# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 1 Introduction
This approach merges the energy-efficient data collection of the star topology with the robustness and redundancy of the mesh network, optimizing performance in dynamic wildfire environments. In wildfire-prone areas, this topology offers enhanced scalability, reliability, and efficiency. Additionally, the use of ZigBee for communication ensures reliable, low-power transmission while maintaining strong security protocols, essential for preserving data integrity in remote areas with high signal interference risks. To analyze real-time environmental data, the system employs artificial intelligence (AI) to enhance detection accuracy and predict fire behavior, thereby improving response effectiveness and wildfire management. Current wildfire detection systems suffer from several limitations, including scalability issues, high power consumption, and latency caused by static or overly complex network architectures. In contrast to conventional topologies, the proposed inherently adaptive star/circle hybrid topology dynamically optimizes deployment for diverse forest terrains (flat/mountainous and dense/sparse). Its concentric circular tiers with axis nodes ensure structural efficiency, minimizing redundant coverage through vertical rerouting in dense forests or intra-tier routing in sparse areas, while adjustable tier spacing enhances environmental resilience. Additionally, the proposed routing protocols introduce adaptive inter-tier jumps to bypass obstacles and hybrid quadrant-based routing to reduce path complexity. Unlike traditional MAC layers, the dynamic arbitration scheme incorporates fire-risk-based queuing and AI-augmented conflict resolution to prioritize critical alerts. Further enhancing deployment feasibility, the proposed approach employs lightweight multiclass logistic regression--contrasting with resource-intensive deep learning, which operates on real-time sensor data without relying on satellite connections. Practical benefits include scalable node deployment, reliable ZigBee-GSM communication, and energy-efficient operation, together overcoming key limitations of current wildfire detection systems in scalability, adaptability, and responsiveness. This work makes several key contributions to wildfire detection systems:  
* A novel hybrid star/circular WSN topology optimized for remote forest environments, reducing node deployment by 53-55% compared to conventional Mesh 2D topologies while ensuring robust coverage through concentric tiered arrangements adaptable to flat or mountainous terrain. * An adaptive routing framework supporting dynamic vertical inter-tier jumps and priority-based arbitration, reducing latency by 41-81% and hops by 50-60% in dense forests while maintaining connectivity during node failures or congestion. * A lightweight AI-driven fire-risk classifier using multiclass logistic regression on real-time temperature/humidity data, achieving 99.97% accuracy with minimal computational overhead, validated against meteorological datasets from Saudi Arabia. * Comprehensive simulation-based validation demonstrating superior packet loss reduction (up to 80.4%), energy efficiency (0.0425-0.0832 W in non-dense topology), and scalability across diverse forest densities using Proteus (hardware-level) and CupCarbon (network-level) tools. * Practical deployment innovations, including GSM integration for offline alert transmission, dynamic round-robin arbitration for conflict resolution, and topology configurability for mixed terrains, ensuring cost-effective and reliable operation in resource-constrained areas. Together, these advances deliver a scalable, energy-efficient, and terrain-resilient solution addressing critical gaps in existing WSN-based wildfire detection systems. The remainder of the paper is organized as follows. We describe the related works in Section 2. We present the proposed architecture and methodology in Section 3. Effectiveness and efficiency evaluation results are described in Section 4. We conclude the paper in Section 5.