This study analyzed IoT-based indoor air quality monitoring data in a cooking room at a high school in Seoul. As a result of measuring the type and concentration change of cooking fumes generated during roasting, frying, and stir-fry, each cooking method showed a different pattern. Some cooking fumes were observed high during the distribution process, not during cooking, and it is necessary to observe and control indoor air quality during the entire process of cooking, storage, and distribution as well as various elements of cooking fumes. Through these results, we propose the addition of an IoT-based real-time indoor air quality monitoring system and ventilation facilities linked to it.

본 논문에서는 건설 현장 관리의 과제를 다루고 IoT 기술 활용을 위한 기술 적용에 대해 정리하였다. 도로 포장 장비의 유휴 시간을 모니터링하는 IoT 장치를 설계 및 구현하여 효율적인 장비 관리 시스템을 개발하는 것을 목표한다. 또한, 본 연구에서는 통신방식 선 정, 사용자 친화적인 플랫폼 설계, 데이터 수집 및 분석을 위한 진동센서 기반 IoT 디바이스 개발을 통한 실시간 관리에 중점을 두고 있다. 플랫폼을 통해 공사현황을 실시간으로 모니터링하고 장비 유휴시간을 관리해 효율성을 높일 수 있으며, IoT 디바이스는 90% 이 상의 데이터 정확도를 보장한다. 현장 테스트를 통해 장비 사용 추적 효과가 확인되어 보다 효율적인 건설 관리에 기여하고자 한다.

The objective of this study is to analyze the indoor air quality of multi-use facilities using an IoT-based monitoring and control system. Thise study aims to identify effective management strategies and propose policy improvements. This research focused on 50 multi-use facilities, including daycare centers, medical centers, and libraries. Data on PM10, PM2.5, CO2, temperature, and humidity were collected 24 hours a day from June 2019 to April 2020. The analysis included variations in indoor air quality by season, hour, and day of the week (including both weekdays and weekends). Additionally, ways to utilize IoT monitoring systems using big data were propsed. The reliability analysis of the IoT monitoring network showed an accuracy of 81.0% for PM10 and 76.1% for PM2.5. Indoor air quality varied significantly by season, with higher particulate matter levels in winter and spring, and slightly higher levels on weekends compared to weekdays. There was a positive correlation found between outdoor and indoor pollutant levels. Indoor air quality management in multi-use facilities requires season-specific strategies, particularly during the winter and spring. Furhtermore, enhanced management is necessary during weekends due to higher pollutant levels.

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.

사회기반 시설물의 노후화에 대응해 이상 징후를 파악하고 유지보수를 위한 최적의 의사결정을 내리기 위해선 디지털 기반 SOC 시설물 유지관리 시스템의 개발이 필수적인데, 디지털 SOC 시스템은 장기간 구조물 계측을 위한 IoT 센서 시스템과 축적 데이터 처 리를 위한 클라우드 컴퓨팅 기술을 요구한다. 본 연구에서는 구조물의 다물리량을 장기간 측정할 수 있는 IoT센서와 클라우드 컴퓨팅 을 위한 서버 시스템을 개발하였다. 개발 IoT센서는 총 3축 가속도 및 3채널의 변형률 측정이 가능하고 24비트의 높은 해상도로 정밀 한 데이터 수집을 수행한다. 또한 저전력 LTE-CAT M1 통신을 통해 데이터를 실시간으로 서버에 전송하여 별도의 중계기가 필요 없 는 장점이 있다. 개발된 클라우드 서버는 센서로부터 다물리량 데이터를 수신하고 가속도, 변형률 기반 변위 융합 알고리즘을 내장하 여 센서에서의 연산 없이 고성능 연산을 수행한다. 제안 방법의 검증은 2개소의 실제 교량에서 변위계와의 계측 결과 비교, 장기간 운 영 테스트를 통해 이뤄졌다.

As indoor activities continue to increase, the importance of indoor air is emphasized. Moreover, children's activities are emphasized as being vulnerable. In this study, vocal organic compounds (VOCs) and CO2 in the indoor air were analyzed among children aged 4 to 7 years attending day care centers in Seoul. In the case of VOCs, the average concentration measured during a period of 24 hours in an asthmatic home was 143.9 (μg/m3). The average concentration measured during a period of 24 hours in the asthma and rhinitis home was 146.7 (μg/m3). In CO2, the average concentration measured during a period of 24 hours in the asthmatic home was 665.9 (ppm). The average concentration measured during a period of 24 hours in the asthma and rhinitis home was 695.9 (ppm). In this study, asthma symptoms increased as the concentration of indoor pollutants increased. Exposure of VOCs (μg/ m3) and CO2 (ppm) among environmental factors shows that respiratory symptoms such as asthma can be induced.

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.

The main objective of this study was to assess the applicability of IoT (Internet of Things)-based flood management under climate change by developing intelligent water level monitoring platform based on IoT. In this study, Arduino Uno was selected as the development board, which is an open-source electronic platform. Arduino Uno was designed to connect the ultrasonic sensor, temperature sensor, and data logger shield for implementing IoT. Arduino IDE (Integrated Development Environment) was selected as the Arduino software and used to develop the intelligent algorithm to measure and calibrate the real-time water level automatically. The intelligent water level monitoring platform consists of water level measurement, temperature calibration, data calibration, stage-discharge relationship, and data logger algorithms. Water level measurement and temperature calibration algorithm corrected the bias inherent in the ultrasonic sensor. Data calibration algorithm analyzed and corrected the outliers during the measurement process. The verification of the intelligent water level measurement algorithm was performed by comparing water levels using the tape and ultrasonic sensor, which was generated by measuring water levels at regular intervals up to the maximum level. The statistics of the slope of the regression line and   were 1.00 and 0.99, respectively which were considered acceptable. The error was 0.0575 cm. The verification of data calibration algorithm was performed by analyzing water levels containing all error codes in a time series graph. The intelligent platform developed in this study may contribute to the public IoT service, which is applicable to intelligent flood management under climate change.

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