The Application of IoT-Based Path Search Algorithms in Rural Tourism Experience

The development of rural tourism has gained significant attention in recent years, with increasing emphasis on enhancing tourist experiences through intelligent navigation and optimized route planning. Traditional path search algorithms, such as Dijkstra and A*, have been widely used for route optimization. However, these approaches often fail to adapt to dynamic environmental factors, user preferences, and real-time traffic conditions in rural areas. Additionally, conventional methods lack the capability to leverage heterogeneous data sources from IoT devices, limiting their effectiveness in personalized and adaptive path recommendations. To address these challenges, we propose IoT-PathFinder, a novel path search algorithm that integrates Internet of Things (IoT) data with intelligent route optimization strategies. The model combines Reinforcement Learning for adaptive decision-making, Graph Neural Networks for spatial awareness, and Transfer Learning to enhance model generalization across different rural landscapes. Moreover, a context-aware feedback mechanism is incorporated to continuously refine route recommendations based on user interactions and environmental changes. Experimental results demonstrate that IoT-PathFinder outperforms existing state-of-the-art methods across multiple metrics. On the RuralNav dataset, the model achieves an accuracy of 96.12%, an F1 Score of 93.87%, and a route optimization efficiency improvement of 17.5% compared to baseline methods. The real-time inference time is reduced to 185.34 ms, enabling efficient path computations for practical applications. The significance of this research lies in its ability to provide an intelligent, adaptive, and scalable solution for enhancing rural tourism experiences through IoT-driven path search algorithms. By integrating cutting-edge machine learning techniques and real-time IoT data, IoT-PathFinder ensures more personalized, efficient, and context-aware route recommendations, ultimately improving the overall satisfaction of tourists exploring rural destinations.