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  • Open Access

    ARTICLE

    A Hybrid Deep Learning Approach for PM2.5 Concentration Prediction in Smart Environmental Monitoring

    Minh Thanh Vo1, Anh H. Vo2, Huong Bui3, Tuong Le4,5,*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3029-3041, 2023, DOI:10.32604/iasc.2023.034636

    Abstract Nowadays, air pollution is a big environmental problem in developing countries. In this problem, particulate matter 2.5 (PM2.5) in the air is an air pollutant. When its concentration in the air is high in developing countries like Vietnam, it will harm everyone’s health. Accurate prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the citizen. This study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City, Vietnam. Firstly, this study analyzes the effects of variables on PM2.5 concentrations in Air Quality HCMC dataset. Only… More >

  • Open Access

    ARTICLE

    Sensor Location Optimisation Design Based on IoT and Geostatistics in Greenhouse

    Yang Liu1,3, Xiaoyu Liu2,3, Xiu Dai1, Guanglian Xun1, Ni Ren1,*, Rui Kang1,4, Xiaojuan Mao1

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1653-1663, 2022, DOI:10.32604/iasc.2022.017049

    Abstract Environmental parameters such as air temperature (T) and air relative humidity (RH) should be intensively monitored in a greenhouse in real time. In most cases, one set of sensors is installed in the centre of a greenhouse. However, as the microclimate of a greenhouse is always heterogeneous, the sensor installation location is crucial for practical cultivation. In this study, the T and RH monitoring performance of different sensors were compared. Two types of real-time environmental sensors (Air Temperature and Humidity sensor and Activity Monitoring sensor, referred as ATH and AM) were selected and calibrated by reliable non-real-time sensors (Honest Observer… More >

  • Open Access

    ARTICLE

    Self-Driving Algorithm and Location Estimation Method for Small Environmental Monitoring Robot in Underground Mines

    Heonmoo Kim, Yosoon Choi*

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 943-964, 2021, DOI:10.32604/cmes.2021.015300

    Abstract In underground mine environments where various hazards exist, such as tunnel collapse, toxic gases, the application of autonomous robots can improve the stability of exploration and efficiently perform repetitive exploratory operations. In this study, we developed a small autonomous driving robot for unmanned environmental monitoring in underground mines. The developed autonomous driving robot controls the steering according to the distance to the tunnel wall measured using the light detection and ranging sensor mounted on the robot to estimate its location by simultaneously considering the measured values of the inertial measurement unit and encoder sensors. In addition, the robot autonomously drives… More >

  • Open Access

    ARTICLE

    An Internet of Things Platform for Air Station Remote Sensing and Smart Monitoring

    David Corral-Plaza1, Juan Boubeta-Puig1, Guadalupe Ortiz1, Alfonso Garcia-de-Prado2,*

    Computer Systems Science and Engineering, Vol.35, No.1, pp. 5-12, 2020, DOI:10.32604/csse.2020.35.005

    Abstract Air pollution is currently receiving more attention by international governments and organizations. Nevertheless, current systems for air quality monitoring lack essential requirements which are key in order to be effective concerning users’ access to the information and efficient regarding real-time monitoring and notification. This paper presents an Internet of Things platform for air station remote sensing and smart monitoring that combines Big Data and Cloud Computing paradigms to process and correlate air pollutant concentrations coming from multiple remote stations, as well as to trigger automatic and personalized alerts when a health risk for their particular context is detected. This platform… More >

  • Open Access

    ARTICLE

    A Multi-Agent System for Environmental Monitoring Using Boolean Networks and Reinforcement Learning

    Hanzhong Zheng1, Dejie Shi2,*

    Journal of Cyber Security, Vol.2, No.2, pp. 85-96, 2020, DOI:10.32604/jcs.2020.010086

    Abstract Distributed wireless sensor networks have been shown to be effective for environmental monitoring tasks, in which multiple sensors are deployed in a wide range of the environments to collect information or monitor a particular event, Wireless sensor networks, consisting of a large number of interacting sensors, have been successful in a variety of applications where they are able to share information using different transmission protocols through the communication network. However, the irregular and dynamic environment requires traditional wireless sensor networks to have frequent communications to exchange the most recent information, which can easily generate high communication cost through the collaborative… More >

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