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American Journal of  Smart 
Technology and Solutions (AJSTS)

Parking Occupant Management System Using QR Code Solutions With AES Algorithm 
Albert I. Luzuriaga1, Gawain Destiny A. De Groot1, Jerold E. Cortez1, Nathaniel U. Babanto1, Meridel C. Ejusa1*

Volume 4 Issue 2, Year 2025
ISSN: 2837-0295 (Online)

DOI: https://doi.org/10.54536/ajsts.v4i2.4717
https://journals.e-palli.com/home/index.php/ajsts

Article Information ABSTRACT

Received: March 12, 2025

Accepted: April 17, 2025

Published: August 28, 2025

This study presents a Parking Occupant Management System utilizing QR Code Solutions 
integrated with Advanced Encryption Standard (AES) technology to address inefficiencies 
in manual vehicle cataloging within educational institutions. Automating this process 
improves accuracy, efficiency, and security while ensuring adaptability across diverse 
school parking environments. QR codes facilitate seamless logging of  vehicle entries and 
exits, embedding contact information that enables security guards to communicate directly 
with vehicle owners in case of  issues. The incorporation of  AES encryption provides 
robust protection for sensitive data, safeguarding it against unauthorized access. Designed 
as a mobile application for Android devices, the system empowers security guards to 
scan QR codes in real time, effectively recording vehicle activity. Focusing on educational 
institutions in General Santos City, this study demonstrates significant enhancements in 
parking management through automation and improved data protection. The system 
establishes a new standard for secure data management in parking operations and 
holds promise for application in sectors such as commercial and residential complexes. 
By offering a scalable solution that enhances efficiency while ensuring data security, it 
contributes to better resource management. Future developments may explore security 
enhancements and expansion to platforms beyond Android, increasing accessibility and 
catering to a wider range of  parking management needs. Ultimately, this system serves as a 
blueprint for modernizing parking management, paving the way for smarter, more secure 
urban environments.

Keywords
AES Encryption, Data Security, 
Parking Management System, QR 
Code, Vehicle Cataloging

1 College of  Engineering and Technology Education, Holy Trinity College, General Santos City, Philippines
* Corresponding author’s e-mail: htc_ejusam@online.htcgsc.edu.ph

INTRODUCTION
The increasing vehicle populations and urbanization pose 
challenges in managing parking operations on college 
and university campuses (Dokania et al., 2020). This 
growing demand highlights the urgent need for efficient 
parking solutions. Traditional manual cataloging methods 
fall short—they are slow, prone to mistakes, and lack 
strong security measures, resulting in operational hiccups 
and safety risks. To tackle this, this research proposes 
a QR-based vehicle management system paired with 
the Advanced Encryption Standard (AES) encryption 
algorithm to enhance the handling of  vehicle entries and 
exits in university parking areas securely and efficiently.
This QR-based system with AES encryption offers a major 
improvement over outdated methods, delivering benefits 
to both campus parking facilities and vehicle owners. It 
strengthens security by encrypting QR codes with AES, 
safeguarding parking occupants’ data from unauthorized 
access or tampering—only a decryption key can unlock 
intercepted details. The system also optimizes vehicle 
entry and exit through quick QR scanning, cutting down 
wait times, improving traffic flow, and boosting overall 
parking efficiency by reducing human errors. While urban 
parking systems have embraced technological upgrades, 
campus parking has lagged, often sticking to manual 
processes with little use of  QR technology. This gap 
has weakened communication between parking users 
and attendants, increasing risks like theft, vandalism, 

accidents, and legal issues.
The main goal of  this work is to create a secure, efficient, 
and user-friendly parking management system for 
educational institutions using QR code technology and 
AES encryption. By adopting automation and focusing 
on user needs, this research improves parking operations, 
enhances security, and fosters better communication, 
overcoming the drawbacks of  manual methods. 
The author’s key contribution is showing how these 
technologies can modernize campus parking, ensuring 
lasting efficiency, safety, and convenience as campuses 
adapt to rising vehicle numbers and urban growth.

LITERATURE REVIEW
Improving vehicle management systems on university 
campuses has become increasingly important in our rapidly 
changing environment. This literature review examines 
how Quick Response (QR) codes and the Advanced 
Encryption Standard (AES) encryption algorithm can 
enhance the security and efficiency of  campus parking 
management. By leveraging these advanced technologies, 
the review draws on significant contributions from prior 
studies to justify their integration into parking systems, 
highlighting their potential to streamline operations and 
bolster data security.

QR Codes in Vehicle Management
As urban environments evolve, integrating QR codes 



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into vehicle management systems significantly enhances 
user convenience by enabling faster queuing processes 
and minimizing waiting times. Lin, Rivano, and Le Mouël 
(2017) classify smart parking ecosystems, identifying 
essential components and usage trends that underscore 
the effectiveness of  QR-based systems in optimizing 
parking operations. Their findings suggest that QR codes 
improve user experience and operational efficiency by 
streamlining data collection and vehicle tracking. Similarly, 
Barriga et al. (2019) explore the primary components and 
usage patterns of  smart parking systems, emphasizing 
how QR codes, alongside sensors and software, enhance 
functionality and urban mobility. Kadu et al. (2014) 
further address urban parking challenges by introducing 
a QR code-based smart parking system that identifies 
users, provides real-time parking information, reduces 
traffic congestion, and increases user satisfaction. These 
studies collectively highlight QR codes as a key variable in 
improving parking management efficiency.

Encryption and Security
The combination of  QR codes with AES encryption 
addresses crucial security concerns in vehicle management 
systems. Chai et al. (2023) stress the necessity of  
incorporating AES encryption into QR codes to protect 
sensitive information, preventing data leakage and 
unauthorized access while preserving system integrity. 
Ajini Asok and Arun (2016) explore the use of  AES-
128 encryption to secure private data within QR codes, 
mitigating risks like eavesdropping due to their visual 
nature. Their approach ensures safe verification without 
delays, enhancing security in data exchange. Additionally, 
Agarwal and Malik (2022) investigate steganography 
with QR codes and AES encryption, emphasizing the 
preservation of  image quality to conceal data effectively. 
These contributions justify AES as a robust cryptographic 
mechanism for securing QR-based systems, supporting 
its adoption in campus parking contexts.

Performance and Efficiency
The efficiency of  AES encryption is vital for its practical 
implementation in parking systems, particularly on 
resource-constrained devices. Doomun, Doma, and 
Tengur (2008) assess AES in Cipher Block Chaining 
(CBC) mode, finding that optimized implementations 
improve encryption speed by 12% to 30%, though with 
increased memory demands. Almuhammadi and Al-Hejri 
(2017) analyze AES block cipher modes like Electronic 
Codebook (ECB) and CBC, evaluating encryption time 
and throughput to guide mode selection. Vaidehi and Rabi 
(2014) highlight ECB’s vulnerabilities and advocate for 
CBC to enhance security and effectiveness. These studies 
provide a foundation for optimizing AES performance, a 
critical variable for real-time parking applications.

Applications and Case Studies
Insights from related fields reinforce the applicability of  
QR codes and AES encryption in parking management. 

Ferdiansyah, Hadiana, and Rakhmat (2021) demonstrate 
their integration in healthcare administration, where 
encrypted QR codes secure sensitive data, offering a 
model for parking systems. Agun, Rabie, and Satoquia 
(2022) showcase a QR-based automated ticketing system 
for traffic violations, improving data collection and 
transaction efficiency—principles applicable to parking 
operations. Gangurde et al. (2022) further emphasize 
the broader implications of  these technologies, noting 
their benefits in security and user experience across 
contexts. These case studies support the hypothesis 
that QR codes and AES can transform campus parking 
management.

Hardware Implementations
Efficient hardware implementations enhance AES 
applicability in parking systems. Rachh et al. (2012) propose 
two AES architectures using composite field arithmetic, 
optimizing S-boxes and operations like MixColumns 
for encryption and decryption. Their designs improve 
processing efficiency, making them suitable for mobile 
and embedded systems in parking management. This 
hardware focus complements software-based security 
measures, reinforcing AES as a versatile solution.
In conclusion, the literature underscores the critical 
role of  QR codes and AES encryption in modernizing 
parking management systems on university campuses. 
These technologies address security, efficiency, and user 
experience challenges, justifying their adoption and 
suggesting hypotheses for further research, such as their 
impact on reducing parking delays and enhancing data 
protection.

MATERIALS AND METHODS
This section outlines the materials, tools, and 
methodologies employed to develop and evaluate the 
Parking Occupant Management System Using QR Code 
Solutions with AES Algorithm. The system was designed 
to automate vehicle cataloging, enhance data security, and 
improve parking management efficiency in educational 
institutions within General Santos City. The approach 
is detailed below, structured into subsections for clarity, 
providing sufficient information to replicate the study 
while maintaining a concise narrative.

System Development Methodology
In developing the parking occupant management system 
using QR code solutions with AES algorithm, the 
researchers used the Iterative Waterfall Model. The f  low 
of  this model takes you through system level requirements, 
requirement analysis, design, implementation, testing, 
integration deployment, and maintenance in a structured 
way. This is different from the traditional waterfall project 
management model, where each stage is fed feedback 
from all stages behind it, as opposed to the classic 
model comparison which just checks for similarities or 
differences between aspects of  project management in 
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Requirement Analysis
The initial phase involved analyzing the existing manual 
parking management processes in educational institutions 
to identify inefficiencies and define system requirements. 
Data were collected through interviews conducted with 
stakeholders, such as security guards and administrative 
personnel, from institutions like General Santos Doctors’ 
Medical School Foundation Inc. For instance, interviews 
revealed that parking management relied on manual steps: 
occupants completed registration forms, administrators 
processed permits, and guards logged vehicle entries and 
exits manually. These processes were prone to errors and 
delays, necessitating automation.
The collected data included occupant details (full name, 
contact number, address) and vehicle information (license 
plate number, type, color, brand, model), which were critical 

for system functionality and security verification. This 
information was securely stored in a MySQL database, 
forming the basis for QR code generation and access control.

System Design and Architecture
The system was designed as a multi-component 
architecture integrating web and mobile platforms. Key 
components included:

Frontend Technologies
● Vue.js with Vuetify: Used for the web interface, 

providing reactive data binding and pre-designed UI 
elements (e.g., buttons, text fields) for a consistent and 
intuitive user experience.

● Flutter with Material.dart: Employed for the Android 
mobile app, ensuring a clean and intuitive design aligned 
with Google’s Material Design guidelines.

Backend Technologies
● PHP Native via RESTful APIs: Managed backend 

logic, such as form processing and database interactions, 
using HTTP methods (GET, POST) with Axios for 
communication.

● MySQL: Stored structured data, including occupant 
profiles, vehicle details, and parking logs.

● XAMPP: Provided a local development environment 
with Apache, MySQL, PHP, and Perl.

Cryptographic and QR Code Tools
● Crypto-JS: A JavaScript library implementing AES 

encryption in Cipher Block Chaining (CBC) mode with 
PKCS7 padding, used to encrypt occupant and vehicle IDs.

Figure 1: Iterative Waterfall Model of  Software 
Development Life Cycle

Figure 2: System Architecture



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● Encrypt.dart: A Dart library for AES decryption in 
CBC mode with PKCS7 padding, used in the mobile app 
to decode encrypted QR code data.

● QRCode.js: Generated QR codes embedding 
encrypted data.

● Flutter_barcode_scanner: A Flutter plugin for 

scanning QR codes on Android devices
The system architecture supported secure data flow, with 
encrypted occupant and vehicle IDs embedded in QR 
codes, scanned by guards to log entries and exits.
The Entity Relationship Diagram detailed the database 
structure, comprising ten interconnected tables to manage 

Figure 3: Entity Relationship Diagram of  the Database

occupant profiles, vehicle data, and parking logs efficiently.

Implementation Details
Data Collection and Encryption
Occupants registered via a web interface, providing 
personal and vehicle details. These data were encoded into 
a JSON string (e.g., {“vehicleId”: 65, “occupantId”: 126}; 
encrypted using the AES-CBC algorithm implemented in 
Crypto-JS. The encryption process, detailed, involved:

1. Generating a random 16-byte Initialization Vector 
(IV).

2. Encrypting the JSON string with a predefined AES 
key (stored as an environment variable) in CBC mode 
with PKCS7 padding.

3. Outputting a Base64-encoded string combining the 
IV and ciphertext (e.g., MM/sbt5xTvvBUVWv16nslg= 
=:1tr4ZmBEInxw+FoRO7mAgeSa5mOthdrlpI6 
Dy9nkFr5xs81s/9ii5RNsXiSOLu+J; Appendix E, p. 77).
This encrypted data was embedded into QR codes using 
QRCode.js, printed, and affixed to vehicles for scanning.
QR Code Generation and Scanning
The QR code generation process linked encrypted 
Occupant ID and Vehicle ID to each vehicle, ensuring 

secure identification. The mobile app, developed in Dart 
using Flutter, utilized flutter_barcode_scanner to decode 
QR codes. Upon scanning, the app extracted the Base64-
encoded data, decrypted it using encrypt.dart, and 
retrieved the original JSON string via the AES key and 
IV, enabling guards to log vehicle actions.

AES Algorithm Implementation
The AES encryption adhered to the standard outlined 
by the National Institute of  Standards and Technology 
(NIST) in [FIPS 197], implemented in Cipher Block 
Chaining (CBC) mode as described by Doomun et al. 
(2008). Modifications to the standard included the use 
of  a fixed AES key in Base16 format and a randomly 
generated IV for each encryption to bolster security.
In CBC mode, each plaintext block is XORed with the 
previous ciphertext block before encryption, with the 
IV serving as the initial vector for the first block. The 
encryption process for each block i is defined as:
Ci=Ek  (Pi ⊕ C(i-1)), with C0=IV 
The decryption process reverses this operation to recover 
the original plaintext, expressed as:
Pi= DK (Ci )⊕C(i-1), with C0=IV 



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This implementation ensured both data integrity and 
confidentiality. The use of  a random IV per encryption 
guaranteed that identical plaintext blocks produced distinct 
ciphertexts, significantly enhancing the system’s security.

Testing and Evaluation
The system underwent thorough testing to assess 
accuracy, efficiency, and security:

● Unit Testing: Verified individual components (e.g., 
encryption, QR code scanning) using Microsoft Visual 
Studio Code as the primary IDE for debugging.

● Integration Testing: Ensured seamless interaction 
between web, mobile, and backend components.

● Security Testing: Confirmed that encrypted QR 
codes were unreadable by third-party scanners (e.g., 
Google Lens), validating AES effectiveness.

● User Testing: Conducted with five respondents 
from educational institutions on October 18, 2024, using 
a survey. Respondents rated interface, functionality, 
usability, experience, and security on a 1-5 scale, yielding 
a mean score of  5.0 (Very Satisfied) across all categories.
Testing focused on real-time logging accuracy, QR 
code scanning speed, and data protection, with results 
indicating high reliability and user satisfaction.

Deployment
The system was deployed in educational institutions in 
General Santos City, operating within the institution’s 
WLAN for enhanced security. The deployment phase 
involved:

1. Installing the mobile app on institution-provided 
Android devices.

2. Configuring the MySQL database on a local XAMPP 
server to store parking data.

3. Distributing QR codes to registered occupants 
(faculty, staff, students).
Deployment ensured that guards could scan QR codes to 
log vehicle entries and exits, with logs stored securely and 
accessible only within the institution’s network.

Data Analysis
Data collected included parking logs (e.g., Log ID, Vehicle 
ID, timestamp, action type; and user feedback. Logs 
were analyzed to evaluate system performance, such as 
entry/exit frequency and error rates. Survey responses 
were quantified to assess user satisfaction and security 
perceptions. The AES encryption’s effectiveness was 
validated by its inability to be decrypted by unauthorized 
tools, ensuring data confidentiality.

RESULTS AND DISCUSSION
This section presents the outcomes of  the evaluation 
of  the Parking Occupant Management System Using 
QR Code Solutions with AES Algorithm, conducted 
within educational institutions in General Santos City, 
and discusses their significance in the context of  modern 
parking management challenges. The evaluation, based on 
a survey of  five respondents, assessed the system across 

five key dimensions: Interface, Functionality, Usability, 
Experience, and Security. The results indicate exceptional 
user satisfaction, with a consistent mean score of  5.0 (on 
a 1–5 scale, where 5 denotes “Very Satisfied”) across all 
evaluated criteria, highlighting the system’s effectiveness 
in automating vehicle cataloging and enhancing data 
security through AES encryption.

Summary of  Key Findings
The survey results demonstrate that the Parking Occupant 
Management System excels in delivering a user-friendly, 
functional, and secure solution for parking management. 
Respondents universally praised the system’s consistent 
interface design across web and mobile platforms, noting 
its intuitive layout and clear feedback mechanisms, 
such as pop-up messages, which enhance usability. The 
system’s functionality, particularly its ability to accurately 
process QR codes and update parking occupancy in real 
time, was rated highly, indicating seamless integration of  
essential parking management features. Usability was a 
standout feature, with first-time users finding navigation 
effortless and QR code scanning smooth and efficient. 
In terms of  overall experience, respondents reported 
significant improvements in convenience and time 
savings, attributing these benefits to the system’s reliable 
performance and the unobtrusive integration of  AES 
encryption, which operates without compromising speed. 
Most notably, the security dimension received unanimous 
approval, with users expressing strong confidence in the 
AES encryption’s ability to protect sensitive data, such 
as personal and vehicle information, during QR code 
scanning and transmission.
As shown in Table 1, the system achieved a total mean 
score of  5.0 across all sections, reflecting its exceptional 
performance in meeting user expectations.

Table 1 : Summary of  Survey Results
Section Mean Score Verbal Description
Interface 5.0 Very Satisfied
Functionality 5.0 Very Satisfied
Usability 5.0 Very Satisfied
Experience 5.0 Very Satisfied
Security 5.0 Very Satisfied

Significance of  the Results
The uniformly high satisfaction scores underscore the 
system’s success in addressing key challenges in parking 
management, particularly within educational settings. 
The flawless mean score of  5.0 across all dimensions 
suggests that the system not only automates the manual 
cataloging process effectively but also elevates user trust 
through robust security measures. A critical factor in this 
success is the implementation of  AES encryption, which 
ensures that sensitive data—such as occupant and vehicle 
IDs embedded in QR codes—remains protected against 
unauthorized access. This is particularly significant in 



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an era where data breaches and privacy concerns are 
escalating, especially in digital systems managing personal 
information. The respondents’ confidence in the system’s 
security, as evidenced by their approval of  features like 
the prevention of  QR code decoding by third-party 
tools (e.g., Google Lens), highlights AES encryption as a 
cornerstone of  the system’s value proposition.
Beyond security, the system’s ability to improve parking 
operations through QR code technology offers practical 
benefits. The real-time logging of  vehicle entries and 
exits, facilitated by efficient QR code scanning, reduces 
human error and administrative overhead compared to 
traditional manual methods. This efficiency is vital for 
educational institutions, where parking facilities often 
face high demand and limited resources. The seamless 
user experience, from intuitive navigation to time savings, 
further positions the system as a user-centric solution that 
enhances operational flow without sacrificing security.

Comparison with Recent Developments and Novelty
In the context of  recent advancements in parking 
management and IoT-based systems, the integration of  
AES encryption with QR code technology distinguishes 
this work from existing solutions. Recent literature 
emphasizes the growing importance of  data security in 
smart systems, with studies noting that many parking 
management platforms lack robust encryption, leaving 
them vulnerable to unauthorized access (e.g., Chai et 
al., 2023, on secure QR-based systems). Unlike these 

systems, the Parking Occupant Management System 
leverages AES encryption to safeguard data at rest and 
during transmission, addressing a critical challenge in the 
field. This focus on security aligns with current trends 
toward privacy-centric design in digital infrastructure, 
particularly as urban environments increasingly adopt 
smart technologies.
Moreover, the system’s adaptability to educational 
institutions—a setting often overlooked in favor of  
commercial or municipal parking solutions—adds to its 
novelty. While some smart parking systems prioritize 
features like space allocation or payment processing 
(Lin et al., 2017), this system emphasizes secure 
communication between parking attendants and vehicle 
owners via encrypted QR codes. This capability fills a gap 
in campus parking management, where manual processes 
have historically limited efficiency and responsiveness to 
incidents like unauthorized parking or emergencies.

Supporting Visuals
The system’s automated workflow and security features 
are captured in three key figures, providing a clear visual 
overview of  its operational efficiency and data protection 
capabilities. These visuals highlight the interactions within 
the system and the encryption-decryption processes that 
safeguard sensitive information.
Figure 4 shows the automated workflow of  the system. 
Parking occupants submit personal information to 
generate QR codes, guards scan these codes to manage 

Figure 4: Context Diagram

parking logs, and admin personnel oversee operations, 
making the entire process more organized and efficient.
Figure 5 illustrates the AES encryption process, where 
plaintext data is transformed into secure ciphertext. This 
encrypted data is embedded into QR codes, ensuring that 
access remains protected and confidential.
Figure 6 shows the AES decryption process, reversing the 
encryption to retrieve the original data from the QR codes. 
This step ensures that only authorized personnel can access 
the information, maintaining data integrity and security.
Together, these figures demonstrate how the system 

enhances parking management efficiency while employing 
robust security measures. The inclusion of  both the 
AES Encryption Flow (Figure 5) and AES Decryption 
Flow (Figure 6) emphasizes the complete cycle of  data 
protection, from securing information to safely retrieving 
it, making the system a notable advancement in secure 
parking management.

CONCLUSION
The Parking Occupant Management System using QR 
Code Solutions with AES Algorithm was successfully 



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Figure 5: AES Encryption Flow Figure 6: AES Decryption Flow

developed and evaluated, demonstrating high levels 
of  user satisfaction across various aspects including 
interface design, functionality, usability, user experience, 
and security. This system addresses critical inefficiencies 
in manual vehicle cataloging processes within educational 
institutions by automating vehicle tracking and enhancing 
data security through AES encryption, significantly 
improving accuracy, efficiency, and security in parking 
management. Its importance lies in providing a modern, 
automated solution that reduces human error, improves 
overall workflow, and ensures robust protection of  
sensitive data, making it a valuable advancement for 
campus parking facilities. Despite its strengths, the 
study faces some limitations: it is specifically tailored 
for educational institutions in General Santos City, does 
not extend beyond campus boundaries, restricts QR 
code generation to faculty, staff, and students, relies on 
internet connectivity for logging, is designed exclusively 
for Android devices, and lacks the capability to monitor 
parking space occupancy. Despite these constraints, 
the system holds substantial relevance for educational 
institutions aiming to upgrade their parking management 
practices, offering a secure and efficient framework that 
could potentially be adapted for broader use in commercial 
and residential parking facilities. The application of  QR 
code technology paired with AES encryption sets a new 

standard for secure parking operations, with scalability 
that promises wider implementation beyond its current 
scope. To enhance its future potential, it is recommended 
that the system incorporate advancements in encryption 
technologies to sustain its security edge, expand its 
functionality to include features like predictive parking 
availability and real-time notifications, and develop 
versions for additional mobile platforms to broaden 
accessibility and user adoption. This comprehensive 
approach ensures the system not only meets current 
needs but also paves the way for smarter, more secure 
parking management solutions.

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