Due to climate change and the rise in international transportation, there is an emerging potential for outbreaks of mosquito-borne diseases such as malaria, dengue, and chikungunya. Consequently, the rapid detection of vector mosquito species, including those in the Aedes, Anopheles, and Culex genera, is crucial for effective vector control. Currently, mosquito population monitoring is manually conducted by experts, consuming significant time and labor, especially during peak seasons where it can take at least seven days. To address this challenge, we introduce an automated mosquito monitoring system designed for wild environments. Our method is threefold: It includes an imaging trap device for the automatic collection of mosquito data, the training of deep-learning models for mosquito identification, and an integrated management system to oversee multiple trap devices situated in various locations. Using the well-known Faster-RCNN detector with a ResNet50 backbone, we’ve achieved mAP (@IoU=0.50) of up to 81.63% in detecting Aedes albopictus, Anopheles spp., and Culex pipiens. As we continue our research, our goal is to gather more data from diverse regions. This not only aims to improve our model’s ability to detect different species but also to enhance environmental monitoring capabilities by incorporating gas sensors.

This study developed an IoT-based agricultural well control system and demonstrated it through a control system in the current state of domestic open-field smart farms where systematic management is insufficient due to the difficulty of collecting accurate data such as agricultural water intake and usage. As a result, it was possible to derive the optimal control system test results according to the watering conditions for each schedule by conducting automatic control focusing on periods when there is no precipitation. This means that irrigation can be carried out smoothly during the time when irrigation is needed to improve crop quality and secure farm income, and as a result, it is possible to systematically manage and operate among demonstration farms connected to irrigation, resulting in a shortage of agricultural water and a resolution of supply imbalances.

PURPOSES : The number of snowfall and the amount of snowfall are gradually increasing, and due to the characteristics of Seoul, which has a lot of traffic, it is difficult to respond quickly with a snow removal method that relies on snow removal vehicles. Therefore, it is necessary to develop an IoT based eco-friendly snow removal system that can respond to unexpected heavy snow in winter. In this study, the low temperature operation and snow removal performance of the IoT road condition snow removal detector and the snow removal system using CNT and PCM are evaluated in the climatic environment chamber. METHODS : To make artificial snow, it consists of an climatic environment chamber that can simulate a low temperature environment and equipment that can supply compressed air and cold water. Depending on the usage characteristics of the climatic environment chamber, use an air-water type snow maker. In order to make artificial snow, wet temperature, which takes into account the actual air temperature and the amount of moisture in the air, acts as the most important variable and is suitable for making snow, below –1.5 ℃. The lower the water temperature, the easier it is to freeze, so the water source was continuously supplied at 0 ℃ to 4 ℃. One of the two different pipes is connected to the water tank to supply water, and the other pipe is connected to the compressor to supply high-pressure air. Water is dispersed by compressed air in the form of many small droplets. The sprayed microscopic water particles freeze quickly in the low temperature environmental climatic chamber air and naturally fall to the floor, forming snow. Based on the KS C IEC 60068-2-1 cold resistance test standard, an integrated environmental test procedure was prepared to apply to IoT-based snow removal systems and performance evaluation was performed accordingly. The IoT based eco-friendly snow removal system is needed to in winter, so visual check and inspect the operation at the climatic chamber before setting up it to the actual site. In addition, grid type equipment was manufactured for consistent and reliable snow removal performance evaluation under controlled environmental conditions. RESULTS : The IoT-based eco-friendly snow removal system normally carried out the task of acquiring data and images without damaging the appearance or freezing in a low temperature environment. It showed clear snow removal performance in areas where PCM and CNT heating technology were applied to the concrete slab. This experiment shows that normal snow removal tasks can be carried out in low temperature environments in winter. CONCLUSIONS : The integrated environmental test procedures and grid type evaluation equipment are applied to low temperature operation and snow removal performance evaluation of snow removal systems. In the climatic environment chamber, where low temperature environments can be simulated, artificial snow is created regardless of the season to derive quantitative experimental results on snow removal performance. PCM and CNT heating technology showed high snow removal performance. The system is expected to be applied to road site situations to preemptively respond to unexpected heavy snow in winter.

PURPOSES : In this study, an existing speed-controlled Marshall stability tester was systemized as an Internet of Things(IoT) system. The Marshall stability test data were transmitted to the cloud in real-time, and an IoT optional-controlled board capable of additional load and displacement control was proposed. METHODS : The IoT systemization was built based on an improvement of an IoT height measuring system, the re-verification of standard samples for comparative analysis, and the development of a wireless IG-IoT board. The developed wireless Induk-GeoTS(IG)-IoT board was compared with existing commercial data logger using displacement- and load- calibration equipment. After the conformity of the developed wireless IoT board was established, a urethane standard sample was reproduced and verified using the recipe presented in a previous study to conduct a round-robin test. In addition, the adequacy of the speed, load, and displacement control tests for the optional-controlled characteristics was verified. the round-robin test for the Marshall stability and deformation strength and the comparative test of indirect tensile strength with the existing Marshall tester were performed using the re-verified standard sample. RESULTS : The improved two-point IoT height measurement system reduced the average relative error by 2.11% relative to the one-point measurement. From the re-verification results of the regenerated urethane standard sample, it was suitable with relative error of 3.65% in the loading elastic modulus and 4.07% in the unloading elastic modulus, compared to the existing standard sample. From the comparative analysis of the developed wireless IG-IoT board and existing commercial data logger, it was confirmed that the wireless IoT board could be reliably used, based on the average relative error of the wireless IoT board, 0.64% and that of the data logger, 3.79% in terms of the displacement(flow value) and an average relative error of 0.78% for the wireless IoT board and 0.79% for the data logger in terms of the load(stability). By analyzing the optional-controlled characteristics, it was found that the Marshall stability speed control conditions were satisfied based on the error results, with an average relative value of 2.96% under deformation strength test condition of 30mm/min, 3.23% under the indirect tensile strength test condition of 50mm/min, and 2.6% under the Marshall stability test condition of 50.8mm/min. It was also found that proper control characteristics were obtained, with an average relative error of 0.72% within the experimental load range in the load control conditions, and an average relative error of 2.4% in the experimental displacement range in the displacement control conditions. The results from the round-robin Marshall stability and deformation strength testing to verify the applicability of the IoT optional-controlled board show that the data were reliable based on the 3σ quality control method. In addition, by comparing the results of the indirect tensile strength tests, the usability of the wireless IG-IoT board was verified, with an average relative error of 0.96%. CONCLUSIONS : The IoT height measuring system was improved, and a wireless IG-IoT board that can transmit test data to a cloud platform was developed. The usability of the developed wireless IoT board was verified by round-robin testing using a re-verified urethane specimen. The IG-IoT optional-controlled board extends the verified wireless IG-IoT board, it was developed and validated for not only the existing speed control, but also for load, and displacement control.

In this study, the effect of improving indoor air quality according to the installation of plants was evaluated in classrooms where students spend much time. The purpose was to prepare sustainable and eco-friendly measures to improve the indoor air quality of school classrooms. A middle school in Bucheon was selected as an experiment subject, and IAQ monitoring equipment based on IoT was installed to monitor indoor air quality. After measuring the basic background concentration, plants and air purifiers were installed and the effects of improving indoor air quality using plants and air purifiers were evaluated based on the collected big data. As a result of evaluating the effects of indoor air quality improvement on the installation of plants and air purifiers, the reduction rates of PM10 and PM2.5 in descending order were plant- and air purifier- installed classes, air purifier-installed classes, and plant-installed classes. CO2 levels were reduced in the classrooms with only plants, and increased in two classrooms with air purifiers. The increase in CO2 concentration in the classrooms with plants and air purifiers was lower than in those with only air purifiers.

Now days, customer’s health awareness is of extreme significance. Food can become contaminated at any point during production, preparation and distribution. Therefore, it is of key importance for the perishable food supply chain to monitor the food quality and safety. Traceability system offers complete food information and therefore, it guarantees food quality and safety. The current study postulates a low cost IoT-based traceability system that utilized RFID and smartphone-based sensors. The RFID handheld reader based on smartphone is utilized to track and trace the product. In addition the smartphone-based sensor is used to measure temperature, humidity, and location during storage and transportation. The proposed system was verified for kimchi supply chain in Korea, and revealed significant benefits to managers as well as customers by providing the real-time product information, location as well as complete temperature and humidity history. The results displayed that compared to the traditional methods, the proposed system is capable of tracking products as well as processing an immense input of data efficiently and effectively.

We developed a small sensor observation system (SSOS) at a relatively low cost to observe the atmospheric boundary layer. The accuracy of the SSOS sensor was compared with that of the automatic weather system (AWS) and meteorological tower at the Korea Meteorological Administration (KMA). Comparisons between SSOS sensors and KMA sensors were carried out by dividing into ground and lower atmosphere. As a result of comparing the raw data of the SSOS sensor with the raw data of AWS and the observation tower by applying the root-mean-square-error to the error, the corresponding values were within the error tolerance range (KMA meteorological reference point: humidity ± 5%, atmospheric pressure ± 0.5 hPa, temperature ± 0.5℃. In the case of humidity, even if the altitude changed, it tends to be underestimated. In the case of temperature, when the altitude rose to 40 m above the ground, the value changed from underestimation to overestimation. However, it can be confirmed that the errors are within the KMA’s permissible range after correction.

Based on IoT (Internet of Things) where its concept contains providing convenience to human life by connecting every objects around us, numerous projects have been done in civil engineering field. This paper has conducted the basic study on adopting IoT sensing technology and network system into harbor.

In this study, plant pipeline diagnosis system based on IoT(Internet of Things)was developed. The system consists of three parts : sensor system, omnicube system and pipeline safety assessment solutions. The plant pipeline diagnosis system was constructed in plant pipeline and was validated field applicability. Plant pipeline diagnosis system based on IoT is appropriate for plant pipeline safety monitoring.

The importance of plant pipe rack safety management has been increased. In this study, a plant safety management system based on IoT(Internet of Things) was constructed in Yeosu Industrial Complex. The purpose of this study is to investigate the structural characteristics performance and structural health monitoring of pipe rack using measured data

In this study, a plant safety management system based on IoT(Internet of Things) was developed. The system consists of three parts : smart sensing technology, wireless networking technology and smart plant safety services. The safety management system was constructed in Yeosu Industrial Complex and was validated field applicability in plant. The IoT plant safety management system is appropriate for plant industrial structures.