University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 13 3 2022 09 23 An Intelligent Energy-Efficient Firefighting Strategy in Mobile WSNs An Intelligent Energy-Efficient Firefighting Strategy in Mobile WSNs 37 54 25936 10.22108/isee.2021.124683.1406 FA Farzad H. Panahi Department of Electrical Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran Fereidoun H. Panahi Department of Electrical Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran Journal Article 2020 09 03 With the increased scope of fires and the widespread destruction of the environment and densely populated urban areas in recent years, researchers have investigated the adoption of rapid and effective firefighting solutions, particularly those based on wireless sensor networks (WSNs). In fact, by evaluating various statistical data and developing a new model of sensors, equipment, and intelligent technologies in a fire sensor network, an effective step toward controlling frequent fires on a wide scale and reducing environmental damage can be taken. In the proposed model, the mobile sensors or firefighting robots based on a fuzzy Q-learning (FQL) algorithm and using two learning strategies in the sensor network, namely partial and perfect, could be used to  surround fire in firefighting operations. We also formulate a sensor mode selection strategy as an optimization problem to maximize the lifetime of the energy harvesting-enabled WSN. Furthermore, we determine optimal upper and lower bounds for the number of active sensors in the fire detection system, guaranteeing that the target detection and false alarm probabilities are achieved. Computer simulations show that using such a solution in the optimal selection of moving sensors and determining the moving trajectory in rapid firefighting is effective. With the increased scope of fires and the widespread destruction of the environment and densely populated urban areas in recent years, researchers have investigated the adoption of rapid and effective firefighting solutions, particularly those based on wireless sensor networks (WSNs). In fact, by evaluating various statistical data and developing a new model of sensors, equipment, and intelligent technologies in a fire sensor network, an effective step toward controlling frequent fires on a wide scale and reducing environmental damage can be taken. In the proposed model, the mobile sensors or firefighting robots based on a fuzzy Q-learning (FQL) algorithm and using two learning strategies in the sensor network, namely partial and perfect, could be used to  surround fire in firefighting operations. We also formulate a sensor mode selection strategy as an optimization problem to maximize the lifetime of the energy harvesting-enabled WSN. Furthermore, we determine optimal upper and lower bounds for the number of active sensors in the fire detection system, guaranteeing that the target detection and false alarm probabilities are achieved. Computer simulations show that using such a solution in the optimal selection of moving sensors and determining the moving trajectory in rapid firefighting is effective. https://isee.ui.ac.ir/article_25936_3c7667177efa2c2bec76ae85fc480dcf.pdf