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ENVIRONMENTAL SCADA SYSTEM USING MOBILE ROBOTS
Abstract
Indoor air pollution monitoring is an important activity toward a more clean air for indoor environments as most of people spend a lot more time in such environments (as e.g. at home, at their workplace or at kindergarten/school/university). The quality of indoor air can have a strong impact on human health, due to longer exposure, especially when ventilation is poor. So far, some solutions were proposed for solving this problem, starting from simple stationary sensors, to more complicated mobile sensing networks and single or multi-robots systems. Intelligent robots either alone or in cooperation with other robots can collect environmental data from specific indoor environments in order to build a map of indoor air pollution, to analyze the air pollution degree or to perform a more detailed analysis of air pollution in the monitored indoor areas and to identify and propose some air pollution reduction measures to be taken by the decision factors. In this paper we propose an indoor air pollution monitoring system, based on the Internet of Things (IoT) concept that can monitor an indoor environment such as a workplace or a living place(residence) using intelligent mobile robots equipped with air pollution monitoring sensors. Robots follow the same path that people follow and offer a more accurate image of the pollution that affects people every day. The robots are equipped with GPS and WiFi data connection and send data using TCP/IP protocol to an environmental SCADA system that records and builds pollution maps of the place that is monitored. Based on these maps, the SCADA system can automatically start the ventilation system or forbid access in areas where pollution is too high.
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Sun S., Zheng X., Villalba-Diez J., Ordieres-Meré J., Indoor air quality data monitoring system: long-term monitoring benefits, Sensors, vol. 19, 4157, 2019.
Saini J., Dutta M., Marques G., A comprehensive review on indoor air quality monitoring systems for enhanced public health, Sustainable Environmental Research, vol. 30/issue 3, pp. 1-12, 2020. DOI: 10.1186/s42834-020-0047-y
Postolache, O.A., Pereira, J.M.D., Girao, P.M.B.S., Smart sensors network for air quality monitoring applications, IEEE Trans. Instrum. Meas., vol. 58, pp. 3253–3262, 2009.
Salman N., Kemp A.H., Khan A., Noakes C.J., Real time wireless sensor network (WSN) based indoor air quality monitoring system, IFAC PapersOnLine, vol. 52, pp. 324-327, 2019.
Neumann P.P., Hirschberger P., Baurzhan Z., Tiebe C., Hofmann M., Hüllmann D., Bartholmai M., Indoor air quality monitoring using flying nanobots: design and experimental study, Proceedings of the IEEE ISOEN, Fukuoka, Japan, 2019.
Kim, J.; Chu, C.; Shin, S., ISSAQ, An integrated sensing systems for real-time indoor air quality monitoring, IEEE Sens. Journal, vol. 14, pp. 4230–4244, 2014.
Saad S.M., Andrew A.M., Shakaff A.Y.M., Saad A.R.M., Yusof A.M.M, Kamarudin, Zakaria A., Classifying sources influencing indoor air quality using artificial neural networks, Sensors, vol. 15, pp. 11665-11684, 2015.
Fisk W.J., The ventilation problem in schools: literature review, Indoor Air, vol. 27, pp. 1039-51, 2017.
L. Tai and M. Liu, A robot exploration strategy based on Q-learning network, The IEEE International Conference on Real-time Computing and Robotics (RCAR), Angkor Wat, pp. 57-62, DOI: 10.1109/RCAR.2016.7784001, 2016.
Bahare K., Frank L., Hamidreza M., Ali K, and Mohammad B., Reinforcement Q-learning for optimal tracking control of linear discrete-time systems with unknown dynamics. Automatica, 50(4):1167–1175, 2014.
Monica A.R., Autonomous Navigation by Reinforcement Learning, Universitat Jaume I12071 Castellon de la Plana, Spain, 2016.
EPA: https://www.epa.gov/indoor-air-quality-iaq
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