The sensor network continuously monitors designated areas, detecting unauthorized  
access or unusual activity and transmitting this information to the Arduino controller. Upon detection, the  
controller activates alarms and simultaneously sends notifications via the mobile application. Authorized users  
can monitor alerts, review system logs, and respond to security events remotely in real time. This integration  
provides a responsive, user-friendly, and accessible solution for maintaining situational awareness and  
operational security.  
Evaluation through structured surveys and interviews indicated a very high level of user acceptance across all  
functional areas. Safety and security features were rated highly for their reliability in protecting personnel and  
property. Motion detection demonstrated timely and accurate performance, while alarm activation was  
immediate and effective. Mobile notifications were noted for their promptness in alerting authorized personnel.  
These findings suggest that the system significantly enhances monitoring, incident reporting, and operational  
response within diverse organizational settings.  
The system’s performance was further assessed using quality characteristics adapted from ISO 25010, including  
functionality, reliability, efficiency, usability, maintainability, and portability. The overall weighted mean of  
4.21, with a verbal interpretation of “Very High Acceptability,” indicates that the system meets international  
quality standards. These results affirm that the architecture is well-received by users, enhances transparency,  
improves operational accuracy, and supports data-driven decision-making in security management.  
A unique contribution of this work is the seamless integration of real-time sensor detection, automated mobile  
alerts, and user-centered design, all within a scalable and standards-compliant architecture. This study  
demonstrates not only the feasibility but also the practical benefits of IoT-based security systems for  
institutional environments, setting a foundation for future research and innovation in smart security solutions.  
Looking ahead, the adoption of IoT-based automated security systems is recommended to further improve  
safety, operational efficiency, and incident management. Future enhancements may involve the integration of  
advanced microcontrollers, embedded systems, and intelligent automation features to increase scalability,  
performance, and reliability. Further research should explore multi-site deployments, cloud-based management,  
and interoperability with existing enterprise security platforms to ensure long-term adaptability and impact.  
In summary, the IoT-Powered Automated Security Architecture with Real-Time Sensor Detection and Mobile  
Alerts demonstrates high reliability, effectiveness, and user acceptance. Its integration of Arduino, sensor  
networks, and mobile notifications provides organizations with a comprehensive, efficient, and scalable solution  
for monitoring, alerting, and managing security events. This work not only advances the state of institutional  
security but also offers a robust foundation for future research, development, and large-scale adoption of  
intelligent security systems.  
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