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Proceeding Paper

Weather Monitoring and Emergency IoT System in Muang-On Cave, Northern Thailand †

by
Khomchan Promneewat
1 and
Tadsuda Taksavasu
2,*
1
Thoranee Krasib Co., Ltd., 152/10, Chang Phueak, Muang Chiang Mai, Chiang Mai 50300, Thailand
2
Department of Mining and Petroleum Engineering, Faculty of Engineering, Chiang Mai University, 239 Huay Kaew Road, Suthep, Muang Chiang Mai, Chiang Mai 50200, Thailand
*
Author to whom correspondence should be addressed.
†
Presented at the 3rd International Electronic Conference on Processes—Green and Sustainable Process Engineering and Process Systems Engineering (ECP 2024), 29–31 May 2024; Available online: https://sciforum.net/event/ECP2024.
Eng. Proc. 2024, 67(1), 7; https://doi.org/10.3390/engproc2024067007
Published: 18 July 2024
(This article belongs to the Proceedings of The 3rd International Electronic Conference on Processes)
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Abstract

:
This study presents a production and development process of an IoT-based weather monitoring and emergency notification system for confined-space environments. The system comprises four working hardware stations cooperating through a data transfer command, an open-source data management system, and a cloud database. The system was preliminarily tested in a relevant confined-space area known as the Muang-On Cave, located in Chiang Mai, Thailand. The system was used to monitor weather conditions and detect emergency signals at all stations for seventeen days during the wet to dry transitional season. The data, including temperature, relative humidity, carbon dioxide, total volatile organic compounds, and emergency codes, were displayed on the web server every 80 to 110 s. However, the extremely humid conditions in the cave actively affected the erroneous readings of gas-detecting sensors that should be accounted for further improvement. Since the system was devised from low-cost electrical and non-electrical materials and open-source software, the total capital cost of the system production indicates a relatively low cost estimated at nearly USD 200. Testing the studied system in other natural caves elsewhere is highly recommended for system stability assessment.

1. Introduction

In modern technology, the implementation of the Internet of Things (IoT) has revolutionized various sectors, from space to the Earth’s surface [1,2], especially in agriculture [3,4], and even deep underground for monitoring, controlling, and transferring data [5,6]. However, the application of IoT in underground environments remains limited compared to other sectors due to challenging electronic operating conditions, such as high humidity, high temperature [7,8], and limited signal [9].
Caves have a long history with human civilization and were once primary habitats for people [10,11]. While the role of the human habitat has decreased over time, the demand for the uses of caves has yet reduced but shifted to other purposes, whether for tourism [12,13,14], natural georesource management [15,16], religions [17,18], or global climate research [19,20]. However, caves are defined as confined spaces with limited air circulation, posing potential risks to humans, especially their respiratory system [21]. The high humidity levels in caves can also induce the growth of fungi and germs, affecting human health [22]. Thus, cave monitoring and emergency systems are necessary to improve human safety and obtain speleological insights, especially in caves where human activity is involved.
Over 5000 caves have been reported in Thailand Bolger and Ellis [23]. Despite this large number, none have tested IoT operating systems to detect, monitor, or transfer data. This study presents a process for creating an IoT-based weather monitoring and emergency system. The proposed system comprises several individual stations that cooperate via wireless technology. The system is installed and run in one of the limestone caves in northern Thailand for two weeks. The results subsequently provide an assessment of the system performance and the data quality, leading to future improvement. Moreover, this study aims to introduce a chain-like wireless data transmission that augments environmental monitoring and communication in caves, allowing for the transmitting and receiving of collected data or signals from a chamber to an entrance without physically entering the cave.

2. System Development

The cave weather monitoring and emergency system proposed here is predominantly composed of three sections that are worked together (Figure 1). The first component is a set of weather monitoring and emergency rescue request detecting stations placed along the cave passage with proper intervals. The second component is a data transfer method for transmitting the collected data from the innermost to the outermost part of the cave that is identical to a chain-like transferring pattern. The last component is a data display coupling with a system database. Once the data are transferred and received by the final station, particularly set at the cave entrance, they are subsequently displayed on a web application and stored within a database by uploading to the Cloud service via internet Wi-Fi.

2.1. Testing Area and Cave Description

The system testing method was conducted in the Muang-On Cave, one of the famous tourist attractions in the Mae-On district of Chiang Mai, and it has a potential for geopark or geotourism development in the north of Thailand [24]. Based on the public geologic background by the Department of Mineral Resources of Thailand, the cave was formed within a unit of massive gray limestones in the Permian period (Figure 2) [25]. According to the cave survey report provided by Promneewat and Taksavasu in 2024 [26], this cave shows a total length of 229 m, comprising a cave entrance, two main chambers formed in different horizontal levels, and a vertical passage connecting the two chambers presented in Figure 3. The upper chamber has a length of 51 m adjacent to the entrance, whereas the lower chamber exhibits a longer length of 142 m. The connecting passage formed between the two chambers is approximately 36 m. The estimated cave volume is around 23,676 cubic meters.

2.2. Weather Monitoring and Emergency Rescue Request Detecting Stations

IoT technology is applied to produce weather monitoring and emergency rescue request detecting stations for running in the confined space area of the cave. It requires specific tools for data sensing and data processing. Table 1 and Figure 4 show detailed descriptions of microcontrollers and sensors used in this study.
According to Kodali and Mahesh [29], ESP8266 is mentioned as one of the low-cost, light, and convenient IoT microcontrollers for data monitoring and processing purposes. It comes with wireless network functionality that allows for the multiple connectivity of Wi-Fi devices, including other ESP8266 units, and enables data transfer. The board has general-purpose input/output (GPIO) pins for connecting to sensors or other electronic functional components. The coding commands to communicate among the ESP8266 and other devices are written in the C programming language.
Compared to other low-cost temperature and humidity sensors, the DHT22 poses high durability and accuracy in various conditions [30]. It comprises two sensing components, including a capacitive sensor and a thermistor. The capacitive sensor monitors relative humidity by detecting changes in electrical capacity that result from the moisture absorption and desorption of a polymer dielectric material. On the other hand, the thermistor monitors an ambient temperature by measuring resistance at a particular time. It also refers to an electrical resistance thermometer since its measured resistance depends on the surrounding temperature.
The CCS811 is a well-known low-cost sensor used for detecting and measuring carbon dioxide (CO2) and total volatile organic compounds (TVOCs) in several environmental monitoring projects [31,32,33]. Operating on a metal oxide semiconductor (MOX), the sensor is sensitive to reacting with CO2 and TVOCs in the air and subsequently influences a change in electrical conductivity [34]. The concentration of CO2 and TVOCs is then obtained from resistance. The resulting value is often expressed as an equivalent carbon dioxide (eCO2).
Each station is composed of a pair of operating subsystems, including a weather monitor and an emergency rescue request detector (Figure 5). The system is operated using electric power supplied by a cable that extends from the existing electrical cable system in the cave. The output data are an orderly text package displayed on a self-generated web server, including temperature (in a unit of degree Celsius), humidity (in a percent unit), CO2 (in a unit of parts per million), TVOCs (in a unit of parts per billion), and ultimately an encoding number for an emergency rescue request signal (1 for “no” and 2 for “yes”). Each sectional text datum is constantly separated by a slash (/). Mobile devices such as laptops, smartphones, tablets, or other ESP8266 modules, which are connected to the Wi-Fi network of the station board, can fully access the web server and receive the text package of the data in real time.
The weather data, including temperature, humidity, CO2, and TVOCs, are directly obtained from the sensor measurement, whereas the emergency rescue request signal is manually submitted by a user who is currently in the cave. The time interval for the data reading, processing, and web server refresh of each station ranges from 80 to 110 s. A detailed step for requesting an emergency rescue is shown in Figure 6.

2.3. Data Transfer Method

This study develops a data transfer method exhibiting a chain-like pattern to transmit the data from the inside to the outside of the cave, as shown in Figure 7. This method refers to the transmission of data from one station to another station in the cave through a GET-RECEIVE command series with a specific working time interval. The data from the different stations are then combined similarly to a single chain and subsequently transmitted to the next station. The data chain continues to grow depending on the number of stations.
In this work, the four working stations of the IoT system cooperate through the chain-like data transfer procedure presented in Figure 8. Station 1 is the innermost station located in the lower chamber of the cave and approximately 85 m from the entrance. Station 2 is in the upper chamber near the top of the connecting passage and 43 m from the first station. This station receives the data from Station 1 every 100 s. The next station is Station 3, located 31 m east of Station 2. It receives the combined data from Station 2 every 110 s. Station 3 ultimately transmits all of the data exhibiting a chain-like pattern to Station 4, located near the cave entrance, every 120 s.

2.4. Data Display and Database

This study utilized a web application to display the detected weather data and the emergency rescue request notification. Since this project consistently implemented open-source software, it unsurprisingly cost only USD 4 monthly for the web hosting service. Multiple coding packages and their related programming languages were necessary to develop the web application. This study involved the packages Apache2 version 2.0, Flask version 2.0.0, SQLAlchemy version 1.3, Plotly version 2.0, timeago version 2.5.10, and AJAX version 3.0.0, written in various coding languages, including C, HTML, CSS, Python, and JavaScript. The details of each package contribution are provided in Table 2.
MySQL version 8.0 is a database management system applied in this study that stores all detected weather information and emergency rescue request signals. It is a text-based type with fast performance, light weight, and high flexibility [35] that is seemingly practical across various industries for handling high volumes of data [36,37,38].
The working relationships between the coding packages and the database via the web application are presented in Figure 9. Starting with Apache2, it represents a web server operation hosting the MySQL database and the Python package library. Flask controls web access channels and utilizes the SQLAlchemy package to connect, commit, and receive all data from the database. Timeago is responsible for converting timestamp data into humanized text datetime data for display via the web application. Plotly receives the monitoring data from SQLAlchemy and subsequently generates a three-dimensional interactive map. Ultimately, AJAX produces steady updates of the displayed data on the Plotly.
Table 2. The programming languages and functions used in the web application are described in each package.
Table 2. The programming languages and functions used in the web application are described in each package.
Programming LanguagesPackagesDescription
CApache2
(Ubuntu operating system)
[39]		Open-source software: served as the main web service for hosting and operating the web application.
Python, HTML, CSS, JavaScriptFlask [40]Python package: functioned as a web framework, managing access channels for web applications.
PythonSQLAlchemy [41]Python package: used to connect, commit, and retrieve data from an SQL database hosted on Apache2.
Plotly [42]Python package: used to create a 3D interactive map displaying monitored data and the location of rescue requests.
timeago [43]Python package: used as a tool to transform the timestamp of the latest sensor-measured data into humanized text.
JavaScriptAJAX [44]JavaScript tool: for smoothly updating data represented on an interactive map.

3. Results and Discussion

The IoT weather monitoring and emergency detecting system installed in the Muang-On Cave began to run on 18 November 2021 and ended on 5 December 2021. The running time was approximately 8 h daily, starting at 8:00 am and ending at 4:00 pm, depending on the electricity availability in the cave. The data collection includes temperature, relative humidity, carbon dioxide, total volatile organic compounds, and emergency codes. The system was set to collect and upload each dataset to the Cloud every 80 to 110 s during its operating time, resulting in a total of 6073 datasets stored in the database. Including electronic parts, electrical wire, a plastic casing box, and tripod, the total cost of each station is estimated at USD 47. Thus, the IoT system consisting of four stations costs nearly USD 200. The outputs and details of the working modes are described in the following.

3.1. Weather Monitoring Display

The display of the weather conditions of the four stations on the user-interactive 3D model via the web server is shown in Figure 10. Other details are provided in the sections below.

3.1.1. Temperature and Humidity

Since the IoT system was tested in late November to early December, it fully experienced a transition from the wet season to the dry season in northern Thailand. This affects the dynamic temperature and humidity data collected in the cave. Daily average temperature and relative humidity data in the cave are presented in Figure 11 and Figure 12, respectively, with comparisons to the outdoor weather data of Chiang Mai in the same period.
The resulting comparisons indicate that the temperatures in the cave are more steady than the outdoor temperatures. In the wet season, the in-cave temperatures are relatively lower than the outdoor temperatures. On the other hand, it appears to be warmer in the cave than the outside during the dry season. However, Stations 3 and 4 show fluctuating temperatures that could be the effects of the outdoor weather. The degrees of temperature differences among the stations vary with the seasons shown in Figure 13. The dry season effectively impacts the discrete temperatures among the stations, especially between Stations 1 and 4.
The relative humidity in the cave is higher than in the outside environment, identical to the results of other studies [45,46,47]. The deepest position in the cave affects the highest relative humidity. These findings support the fact that the cave weather is significantly humid and actively induces the chemical weathering of the geological features in the cave, such as stalactites, stalagmites, and dissolved chambers. Moreover, Station 4, the nearest position to the entrance, shows a humidity-trending pattern similar to the outdoor weather.
The findings of this study certainly agree that the use of DHT22 temperature and humidity sensors is functional in confined space areas, especially caves. The sensors also have the potential for operating in outdoor environments.

3.1.2. CO2 and TVOCs

The two dominant parameters in cave studies are detected by the CCS811 sensors. Prior to the real-world testing, several sensors were run and calibrated in the laboratory. The operational issues, however, occurred at Stations 1 and 2 due to the sensor errors resulting in too high and too low value readings (Figure 14). Stations 3 and 4 provide reliable measured CO2 values ranging from 2000 to 4000 ppm and 400 to 500 ppm, respectively. Similarly to CO2, the TVOC values obtained from Stations 3 and 4 are practical, exhibiting a range of 200–600 ppb and 20–200 ppb, respectively.
Based on the CO2 data in the cave, the zone of approximately 11 m from the entrance into the cave can be sufficiently defined as a safe area since the CO2 level should not exceed 5000 ppm recommended by the Permissible Exposure Limit (PEL) from the Occupational Safety and Health Administration (OSHA) [48]. Meanwhile, the TVOC levels measured in the cave are equal to the safety levels of other indoor environments, for example, hospitals and libraries [49,50,51]. However, the sources and types of the volatile organic compounds might be dissimilar. More advanced research is highly recommended to predict the possible compound species and their availability in the caves.
According to previous works of Komarudin, et al. [52] and Varzaru, et al. [53], the eCO2 level measurement using the CCS811 sensors refers to an indirect CO2 and TVOC measuring method. It typically represents higher value readings compared to the direct methods of CO2 and TVOC measurement. Also, there are unpredictable discrepancies in the readings. Siagian and Fernando [54] reported unusually low CO2 levels (below 50 ppm) measured by the CCS811 sensor. The latest study by Pietraru, et al. [55] suggested that the humidity in the air directly impacts the effectiveness of MOX sensors. Thus, the CO2 and TVOC data errors that occurred in Stations 1 and 2 of this study probably highlight the challenges of the use of CCS811 sensors in extremely humid environments.
The CCS811 sensor errors that occurred at Stations 1 and 2 are possibly related to extremely high relative humidity in the air. Station 1 distinctly shows a very high humidity of 85%. Station 2, unsurprisingly, shows 90% humidity. It is also known that a humidity level exceeding 85% is considered a harsh environmental condition for sensor operation [56]. In addition, the specification of the CCS811 sensor provides a preferred humidity range of working conditions to be no greater than 95%. Stations 3 and 4 have no issues since their relative humidity ranges from 78 to 82%. This study highly agrees that the consideration of other CO2 and TVOC measuring methods, such as a nondispersive infrared sensor (NDIR sensor), should be an interesting alternative to simply avoid the errors.

3.2. Emergency Detection

Besides the weather monitoring, this study tested the rescue request commands, emergency detection, and notification of all IoT stations in the cave. Similarly to the working procedures of the weather data, each station detects a signal generated by a user manually every 80 to 110 s. The received signal is then encoded into text and interpreted as a rescue requirement. The location of the signal source can be determined through the chain-like text package and simultaneously displayed via the web application (Figure 15). If no emergency signal is generated, a default value of 1 is always replaced, which stands for no emergency currently needed.

4. Conclusions

The IoT-based weather monitoring and emergency request detecting system produced by this study is composed of four sensing stations, data transferring units, an open-source data management system, and a cloud database. Since the system was devised from a variety of low-cost electrical materials and open-source software, the total capital cost of the system production indicates a relatively low cost. The system testing was performed in the real-world cave environment for seventeen days and subsequently came with positive results. The detected weather data and emergency signals were reported and displayed on the web server at an approximately 95 s interval. The weather data monitored in the cave were applied for understanding its environmental setting and comparisons with the relevant outdoor data, leading to further weather research development. However, extremely high humidity in the cave effectively causes damage and erroneous readings of the CCS811 sensors. Applying other typed sensors, such as NDIR, to the system should be considered for further improvement.

Author Contributions

Conceptualization, K.P. and T.T.; Methodology, K.P. and T.T.; Software, K.P.; Validation, K.P.; Formal analysis, T.T.; Investigation, K.P.; Resources, T.T.; Writing—original draft preparation, K.P. and T.T.; Writing—review and editing, K.P. and T.T.; Visualization, K.P.; Supervision, T.T.; Project administration, T.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data are incorporated and represented within this study.

Acknowledgments

The authors thank the Sahakorn 2 Village organization for generously granting permission to install and test our monitoring and emergency system in the Muang-On Cave, Mae-On District, Chiang Mai Province, Thailand.

Conflicts of Interest

Author K.P. was employed by the Thoranee Krasib Co. Ltd. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. The three main components cooperating in the IoT-based system developed by this study include four individual stations for weather monitoring and emergency rescue request detecting (gray-colored filled boxes), a set of data transfer commands to transmit the data from the innermost (lack of internet Wi-Fi and cellular) to the outermost part of the cave passage (full-service access) resembling the chain-like pattern (pink-border open box), and a data display via a web application and an SQL database uploading via Cloud (blue-border open box).
Figure 1. The three main components cooperating in the IoT-based system developed by this study include four individual stations for weather monitoring and emergency rescue request detecting (gray-colored filled boxes), a set of data transfer commands to transmit the data from the innermost (lack of internet Wi-Fi and cellular) to the outermost part of the cave passage (full-service access) resembling the chain-like pattern (pink-border open box), and a data display via a web application and an SQL database uploading via Cloud (blue-border open box).
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Figure 2. The location of Muang-On Cave is in the north of Thailand, displayed on a 1:50,000-scaled geologic map adapted from the Department of Mineral Resources [27]. A set of contour lines and the WGS84 coordinate system exhibiting the 47N UTM zone adapted from the Royal Thai Survey Department [28]. The cave is located within the Permian limestone unit, as indicated by a red-filled circle.
Figure 2. The location of Muang-On Cave is in the north of Thailand, displayed on a 1:50,000-scaled geologic map adapted from the Department of Mineral Resources [27]. A set of contour lines and the WGS84 coordinate system exhibiting the 47N UTM zone adapted from the Royal Thai Survey Department [28]. The cave is located within the Permian limestone unit, as indicated by a red-filled circle.
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Figure 3. Three-dimensional cave model representing a comprehensive structure of the Muang-On Cave generated by LiDAR scanning techniques modified from Promneewat and Taksavasu (2024) [26]. The cave comprises one entrance, two horizontal chambers, and a vertical connecting passage.
Figure 3. Three-dimensional cave model representing a comprehensive structure of the Muang-On Cave generated by LiDAR scanning techniques modified from Promneewat and Taksavasu (2024) [26]. The cave comprises one entrance, two horizontal chambers, and a vertical connecting passage.
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Figure 4. The general dimensions of the ESP8266 microcontroller, the DHT22 temperature–humidity detecting sensor, and the CCS811 CO2 and TVOC detecting sensor are approximately 2.5 × 5 cm2, 1.5 × 3.8 cm2, and 1.5 × 2.3 cm2, respectively, compared to a quarter.
Figure 4. The general dimensions of the ESP8266 microcontroller, the DHT22 temperature–humidity detecting sensor, and the CCS811 CO2 and TVOC detecting sensor are approximately 2.5 × 5 cm2, 1.5 × 3.8 cm2, and 1.5 × 2.3 cm2, respectively, compared to a quarter.
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Figure 5. The system layout of each IoT station and its working diagram. The data obtained from the system through the microcontrollers and sensors, including station no. (S), temperature (A), humidity (B), CO2 (C), TVOCs (D), and emergency code (E), are present in the form of a text package separated by a slash displayed on a web server.
Figure 5. The system layout of each IoT station and its working diagram. The data obtained from the system through the microcontrollers and sensors, including station no. (S), temperature (A), humidity (B), CO2 (C), TVOCs (D), and emergency code (E), are present in the form of a text package separated by a slash displayed on a web server.
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Figure 6. A step-by-step process for requesting an emergency rescue in the cave that includes the following: (a) finding the nearest IoT station and connecting to the Wi-Fi network named ‘Emergency station’ via a smartphone or a mobile device, (b) accessing an HTML page by typing “emergency/” via the internet browser or by scanning a QR code found at the station, and (c) tapping on a HELP button to generate an emergency signal. The emergency board then takes an order of “yes”, encodes it as 2, and transmits these data to the station board. The emergency request consequently shows on the web application.
Figure 6. A step-by-step process for requesting an emergency rescue in the cave that includes the following: (a) finding the nearest IoT station and connecting to the Wi-Fi network named ‘Emergency station’ via a smartphone or a mobile device, (b) accessing an HTML page by typing “emergency/” via the internet browser or by scanning a QR code found at the station, and (c) tapping on a HELP button to generate an emergency signal. The emergency board then takes an order of “yes”, encodes it as 2, and transmits these data to the station board. The emergency request consequently shows on the web application.
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Figure 7. The chain-like data transferring method is developed by this study. The final text package data received by the last station at the cave entrance is ultimately uploaded to the Cloud database via the Wi-Fi internet connection from the cellular service.
Figure 7. The chain-like data transferring method is developed by this study. The final text package data received by the last station at the cave entrance is ultimately uploaded to the Cloud database via the Wi-Fi internet connection from the cellular service.
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Figure 8. Illustrations showing the Muang-On Cave model with the positions of the four IoT stations. (a) Mappings show the positions of each station with spacing intervals of 43, 31, and 11 m for the distance between Stations 1 and 2, Stations 2 and 3, and Stations 3 and 4, respectively. (b) The perspective view of the model exhibits a red survey line drawn from the innermost station to the cave entrance station. The line is a total of 85 m in length.
Figure 8. Illustrations showing the Muang-On Cave model with the positions of the four IoT stations. (a) Mappings show the positions of each station with spacing intervals of 43, 31, and 11 m for the distance between Stations 1 and 2, Stations 2 and 3, and Stations 3 and 4, respectively. (b) The perspective view of the model exhibits a red survey line drawn from the innermost station to the cave entrance station. The line is a total of 85 m in length.
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Figure 9. An illustration of the working relationships between the computational coding packages and the system database associated with the related outputs displayed on the web application.
Figure 9. An illustration of the working relationships between the computational coding packages and the system database associated with the related outputs displayed on the web application.
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Figure 10. The data display of the IoT system on the web application, showing the cave 3D model associated with the survey line, orientation, the weather data received from four stations, and the scales. The data include temperature (°C), relative humidity (%), CO2 (ppm), and TVOCs (ppb).
Figure 10. The data display of the IoT system on the web application, showing the cave 3D model associated with the survey line, orientation, the weather data received from four stations, and the scales. The data include temperature (°C), relative humidity (%), CO2 (ppm), and TVOCs (ppb).
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Figure 11. Average temperature comparisons between the inside and the outside of the cave. The temperatures in the cave are divided into four lines indicating the four IoT stations. The outdoor temperatures include the maximum, minimum, and average temperatures. In middle to late November, the temperatures of all stations in the cave show a steady trend compared to the temperatures of early December.
Figure 11. Average temperature comparisons between the inside and the outside of the cave. The temperatures in the cave are divided into four lines indicating the four IoT stations. The outdoor temperatures include the maximum, minimum, and average temperatures. In middle to late November, the temperatures of all stations in the cave show a steady trend compared to the temperatures of early December.
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Figure 12. Average relative humidity comparisons between the inside and the outside of the cave. Similarly to Figure 11, the relative humidity trends in the cave are divided into four lines indicating the four IoT stations. The humidity of all stations in the cave is relatively high compared to the outside.
Figure 12. Average relative humidity comparisons between the inside and the outside of the cave. Similarly to Figure 11, the relative humidity trends in the cave are divided into four lines indicating the four IoT stations. The humidity of all stations in the cave is relatively high compared to the outside.
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Figure 13. Daily temperature comparisons between the first day (left) and the last day (right) of the testing period. The first day likely indicates the wet season, whereas the final day indicates the dry season. The in-cave temperatures from all stations measured in the representative wet season are near steady and lower than the average outdoor temperature. The dry season, on the other hand, results in a rising trend of all-station temperatures in the cave. The weather is apparently warm in the cave during the dry season.
Figure 13. Daily temperature comparisons between the first day (left) and the last day (right) of the testing period. The first day likely indicates the wet season, whereas the final day indicates the dry season. The in-cave temperatures from all stations measured in the representative wet season are near steady and lower than the average outdoor temperature. The dry season, on the other hand, results in a rising trend of all-station temperatures in the cave. The weather is apparently warm in the cave during the dry season.
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Figure 14. Average CO2 (left) and TVOC (right) data obtained from the IoT weather monitoring system in the Muang-On Cave showing erroneous data on Stations 1 and 2 and valid data on Stations 3 and 4. The errors are believed to have resulted from the high humidity of the areas in the cave exhibiting too high and too low values.
Figure 14. Average CO2 (left) and TVOC (right) data obtained from the IoT weather monitoring system in the Muang-On Cave showing erroneous data on Stations 1 and 2 and valid data on Stations 3 and 4. The errors are believed to have resulted from the high humidity of the areas in the cave exhibiting too high and too low values.
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Figure 15. Emergency detection and rescue request notifications displaying on the 3D interactive map via the web application. The yellow color-highlighted marker indicates the station, in which the emergency signal was produced. For example, the figure shows that the signal was requested from Station 2.
Figure 15. Emergency detection and rescue request notifications displaying on the 3D interactive map via the web application. The yellow color-highlighted marker indicates the station, in which the emergency signal was produced. For example, the figure shows that the signal was requested from Station 2.
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Table 1. Detailed descriptions of the model, quantity, and cost of the microcontrollers and two types of sensors required for the production of each weather monitoring and emergency detecting station.
Table 1. Detailed descriptions of the model, quantity, and cost of the microcontrollers and two types of sensors required for the production of each weather monitoring and emergency detecting station.
TypesModelQuantityCost in Total
(USD)
MicrocontrollerESP8266315
Temperature and Humidity SensorDHT2213
Cabon dioxide and TVOC SensorCCS811114
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Promneewat, K.; Taksavasu, T. Weather Monitoring and Emergency IoT System in Muang-On Cave, Northern Thailand. Eng. Proc. 2024, 67, 7. https://doi.org/10.3390/engproc2024067007

AMA Style

Promneewat K, Taksavasu T. Weather Monitoring and Emergency IoT System in Muang-On Cave, Northern Thailand. Engineering Proceedings. 2024; 67(1):7. https://doi.org/10.3390/engproc2024067007

Chicago/Turabian Style

Promneewat, Khomchan, and Tadsuda Taksavasu. 2024. "Weather Monitoring and Emergency IoT System in Muang-On Cave, Northern Thailand" Engineering Proceedings 67, no. 1: 7. https://doi.org/10.3390/engproc2024067007

APA Style

Promneewat, K., & Taksavasu, T. (2024). Weather Monitoring and Emergency IoT System in Muang-On Cave, Northern Thailand. Engineering Proceedings, 67(1), 7. https://doi.org/10.3390/engproc2024067007

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