2026-06-10T23:58:38.060Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:8706 2022-03-24T00:18:02Z 2

SMART WIRELESS HEALTH CARE SYSTEM USING GRAPH LSTM POLLUTION PREDICTION AND DRAGONFLY NODE LOCALIZATION 2022 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/8706 https://www.sciencedirect.com/science/article/abs/pii/S2210537922000506 N. Bacanin M. Sarac N. Budimirovic M. Zivkovic A. AlZubi A. Bashir Wireless sensing networks (WSNs) have been applied on various research applications such as monitoring health of humans, targets tracking, natural resources investigation, air quality prediction, water pollution prediction and radiation pollution. The challenges on predicting these applications are still exist. In order to maintain the healthy society with sustainable growth, suitable monitoring systems is necessary. With the advancement of Internet of Things and modern sensors, the environmental monitoring systems are become smart monitoring system. These wireless sensors are scattered around the environmental locations and placed. The localization of placing the sensor at correct place will reduce the redundancy of the sensing environment and cost. More nodes are placed at the area that has more pollutant. Accurate node sensor placing on the needed area will reduce the cost of sensors and increase the prediction accuracy. This helps to keep our health safe by selecting pollution less environment. Hence, this article focuses on introducing the deep learning algorithm called Graph Long Short-Term Memory (GLSTM) neural network to predict the air quality characteristics. Next, the evolutionary algorithm called Dragon fly optimizer has been used to localize the node based on the prediction. Deep evolutionary based algorithms will improve the air pollutant prediction and node localization sensor cost. article 10.1016/j.suscom.2022.100711 1 8 2210-5379 Sustainable Computing: Informatics and Systems