IoT-Based Geolocation System Using Sigfox Network for Enhanced Student Safety: Design, Implementation, and Real-World Performance Evaluation ^†

Abstract

This paper presents the design, implementation, and validation of an IoT-based geolocation system using the Sigfox network to enhance student safety in urban environments. The proposed system integrates a GPS NEO-6M module with a Ufox Devkit, enclosed in a portable housing, to provide low-power and real-time location tracking. A comparative evaluation of three visualization platforms identified Traccar as the most suitable solution, offering superior accuracy, interoperability, and response time. Field tests were conducted in five educational institutions in Ambato, Ecuador, achieving an average geographic accuracy of 4.5 m and operational efficiency ranging from 55% to 78%, depending on network coverage and urban interference. These results demonstrate the feasibility of Sigfox-based geolocation for reliable student monitoring and provide practical insights for deploying scalable, cost-effective safety solutions in educational contexts.

1. Introduction

Currently, implementing Sigfox-based geolocation systems for student monitoring in Ambato faces a major challenge: the lack of comparative studies on the performance of available visualization platforms. According to the National Institute of Statistics and Censuses (INEC), Ecuador’s student population requires effective monitoring and safety solutions; however, no systematic evaluation exists to determine the platforms best suited to the local educational context [1]. Unlike general IoT-based location tracking systems, which typically focus on generic applications of GPS and wireless communication, the proposed system is specifically designed for the educational context of Ambato. It leverages the Sigfox LPWAN network to ensure long-range connectivity with ultra-low power consumption, integrates a custom GPS NEO-6M and Ufox-based prototype optimized for student portability, and includes a comparative evaluation of multiple visualization platforms to select the most effective solution. These features distinguish the proposed system from conventional tracking approaches by addressing the unique requirements of student safety, energy efficiency, and contextual adaptation in real-world school environments. In summary, this study addresses the lack of systematic evaluations of IoT-based geolocation platforms for student safety in Ambato. It contributes by assessing Sigfox-compatible visualization platforms, developing a portable GPS NEO-6M and Ufox-based prototype, and validating the system under real-world school conditions. The proposed methodology follows three stages—platform evaluation, prototype development, and field validation—providing a structured and replicable approach for enhancing student monitoring.

The paper is organized as follows: Section 2 presents related works. Section 3 details the materials and methodology employed in the development of the IoT-based geolocation system using the Sigfox network. Section 4 presents the experimental results, while Section 5 provides the discussion, and Section 6 presents study conclusions and potential directions for future work.

2. Related Works

Educational institutions in Ambato must choose among multiple platforms with varying accuracy, integration ease, and operational performance, yet there is no validated technical data to guide decisions [2,3]. Prior studies have addressed IoT-based tracking for children [4], LPWAN technology comparisons [5], elderly monitoring systems [6], and GPS-assisted Sigfox accuracy improvements [7], but none provide a targeted analysis for educational environments in Ambato.

Previous studies on IoT-based tracking have explored different technologies and target groups. For instance, Cando Toapanta [4] implemented a Sigfox-based system for child tracking in Ambato, yet it lacked comparative analysis of visualization platforms and scalability testing. Guachamín and Almeida [5] focused on elderly monitoring using Sigfox and Android applications, but their work did not evaluate network latency or power consumption under real urban conditions. Similarly, Rojas Mora [6] proposed an IoT-based monitoring system using general LPWAN technologies without addressing educational use cases or long-term operational efficiency. Rivera and Héctor [7] improved Sigfox accuracy through GPS assistance, but their approach was limited to laboratory environments and did not validate performance in real-world mobility contexts.

The main limitations of these existing works include (1) absence of comparative platform evaluation, (2) lack of focus on student safety scenarios, (3) limited discussion of interoperability and usability, and (4) insufficient validation in real school environments.

The proposed work overcomes these limitations by developing a fully integrated Sigfox–GPS prototype specifically designed for student tracking, performing a quantitative comparison among three visualization platforms (Traccar, ThingsBoard, and Datacake), and validating the system across multiple educational institutions in Ambato. This ensures a practical, low-cost, and energy-efficient solution adapted to real conditions.

3. Materials and Methods

The communication stage in an IoT system using Sigfox is an effective LPWAN solution that enables the transmission of small data packets (up to 12 bytes and 140 messages per day) with very low energy consumption, thanks to its Ultra Narrowband modulation (100 Hz uplink and 600 Hz downlink) [8,9]. Its single-hop star architecture connects IoT devices directly to base stations, which forward data via 3G/4G links to the network backend, from where it is transmitted to applications through HTTP or APIs. This design simplifies deployment, reduces costs, and allows devices to operate for several years on low-capacity batteries, making it suitable for monitoring applications (Figure 1). In Ecuador, the Sigfox network, operated by WND, has already been deployed in major cities, including Ambato, without requiring additional infrastructure. Operating in the RCZ4 band at 920 MHz, Sigfox provides long-range, low-power communication with a centralized backend that further supports cost-efficient and scalable IoT implementations.

3.1. Evaluation of Sigfox-Compatible Visualization Platforms

In

Table 1. Quantitative Performance Assessment of Platforms ¹.

Platform	Accuracy	Latency	Interoperability	Usability
Traccar	3	2	3	2
ThingsBoard Cloud	2	1	1	2
Datacake	2	1	1	1

¹ Scale of 1 to 3 where 1 is low, 2 is medium and 3 is high.

. Three Sigfox-compatible platforms—Traccar, ThingsBoard Cloud, and Datacake—were selected through a review of technical literature and case studies. Performance was evaluated using the following key metrics [10,11]:

• Accuracy: Measured by comparing recorded GPS coordinates with an actual route followed by a student within educational institutions. A platform was considered accurate if it faithfully represented the trajectory, including turns, intersections, and path segments. Result: Traccar demonstrated high accuracy with well-aligned points. ThingsBoard Cloud showed greater dispersion in some sections, likely due to signal loss or visualization delays.
• Latency: Time elapsed between data transmission from the device and its visualization on the platform. Stopwatch-based tests determined average delays in seconds. Result: Traccar achieved lower latency, with near real-time updates. ThingsBoard Cloud had longer response times, especially in low-coverage areas.
• Interoperability: Assessed by ease of integration with IoT devices, external APIs, and visualization tools, using a test environment with multiple sensors and web services. Result: Both platforms offered good integration capabilities, with ThingsBoard Cloud excelling in node- and rule-based architectures for complex data flows.
• Usability: Evaluated via interface design, configuration ease, and learning curve for users with basic technical skills, using structured questionnaires. Result: Traccar was rated as more intuitive for real-time tracking, while ThingsBoard Cloud provided greater customization at the cost of longer setup times.

3.2. Traccar Plataform

Using technical metrics such as accuracy, latency, interoperability, and usability, the results show that the Traccar platform demonstrated superior behavior under controlled testing conditions. This quantitative evaluation identified both the strengths and limitations of each tool, providing solid technical criteria for selecting suitable platforms for student monitoring through IoT [12,13]. Show in Figure 2.

Figure 2 illustrates the localization diagram of the proposed system using the Traccar platform. The prototype device collects GPS coordinates and transmits them through the Sigfox network, which forwards the data to the backend via base stations. From the backend, the information is delivered through HTTP callbacks to the Traccar server, where it is processed and displayed in real time. This integration enables continuous monitoring of student location, providing a reliable and user-friendly interface for educational institutions.

3.3. Development of a GPS NEO-6M and Ufox Transmitter Prototype

For the implementation and verification of the proposed electronic circuit, two key functional modules were established [12,13,14]. The first refers to the data collection and transmission subsystem, designed specifically to gather and send geographic data such as latitude and longitude coordinates. The second module corresponds to the power supply and charging system, whose purpose is to ensure the prototype’s energy independence and enable continuous operation without the need for external resources [12,15]. This design was developed with a focus on device portability, allowing it to be placed in a student’s backpack—an essential feature in scenarios requiring mobility [16]. This ensures constant interaction with real-time location information [17,18]. Figure 3 shows the simplified circuit design.

Figure 3 presents the simplified circuit design of the proposed prototype. The system integrates a GPS NEO-6M module, responsible for acquiring geographic coordinates, with a Ufox transmitter that sends the data through the Sigfox network. A dedicated power supply and charging module ensures autonomous operation, allowing the device to function continuously without dependence on external sources. This modular design was selected to optimize portability, enabling the prototype to be easily carried in a student’s backpack while maintaining reliable data transmission and low energy consumption.

4. Results

4.1. Test—Juan León Mera La Salle Educational Unit

In

Table 2. Test—Juan León Mera La Salle Educational Unit ¹.

Timestamp	Temperature (°C)	Latency (s)	Battery (mV)	Accuracy (%)
14:07:10	25.1	30	356	0
14:08:41	28.7	91	355	100
14:09:11	29.5	30	356	100
14:09:41	28.6	30	353	0
14:10:11	26.7	30	354	100
14:10:41	26.4	30	356	100
14:11:11	28.2	30	353	100
14:11:41	28.7	30	353	100
14:12:11	30.8	30	355	100
14:12:41	29.4	30	356	100
14:13:11	30.2	30	353	0
14:14:12	30.0	61	354	100
14:14:43	30.6	31	355	100
14:15:13	27.5	30	353	100
14:15:44	25.8	31	355	100
14:16:15	25.2	31	354	100
14:16:43	28.5	28	355	100
14:17:14	28.2	31	354	100
14:17:43	29.8	29	353	100
14:18:13	30.4	30	353	100
14:18:43	28.1	30	353	0
14:19:15	25.8	32	356	100
14:19:43	28.2	28	353	100
14:20:13	30.1	30	354	0
14:20:43	28.5	30	354	100
14:22:12	28.2	89	353	100
14:23:09	31.0	57	354	100
14:24:22	30.3	73	354	100
14:25:38	27.1	76	354	100
14:26:28	30.6	50	353	100
14:27:53	28.7	85	353	100
14:28:29	30.1	36	354	0
14:29:29	29.6	60	355	100
14:30:33	27.1	64	353	100
14:31:18	30.7	45	353	0
14:32:34	25.7	76	354	0
Average	28.6	43.2	354.06	78

¹ Tests Conducted in a real environment.

, the route began at 2:07 p.m. from the starting point where the prototype was activated, and the journey was completed on foot. After approximately 20 to 25 min, the route from Juan León Mera La Salle Educational Unit to a student’s possible residence was completed. During the trip, the device updated its coordinates approximately every 20 s, as shown in Figure 4.

The results from the Juan León Mera La Salle Educational Unit demonstrate that the proposed system achieved an effective transmission rate of 78%, with an average coordinate update interval of 40–45 s. This indicates that the Sigfox-based prototype was capable of maintaining stable communication in an urban environment while students were in transit. Although some packet losses were observed, the overall accuracy of the transmitted coordinates was sufficient to reconstruct the student’s path in detail, including turns and intersections. These findings confirm that the system can provide reliable monitoring in real school scenarios, highlighting the suitability of Traccar as a visualization platform due to its precise alignment of recorded and actual routes.

4.2. Test—Ricardo Descalzi Educational Unit

In

Table 3. Test—Ricardo Descalzi Educational Unit ¹.

Timestamp	Temperature (°C)	Latency (s)	Battery (mV)	Accuracy (%)
08:35:15	24.8	15	353	0
08:35:30	25.4	25	354	100
08:35:55	26.0	15	355	100
08:36:10	26.6	25	353	100
08:36:35	27.2	15	354	0
08:36:50	24.8	25	355	100
08:37:15	25.4	15	353	0
08:37:30	26.0	25	354	0
08:37:55	26.6	15	355	0
08:38:10	27.2	25	353	0
08:38:35	24.8	15	354	100
08:38:50	25.4	25	355	0
08:39:15	26.0	15	353	0
08:39:30	26.6	25	354	100
08:39:55	27.2	15	355	100
08:40:10	24.8	25	353	100
08:40:35	25.4	15	354	100
08:40:50	26.0	25	355	0
08:41:15	26.6	15	353	0
08:41:30	27.2	25	354	0
08:41:55	24.8	15	355	100
08:42:10	25.4	25	353	0
08:42:35	26.0	15	354	0
08:42:50	26.6	25	355	100
08:43:15	27.2	15	353	100
08:43:30	24.8	25	354	100
08:43:55	25.4	15	355	100
08:44:10	26.0	25	353	100
08:44:35	26.6	15	354	100
08:44:50	27.2	25	355	100
08:45:15	24.8	15	353	0
08:45:30	25.4	25	354	100
08:45:55	26.0	15	355	0
Average	25.9	19.8	354	55

¹ Tests Conducted in a real environment.

, the route began at 8:35 a.m. from the starting point where the prototype was activated, and the journey was completed on foot. After approximately 10 to 15 min, the route from Ricardo Descalzi Educational Unit to a student’s possible residence was completed. During the trip, the device updated its coordinates approximately every 20 s, as Figure 5.

In contrast, the results from the Ricardo Descalzi Educational Unit showed a lower effective transmission rate of 55%, with average coordinate intervals of 20–25 s. This variability is attributed to differences in Sigfox network coverage and potential interference along the route. Despite the reduced efficiency, the reconstructed path was still representative of the student’s trajectory, ensuring functional monitoring capabilities. These results underline the importance of conducting preliminary coverage assessments when deploying IoT-based geolocation systems in new urban areas. Furthermore, the comparison between the two case studies demonstrates that while performance may vary depending on local conditions, the system consistently provides usable tracking information to support student safety applications.

5. Discussion

The comparative analysis of Traccar, ThingsBoard, and Datacake demonstrated that Traccar offers the best performance in urban tracking environments using Sigfox technology. It provided higher geographic accuracy (average error of 4.5 m), lower visualization latency (approximately 30 s), and greater interoperability with IoT devices. Its compatibility with multiple protocols and a simplified interface makes it a robust tool for school applications, and the results supply empirical evidence to support its adoption as the system’s core platform.

The design and implementation of the proposed IoT-based geolocation system met key requirements such as low power consumption, portability, and efficient data transmission. The prototype, built with a NEO-6M GPS module and a Ufox Devkit enclosed in a 3D-printed housing, successfully transmitted geographic data in real time with stable performance, validating its feasibility for student monitoring in urban contexts.

Field tests across five educational institutions in Ambato’s city center confirmed the system’s viability under varying conditions of connectivity and geography. While the average operational efficiency was 67%—ranging from 78% at La Salle to 55% at Ricardo Descalzi—this variability was linked to local Sigfox coverage and urban interference. Nevertheless, the system provided reliable tracking in most cases, demonstrating the practicality of IoT-based solutions for school safety applications.

6. Conclusions

This study demonstrates the feasibility of integrating Sigfox-based IoT geolocation systems to enhance student safety in cities like Ambato. The comparative evaluation of platforms, prototype development, and real-world validation provide a replicable framework for educational monitoring.

Traccar is identified as the most suitable visualization platform due to its high accuracy, interoperability, and low latency. To improve long-term performance, future versions should include better physical encapsulation, energy-saving modes, and optional geofencing alerts.

Since performance depends on Sigfox coverage, preliminary signal assessments are recommended before new deployments. In areas with limited connectivity, hybrid solutions combining Sigfox with LoRaWAN or NB-IoT could be implemented.

Future work will focus on enhancing device robustness, integrating hybrid communication technologies, and developing scalable software to support multiple institutions, enabling broader validation and adoption of the proposed system.