Offline System for 2D Indoor Navigation Utilizing Advanced Data Structures

Abstract

This study introduces a robust offline system for 2D indoor navigation, developed to address common challenges such as complex layouts and connectivity constraints in diverse environments. The system leverages advanced spatial modeling techniques to optimize pathfinding and resource efficiency. Utilizing a structured development process, the proposed solution integrates lightweight data structures and modular components to minimize computational load and enhance scalability. Experimental validation involved a comparative approach: traditional navigation methods were assessed against the proposed system, focusing on usability, search efficiency, and user satisfaction. The results demonstrate that the offline system significantly improves navigation performance and user experience, particularly in environments with limited connectivity. By providing intuitive navigation tools and seamless offline operation, the system enhances accessibility for users in educational and other complex settings. Future work aims to extend this approach to incorporate additional features, such as dynamic adaptability and expanded application in sectors like healthcare and public services.

1. Introduction

The evolution of mobile applications tailored for indoor navigation has garnered significant attention, particularly within complex environments such as university campuses. While GPS-based systems are highly effective for outdoor navigation, their limitations in indoor settings—where stable internet connectivity and precise localization can be challenging—have necessitated the development of alternative solutions. Previous studies have explored different approaches, including virtual reality (VR) to create immersive campus tours that increase user engagement and satisfaction. For instance, Kerubin et al. [1] demonstrated how VR could be utilized to offer virtual tours via smartphones, enabling institutions to showcase their campuses to a global audience. Similarly, projects by Veloz et al. [2] and Lu et al. [3] have leveraged modern technology to develop interactive and efficient virtual tours. However, many of these solutions rely on advanced hardware or continuous internet connections, which can limit their accessibility and usability across diverse geographic regions [4,5].

In response to these challenges, recent research has focused on developing more accessible and efficient tools that can operate in environments with limited connectivity. For example, the application by Schuldt et al. [6] have successfully utilized indoor positioning technology to assist users in navigating university buildings, significantly improving user experience by reducing the time needed to locate destinations. Additionally, other initiatives such as those by Brown et al. [4] and Kamaruzzaman et al. [5] have demonstrated the effectiveness of combining lightweight, open-source frameworks with robust backend services to create functional and user-friendly applications. These advancements not only address the limitations of earlier models but also ensure that the applications remain operational under various network conditions, making them valuable tools for universities and similar institutions [7,8].

The role of navigation aids extends beyond simple direction-giving; it involves improving the overall user experience, particularly for individuals with disabilities. Shahini et al. [9] emphasize the importance of developing features that cater to people with mobility impairments, ensuring that navigation systems are inclusive and usable by a diverse population. The importance of accessibility is also highlighted by research into wayfinding signage and systems designed for people with color blindness, as explored by Lee et al. [10], and the navigation challenges faced by deafblind individuals, as discussed by Swobodzinski et al. [11]. Additionally, advanced technologies such as digital twins have proven beneficial in visualizing and optimizing indoor environments for energy efficiency and user comfort. Digital twins allow for the simulation of indoor spaces and the monitoring of environmental parameters, providing users with insights into the impact of design choices on indoor conditions, thereby enhancing their understanding and experience [12].

The development of mobile applications for indoor environment tours, particularly those utilizing graph theory and the A* algorithm, necessitates a detailed understanding of the various factors that influence indoor navigation, spatial memory, user experience, and accessibility. Eklund et al. [13] highlighted the significance of location-based context-aware services in digital ecosystems, which are essential for tailoring navigation experiences to individual user needs. Kim et al. [14] demonstrated the effectiveness of mobile applications for monitoring environmental factors such as indoor air quality, which is crucial for the well-being of users in enclosed spaces. Moreover, Iftikhar et al. [15,16] explored the synthesis of static and mobile wayfinding information, emphasizing the importance of integrating various sources of wayfinding data to enhance navigation in complex environments. Jamshidi [17] further identified key environmental elements and attributes that contribute to effective indoor wayfinding, while Williams [18] discussed personalized wayfinding assistance, demonstrating how user-centered design can improve navigation outcomes. This approach aligns with universal design principles, which aim to make environments accessible to all individuals, including those with special needs [19].

Building Information Modeling (BIM) has also been integrated with mobile navigation applications to create highly accurate indoor maps, which are particularly useful in complex environments such as university campuses [20,21]. Additionally, the adoption of technologies such as Bluetooth Low Energy (BLE), WiFi positioning systems, and near-field communication (NFC) has enabled the development of efficient indoor navigation systems. These systems are especially suited to intricate indoor spaces like university buildings, where maintaining accurate positioning and providing real-time guidance are crucial [22,23,24]. The use of beacon technology, as discussed by Uttarwar et al. [25], and augmented reality (AR)-based systems [3,26] further contributes to the development of sophisticated navigation tools that enhance the user experience by offering context-aware, dynamic guidance.

A crucial aspect of developing effective indoor navigation applications is ensuring that they work reliably in environments with limited or no internet connectivity, as in remote areas the accuracy of GPS and therefore the reliance on signal coverage may be insufficient. Offline functionality is essential for maintaining system usability in scenarios where network connectivity is unreliable or unavailable [27]. This functionality is particularly important in critical sectors such as healthcare, where consistent service delivery is necessary even in remote or connectivity-challenged environments [28]. Additionally, offline capabilities are valued in other domains, such as tourism and education, where uninterrupted access to navigation services enhances user satisfaction and operational efficiency [29,30].

The focus on inclusive design is relevant in research that emphasizes creating barrier-free wayfinding and navigation systems. These systems must cater to all users, including those with disabilities. Studies have highlighted the importance of environmental cues, personalized wayfinding assistance, and open geospatial platforms in developing more accessible and user-friendly navigation systems [31,32]. Furthermore, the integration of AR-based navigation with hybrid maps [33] and the consideration of specific needs such as color blindness and deafblindness ensure that navigation systems are inclusive and meet the diverse requirements of all users [17,18].

The adoption of innovative technologies and methodologies has significantly advanced indoor navigation systems, particularly in complex environments like university buildings. For instance, Singh [34] introduced the Three-Dimensional Routing Information Framework (3D RIF) to enhance indoor navigation by leveraging 3D geometry for accurate route planning. Similarly, the combination of iBeacons with particle filter methods and indoor magnetic fingerprint maps has proven effective in developing precise indoor positioning systems [35,36]. These advancements, along with the integration of AR, mobile projectors, and RFID devices, contribute to the creation of robust and user-friendly navigation tools that cater to the specific needs of university communities [37,38].

Therefore, the development of mobile applications for indoor navigation in university buildings requires a multidisciplinary approach (Figure 1) that integrates advanced technologies, ensures offline functionality, and incorporates inclusive design principles. By leveraging these insights, developers can enhance the wayfinding experience for all users, creating comprehensive indoor navigation systems that meet the diverse needs of educational environments. This underscores the importance of usability, efficiency, and personalization in designing systems that not only improve navigation but also contribute to the overall accessibility and inclusivity of university campuses and other institutions of a similar nature.

The main objective of this study is to show how graph theory and the A* algorithm enable route planning optimization in complex indoor environments through an indoor navigation mobile application. In addition, we evaluate the application’s usability and user experience in scenarios with little or no internet connection through user testing with university students. Furthermore, the performance of the developed application is compared with existing solutions, focusing on the offline functionality and the advantages of the open-source design. Validation was conducted through empirical testing in a university environment, including key metrics such as the average route computation time. Additionally, the performance was compared with other route planning techniques, such as Dijkstra’s algorithm and the EBS-A* variant, highlighting the efficiency of the proposed model in terms of reducing computational load and improving user experience.

2. Materials and Methods

The method used in this study was structured in different phases (Figure 2). In Phase I, the researchers adopted a user-centered design approach. Phase II was devoted to a comprehensive analysis of the user satisfaction with the application. In Phase III, a comparative analysis of the development proposed in this research with other navigation systems was carried out. This method allowed the researchers to gain a holistic understanding of how users interact with the application, identify areas for improvement and, in Phase IV, draw several relevant conclusions.

2.1. App Design

The development of the mobile navigation app followed the Waterfall model, with the process divided into distinct phases:

• Requirements Gathering: This phase involved collecting data from users and stakeholders, focusing specifically on navigation challenges within university environments lacking stable internet connectivity. The analysis covered architectural layouts, user mobility patterns, and specific requirements for offline navigation.
• System Design: Graph theory was applied to model indoor spaces and define optimal navigation paths, with the A* algorithm selected for efficient route optimization. The system’s data architecture was based on Firebase’s document-oriented structure, facilitating scalability and flexibility, while SQLite ensured robust offline storage on mobile devices for uninterrupted navigation. To improve the performance of the A* algorithm, three key components were developed that correct the disadvantages that have existed until now: (i) Animate: manages the determination of the route to design the plane at a high level; (ii) Walking: manages the animation in real time to draw the route, in addition to verifying the existence of curves and correcting the trajectory accordingly; and (iii) CorrectTrajectory: identifies available cells to complete the trajectory efficiently.
• Implementation: The app was developed using the Ionic framework for cross-platform deployment, with Angular used to manage the front end and Firebase for backend support. CapacitorJS was integrated to ensure seamless offline functionality, allowing users to access the navigation features even in areas with unreliable internet. Figure 3 and Figure 4 show the architecture used in the web and mobile applications.
• Testing: Comprehensive black-box and white-box testing methodologies were employed to assess the app’s stability, performance, and robustness. User acceptance testing involved university students, staff, and visitors, with feedback gathered through questionnaires to evaluate user satisfaction, app functionality, and overall performance.
• Deployment: The app was deployed within the university, with continuous feedback mechanisms set up to identify and implement potential enhancements. User feedback played a key role in guiding future updates and iterations of the app.

2.2. Satisfaction Questionnaire

To measure user satisfaction, a questionnaire of 7 questions of our own design was used, asking users to rate different technical and usability aspects of the app on a Likert scale of 1 to 5 (Supplementary Materials). To measure this satisfaction, two explained variables were defined: (i) the app usability rating; and (ii) rating of the technical aspects of the app.

The sample of participants consisted of 29 university students with humanities degrees. Participants were selected using a convenience sampling approach, considering their familiarity with campus spaces and their experience with digital tools, which facilitated an objective evaluation of the application. The selection of this population was also based on their frequent mobility within university buildings, allowing for a more comprehensive validation of the system in a real-use environment. An Exploratory Factor Analysis (EFA) was carried out on the responses obtained, which confirmed that the model that explains the greatest proportion of the variance of the responses consists of two factors or families of questions that correspond to the defined explained variables. It has also been verified, by means of Cronbach’s alpha parameters, that the responses enjoy high internal reliability. Finally, conclusions were drawn from the descriptive statistics of the responses and grouped according to the factors identified in the EFA.

3. Results and Discussion

3.1. App Design

The developed mobile application is designed to guide users through university buildings, offering a robust solution even in environments with unstable internet connectivity. It employs a matrix-based model to represent spaces and routes (Figure 5), optimizing storage usage on the device, which was used in previous studies [39]. Unlike other applications that depend on stable internet connections and large files, such as GIFs or AutoCAD 2023 templates, this application minimizes storage requirements and operates efficiently offline due to CapacitorJS 5.6 integration.

In comparison to other applications, such as those discussed in [1,3], which rely heavily on virtual reality and continuous internet connectivity, the current application provides a more accessible and practical alternative, particularly in areas with limited infrastructure. Previous studies [4] highlighted the importance of stable connectivity in navigation applications, while our solution emphasizes offline functionality, ensuring users can access navigation assistance regardless of internet availability. This study provides a more accessible and practical alternative, particularly in areas with limited infrastructure. This accessibility improvement can be seen in studies such as [5], as well as in tests demonstrating the app’s ability to function offline. The lightweight data structure used in the system reduces computational requirements compared to cloud-reliant models, ensuring smooth operation even on low-end mobile devices.

As shown in Figure 6, the app’s primary interface allows users to view their current location within the university building and receive real-time directions to their selected destination. The building layout is presented using a matrix-based model, which simplifies the display of rooms and facilities, making navigation more intuitive for users [39]. Furthermore, this interface offers detailed information about specific locations, such as offices and their associated staff members. The numbers displayed in the figure, such as 103 and 104, represent room identifiers within the building, assisting users in navigation. The localization system relies on initial positioning based on common access points within the building map. It dynamically updates the user’s position according to the selected route and estimated movement, simulating user progression. Additionally, the system provides real-time guidance, automatically recalculating the route if the user adjusts their location or changes the path.

In Figure 7, the process of selecting a destination and navigating various environments within the building is illustrated. The application features a dynamic selection tool that enables users to specify both their current location and desired destination, while offering additional options such as elevators and floor-specific navigation. This functionality is designed to enhance the overall user experience by providing intuitive and accessible navigation features, ensuring efficient route planning within complex, multi-floor structures.

3.2. Graph Theory and A* Algorithm Implementation

The graph theory integration in the mobile application enables efficient modeling of complex indoor environments as simplified nodes and edges, representing rooms, hallways, and navigation paths (Figure 8). This abstraction minimizes computational complexity and allows the application to process data rapidly, even on devices with limited resources. Experiments have demonstrated how traditional A* algorithms on grid-based maps require exploring thousands of nodes. In [40], 107,568 nodes were reported to be explored in a 500×500 grid environment. In contrast, a graph-based method reduced the search space to just 18 nodes, achieving a 30-fold improvement in efficiency. Additionally, the planning time decreased from 5.64 s to 0.1579 s, and the generated path length was 7% shorter, demonstrating both faster execution and better route optimization. These improvements highlight the advantages of graph-based preprocessing, which minimizes redundant computations, reduces memory usage, and ensures rapid path planning.

The A* algorithm is an optimal route-finding mechanism that uses a heuristic approach to calculate shorter and more efficient routes in real time. This algorithm considers user preferences, such as accessibility routes or elevator availability, allowing for personalized and efficient navigation. Unlike applications that rely on stable internet connections, the A* algorithm works effectively offline, using preloaded spatial data. This capability ensures high performance even in areas with little or no internet connectivity.

Alternative approaches, such as EBS-A*, which introduces distance expansion, bidirectional search, and smoothing, improve route efficiency and reduce the number of critical nodes and straight turns. However, they require considerable computational resources in terms of memory and processing [41]. For its part, the Dijkstra Algorithm can be more efficient than the A* algorithm since it explores many more nodes, especially in large graphs; however, it does not handle graphs with negative weight edges well, limiting its applicability in various contexts [42].

In indoor environments where GPS is not effective, indoor navigation systems offer significant improvements in efficiency and accuracy. However, when using multiple sensors and technologies such as LIDAR and Bluetooth, higher costs and continuous maintenance are required for optimization. In contrast, the developed system provides a more cost-effective alternative. Unlike general-purpose mapping applications like Google Maps, which rely on the internet and WiFi/Bluetooth for localization, this system operates completely offline with high accuracy. Additionally, it supports accessible routes for individuals with reduced mobility. Its low resource consumption and local storage make it more efficient on mobile devices, ensuring optimal performance in connectivity-limited environments, such as university buildings [43]. Additionally, navigation methods based on computer vision provide more precise and robust navigation, better adapting to different environments and user needs, but may be less effective in environments with significant variations in lighting or with frequent visual obstructions [44].

This proposal stands out for its efficiency in the use of resources, reducing storage and computational load through a matrix-based model that integrates graph theory with the A* algorithm. This optimization is crucial in environments where device storage and processing power are limited, aligning with previous research promoting lightweight indoor navigation solutions and thus benefiting users with low-power mobile devices.

While previous studies, such as those by Brown et al. [4] and Schuldt et al. [6], emphasized the need for stable internet connections and robust backend services, this study focuses on offline functionality. By integrating CapacitorJS within the Ionic framework, the application ensures seamless performance without internet access. This offline capability significantly enhances usability in regions with limited infrastructure, such as remote campuses or areas with fluctuating connectivity. As demonstrated in similar environments, this approach effectively addresses the challenge of unstable or nonexistent internet, allowing the system to remain functional regardless of external conditions.

3.3. User Satisfaction

The Exploratory Factor Analysis carried out on the responses to the satisfaction questionnaire revealed the existence of two factors or families of questions whose responses are strongly correlated, as shown in

Table 1. Weights of the Exploratory Factor Analysis.

Question	Factor 1. App Usability	Factor 2. App Technical Aspects
The app content is clear	0.86
It is easy to use the app	0.74
It is easy to search places	0.76
The app is intuitive	0.90
It is quick to get familiar with the app	0.79
App design assessment		0.78
Packages are consistent for offline use		0.85

. The two-factor model thus obtained explains a cumulative variance of 83.30%. Likewise, the reliability of the model, measured in terms of Cronbach’s alpha parameter, is adequate, since all the parameters are greater than 0.70, both for the factor measuring app usability (0.95) and for the factor measuring the assessment of technical aspects (0.96).

The average ratings of the two variables analyzed (corresponding to the two factors identified in the EFA, i.e., usability and technical aspects) are very high (above 4 out of 5 in both cases) and there are no significant differences between them (

Table 2. Descriptive statistics of the responses.

Family of Questions	Mean (Out of 5)	Standard Deviation (Out of 5)
Usability	4.80	0.42
Technical aspects	4.74	0.48

). Likewise, there are no significant differences between the dispersions of the responses of both variables, measured in terms of standard deviation (Table 2). Consequently, mean satisfaction is high for both technical and usability aspects, with no significant gaps in these ratings.

The findings from the user satisfaction survey confirm that the mobile application is both intuitive and accessible, with high usability ratings across the tested user base. These results are consistent with prior studies that underline the importance of user-friendly interfaces in navigation systems, particularly in environments where users may be unfamiliar with complex interfaces or limited by physical or cognitive impairments [9,19]. The use of a simplified 2D layout, in contrast to more resource-intensive virtual reality systems, ensures that the application can be easily adopted by users with varying levels of technical proficiency [10,17].

Additionally, the dynamic route selection tool, which enables users to define both their current location and desired destination while incorporating features such as floor-specific navigation and elevator routes, is a key aspect of the application’s usability. Previous research has noted the significance of such adaptable systems for enhancing user satisfaction and reducing the time spent navigating complex indoor spaces [12,15].

3.4. Comparative Performance Analysis

In comparison to the traditional A* algorithm, the modified version used in this study introduces key optimizations that enhance efficiency without significantly increasing computational costs. Specifically, improvements in trajectory management were implemented through the Animate, Walking, and CorrectTrajectory modules, which reduce the number of explored nodes and optimize the route in real time. Additionally, the heuristic and data structure were optimized. Unlike variants such as EBS-A*, which require higher processing capacity, this approach balances efficiency and low resource usage, ensuring its viability on mobile devices and in offline environments [45].

The results of this study clearly demonstrate the effectiveness of the developed mobile application for indoor navigation, particularly in university settings. By employing graph theory and the A* algorithm, the application provides optimized route planning while addressing the persistent issue of poor or unstable internet connectivity—a challenge frequently emphasized in similar studies on indoor navigation systems [1,6,13]. The deployment of this solution at the Faculty of Humanistic Studies at the Technical University of Manabí where internet availability is often unreliable, proves this is particularly valuable.

A significant advantage of the developed application lies in its ability to function offline. In contrast to many existing solutions that rely on continuous internet connectivity for real-time updates and navigation guidance [1,3], the application in this study incorporates CapacitorJS and an optimized data storage structure, enabling full functionality even without internet access. This offline capability not only reduces the need for heavy data loads but also lowers the resource requirements of the application, allowing it to be more accessible to users in regions with underdeveloped or unstable internet infrastructure [18,23].

When compared to other navigation systems, such as those discussed by Kerubin et al. [1] and Lu et al. [3], which rely heavily on advanced hardware (e.g., virtual reality systems or complex 3D interfaces), the developed application presents a more lightweight and practical alternative. Many existing indoor navigation apps demand a continuous internet connection to load detailed maps or 3D models, often requiring large file sizes or sophisticated hardware components to function smoothly. However, these solutions, while effective in certain urbanized or well-equipped areas, present significant challenges in environments where internet access is unstable, or the infrastructure cannot support high data loads [4,19].

The system developed in this study not only circumvents these limitations but also offers a more user-friendly interface by focusing on a simplified 2D layout. This 2D approach reduces the cognitive load on users, making navigation within complex university buildings more intuitive and straightforward. While VR-based and highly interactive applications offer an immersive experience, their reliance on real-time updates and advanced visualization techniques makes them less feasible in rural or developing regions [2,5]. The comparative advantage of the 2D-based solution designed in this study lies in its efficiency and reliability, particularly in contexts where internet access may be sporadic.

The offline functionality of the app, which has been integrated through SQLite and Firebase for data storage, ensures that the user experience remains smooth even in the absence of a network connection. This is particularly advantageous in university settings, where students, staff, and visitors often move through multiple buildings, some of which may have weak or no Wi-Fi coverage. The benefits of such offline capability align with the findings of Schuldt et al. [6], who emphasize the importance of designing systems that prioritize functionality even in low-connectivity environments.

Moreover, by reducing the storage burden on mobile devices through a matrix-based model of space representation, the application minimizes the need for large storage capacities. This is crucial in scenarios where users may be using older devices or those with limited storage, further expanding the potential reach of the system to a broader audience [18,24].

The modified A* algorithm’s performance is superior to traditional algorithms, such as standard A* and Dijkstra. Figure 9 demonstrates this claim using data obtained from [42]. This analysis shows how modifications to the A* algorithm can significantly impact key metrics such as the average execution time (measured in seconds) and average path length (measured in nodes).

The data presented in the figure demonstrate that Dijkstra’s algorithm has the highest average execution time due to its exhaustive approach, which evaluates all possible paths without using heuristic optimization. On the other hand, the standard A* algorithm, thanks to a heuristic function, reduces this search time considerably. However, it still retains certain limitations in terms of optimization. Finally, the modified A* algorithm shows significantly better performance by combining improvement strategies, such as advanced heuristic functions and more efficient data structures. This allows a noticeable reduction in search time while maintaining comparable quality in the length of the generated routes. Furthermore, as highlighted in [45], advanced graph search strategies can effectively reduce the number of nodes explored, which contributes to both improving search time and optimizing the use of computational resources.

Figure 9 supports the hypothesis that improvements in the A* algorithm, such as heuristic optimization or data structure refinement, can significantly reduce the execution time without compromising the quality of the routes. Furthermore, these improvements also have a positive impact on memory usage, as pointed out by [40], where it is demonstrated how the refinement of data structures can minimize storage costs without affecting the performance of the algorithm. This is aligned with the objectives of this work, which seeks to highlight how modifications to A* contribute to improving efficiency both in terms of time and memory, making it more applicable to environments with limited computational resources.

4. Limitations and Lines of Future Research

Despite the numerous advantages offered by the developed application, there are some notable limitations. One primary limitation is its reliance on a 2D interface for indoor navigation, which, while effective for many users, may not be sufficient in more complex environments or for users less familiar with such systems. As noted in studies by Lee et al. [10] and Shahini et al. [9], 2D interfaces may limit spatial understanding, particularly in multi-level structures. Future research should consider the integration of three-dimensional (3D) or augmented reality (AR) interfaces to improve the user experience. Three-dimensional and AR-based systems, as demonstrated by several studies [3,33], have been shown to significantly enhance spatial perception, user engagement, and overall navigation effectiveness in environments such as airports, hospitals, or large shopping malls—areas that present more intricate navigation challenges.

Additionally, expanding the application’s accessibility features is a key area for future development. The incorporation of virtual avatars or personalized navigation aids would provide valuable support for users with disabilities, such as those with mobility impairments or sensory disabilities. Previous research highlights the importance of inclusivity and accessibility in navigation systems, with studies showing that virtual assistants and real-time interactive elements can greatly improve the navigation experience for all users [11,19]. For instance, providing auditory guidance or haptic feedback could assist visually impaired users, while customized routes could be generated based on user-specific needs.

Another area for improvement is the integration of real-time positioning technologies, such as Bluetooth beacons or Wi-Fi triangulation, which could enhance the system’s adaptability and accuracy. These technologies would allow the application to respond dynamically to environmental changes and provide more precise navigation in real-time, as evidenced by the findings in earlier research [5,25]. Incorporating such real-time features would bring the application on par with the most advanced systems currently in use for indoor navigation, offering a more robust and flexible user experience.

5. Scalability and Broader Applications

The scalability of the application is another critical factor to consider for future enhancements. Although this study focused on a university environment, the design principles underpinning the application can be easily adapted to other large, complex structures, such as airports, hospitals, and shopping malls. This adaptability is in line with previous research on scalable indoor navigation systems, which emphasizes the importance of creating flexible, robust solutions that can be tailored to different contexts and user needs [24,35]. Testing the system in various environments would provide valuable insights into its scalability and versatility, helping to refine its functionality for broader use cases.

Moreover, as the application is designed with open-source technologies, it presents significant potential for customization and extension by other institutions or developers. The fact that it is an open-access application is certainly not a solution to a research problem, but it is worth emphasizing because it expands the possibilities for users. The integration of industry-specific requirements or additional features could extend the system’s usability beyond educational settings, further supporting its implementation in other sectors. This approach is in line with previous studies advocating for the development of flexible, cross-domain navigation systems that prioritize adaptability and ease of implementation [6,7].

In summary, while the current version of the application provides a functional and efficient solution for indoor navigation in university settings, future iterations should focus on incorporating more advanced features to enhance user engagement, accessibility, and scalability. Expanding the system to include 3D or AR interfaces, real-time positioning technologies, and personalized assistance would greatly improve its applicability in more complex environments. Testing the system across different contexts and user demographics will provide a comprehensive understanding of its potential for broader adoption and its capability to address the diverse needs of its users.

6. Conclusions

The mobile application developed in this study presents an efficient and accessible solution for indoor navigation, particularly within the Faculty of Humanistic Studies at the Technical University of Manabí. By leveraging graph theory and the A* algorithm, the application optimizes route planning with a clear and efficient 2D layout. Its offline functionality is a standout feature, ensuring reliable access in areas with poor or unstable internet connectivity. User feedback has been overwhelmingly positive, particularly regarding its usability, design, and overall performance. The application has been especially beneficial for new students and visitors, reducing the time spent locating specific areas and improving overall navigation ease.

For future enhancements, the integration of 3D navigation or augmented reality features could significantly enrich the user experience, especially in more complex, multi-level environments. Furthermore, incorporating a virtual avatar for personalized navigation assistance would improve accessibility, particularly for users with disabilities. Expanding the application’s applicability to other environments, such as airports, hospitals, and large commercial complexes, would provide valuable insights into its scalability and robustness, offering potential for broad adoption in various sectors.