Forests have an important role in environmental preservation and maintenance. The primary threat is forest fires, which have disastrous repercussions. As a result, it is critical to identify and extinguish a fire before it spreads and destroys resources. To that end, we propose a forest fire detection and fighting mechanism using wireless sensor and actor networks (WSANs). Temperature sensors are utilized to detect fires, and actors (robots) are employed to extinguish them. Sensors and robots are distributed at random throughout the forest, forming clusters. Clustering, sleep/active scheduling for the sensors, and energy harvesting (EH)/moving modes for the robots, are used to extend and maximize the sensors/robots lifetime in the WSAN. In such a network, robots should move to the fire site as quickly as possible. To do this, we further propose a robot routing mechanism that focuses on determining the shortest path for each firefighting robot. In particular, each firefighting robot equipped with on-board processing uses a fuzzy $Q$ -learning (FQL)-based trajectory mechanism to learn the shortest path to the fire zone in the least amount of time. Simulations are conducted to demonstrate the benefits of employing the proposed framework for rapid and effective fire response. When compared to the traditional $Q$ -learning, the total approaching rate (a measure of how quickly the firefighting robots can reach the fire) to the fire spot is greater when utilizing the proposed FQL-based strategy.

中文翻译：

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一种用于无线传感器和行动者网络消防的智能路径规划机制

森林在环境保护和维护方面具有重要作用。主要威胁是森林大火，它会造成灾难性的后果。因此，在火势蔓延和破坏资源之前识别并扑灭火势至关重要。为此，我们提出了一种使用无线传感器和参与者网络 (WSAN) 的森林火灾探测和扑救机制。温度传感器用于检测火灾，并使用演员（机器人）来扑灭火灾。传感器和机器人随机分布在整个森林中，形成集群。传感器的集群、睡眠/活动调度以及机器人的能量收集 (EH)/移动模式，用于延长和最大化传感器/机器人在 WSAN 中的寿命。在这样的网络中，机器人应该尽快移动到火灾现场。去做这个，我们进一步提出了一种机器人路由机制，专注于确定每个消防机器人的最短路径。特别是，每个配备车载处理的消防机器人都使用模糊 $Q$ - 基于学习 (FQL) 的轨迹机制，可以在最短的时间内学习到火区的最短路径。进行了模拟，以证明采用所提出的框架进行快速有效的火灾响应的好处。与传统相比 $Q$ -学习，当使用所提出的基于 FQL 的策略时，到火点的总接近率（衡量消防机器人到达火场的速度）更大。