# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 2 Literature Review  
The detection of forest fires is a critical problem that has garnered significant attention in recent years due to its high environmental and economic impact, leading to the development of various technologies to overcome the limitations of traditional systems. WSNs are now central to modern wildfire detection, addressing the inefficiencies and delays of traditional methods such as towers, cameras, and satellites, which are limited by weather and image acquisition frequency [9]. Over the years, WSNs have evolved to address these challenges. Early studies focused on integrating WSNs with environmental sensors, including temperature, humidity, and smoke detection sensors, to create reliable, low-cost, and energy-efficient monitoring systems. In [19], [PERSON] discussed the significant challenges in routing for energy efficiency in WSNs. [PERSON] et al. [20] presented a ZigBee-based system to enhance the reliability of fire detection while reducing delays and energy consumption. [PERSON] et al. [21] emphasizedthe advantages of WSNs in real-time fire prediction, as their system provides superior spatial and temporal resolution compared to satellite-based approaches. The integration of artificial intelligence (AI) and the Internet of Things (IoT) has significantly enhanced wildfire detection systems by leveraging real-time environmental data and complex algorithms for more accurate and timely predictions. [PERSON] et al. [22] pioneered AI-based fire detection systems, using AI to distinguish real from false alarms in residential fire systems. [PERSON] et al. [23] developed an intelligent WSN for early detection by analyzing real-time environmental parameters like temperature and smoke. Recent advancements focus on combining machine learning (ML) and deep learning (DL) with WSNs for real-time wildfire prediction. For instance, [PERSON] et al. [24] proposed a robust ML framework, leveraging AI for fire events in a variety of environments. [PERSON] et al. [25] applied information fusion techniques to enhance detection accuracy, achieving 100% detection in controlled settings and emphasizing energy efficiency for real-world applications. Recent works by [PERSON] et al. and [PERSON] et al. explored using machine learning to improve fire detection accuracy by analyzing sensor data for early prediction. These studies focus on machine learning models to detect fire hazards early [26; 27]. [PERSON] et al. [28] incorporated IoT-based WSN systems with advanced algorithms that fuse environmental data in real time, optimizing power consumption and detection accuracy. In [29], [PERSON] et al. incorporated deep learning to refine wildfire detection by identifying complex patterns in sensor data and images, making the system adaptable to diverse environmental conditions. In [30], [PERSON] et al. developed a hybrid AI model combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to predict wildfires, significantly reducing false negatives in detection systems. AI models have been enhanced by hybrid IoT-AI systems combining real-time sensor data and satellite imagery. In [31], [PERSON] et al. integrated smoke and gas sensors with deep learning for fire classification, improving the monitoring of large forest areas by reducing false alarms and enhancing detection accuracy. The choice of network topology is crucial for optimizing WSN performance in wildfire detection systems. Various topologies, such as ring, star, mesh, and tree, have been explored, each with distinct benefits and limitations. [PERSON] et al. [32] highlighted the importance of coverage in WSNs and the trade-offs between different deployment schemes. [PERSON] et al. [33] reviewed energy-efficient topology control techniques to enhance data transmission and network efficiency in wireless ad hoc networks. To address the limitations of traditional WSN configurations, some studies have proposed hybrid and dynamic topologies. For instance, [PERSON] et al.