Application of wearable positioning system based on sensor network in rural tourism management

Abstract This study investigates the use of wearable positioning systems supported by sensor networks for rural tourism management. A novel Advanced Kalman Filter Tourism based on Sensor Network (AKFT-SN) is proposed to integrate infrastructure monitoring, tourist interaction platforms, and environmental sensing. The model predicts visitor flows, restricts overcrowding through traffic detection, and supports real-time health and safety monitoring. Experimental evaluation using a rural tourism dataset (2018–2023) demonstrates that AKFT-SN achieves superior accuracy (96.5%) compared with existing methods such as Support Vector Machine (SVM), Internet of Things (IoT), Convolutional Neural Network (CNN), and Apriori algorithms. The results highlight its potential for optimizing resource allocation, enhancing visitor experiences, and promoting sustainable rural tourism development.