# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 1 Introduction  
Forests are vital for ecological balance, climate regulation, and resource provision, making their protection essential [1]. Although forest fires are a natural part of some ecosystems, they increasingly pose serious threats to both the environment and human life, especially under dry conditions, high temperatures, and strong winds [2]. Wireless sensor networks are a key technology for early forest fire detection, offering real-time monitoring with high accuracy and minimal human input [3]. By sensing parameters like temperature, humidity, and smoke, they enable timely responses that help prevent small fires from becoming large-scale disasters, reducing environmental, economic, and human risks [4]. In this context, smoke detection identifies airborne particulate matter (PM) and combustion gases, such as carbon monoxide (CO), which are released during the early stages of a fire. Optical and ionization sensors are commonly used to detectsuch components based on light scattering or changes in electrical current. These sensing methods play a crucial role in improving fire detection accuracy and responsiveness [5]. WSNs, typically composed of thousands of low-cost, low-power smart nodes, can be deployed in various topologies such as star, tree, and mesh architectures [6; 7; 8]. They have significantly advanced wildfire detection and are widely applied in areas including environmental monitoring, target tracking, security, military defense, and healthcare [9; 10]. A key advantage of WSNs lies in their ability to integrate multiple energy sources, providing a stable and continuous power supply essential for sustained operation [11]. The sensor node is a core component of WSNs due to its critical role in establishing interconnectivity and enabling data acquisition. It is responsible for collecting, monitoring, and transmitting environmental data. Modern sensors are capable of measuring both dynamic and static variables, such as humidity, land gradient, wind direction and speed, and smoke presence [12]. By utilizing sensor-based systems, it becomes possible to detect fires in their early stages and support informed decision-making for effective fire suppression. ZigBee, based on the IEEE 802.15.4 standard [13], is commonly used in remote forest areas due to its low power consumption, reduced latency, and simplicity. It supports communication over distances of 10 to 100 m at a data rate of 250 Kbit/s, with extended range through mesh network topology [7]. This mesh topology is particularly beneficial for wildfire detection systems, allowing nodes to relay data through others, ensuring reliable communication even in dense forests [14]. To extend connectivity and integrate the local ZigBee sensor network with the internet, GSM technology is essential [15]. It enables communication between the sink node and the control center, offering broader coverage and internet access when available [16]. In wildfire detection systems, a central GSM node collects fire location data from sensor nodes and transmits it via SMS to the control center. With appropriate routing techniques, a single GSM node can connect to over 1000 nodes, enabling comprehensive monitoring [17]. GSM modules offer excellent data rates and very long communication ranges. However, obtaining reliable GSM coverage is not always possible in forested areas. Additionally, signal quality can be affected by various environmental factors such as temperature, humidity, atmospheric pressure, and other air conditioning effects [18]. Artificial intelligence (AI) is increasingly used in wildfire detection systems to improve accuracy and response times. AI algorithms, including machine learning, analyze real-time sensor data to identify fire indicators such as temperature spikes and smoke, enabling faster detection and more efficient resource deployment [3]. The proposed fire detection system utilizes a hybrid star/circle topology, combining both centralized and mesh network configurations.