文献类型：期刊文献

英文题名：Hybrid Models for Forecasting Allocative Localization Error in Wireless Sensor Networks

作者：Li, Guo[1]; Sheng, Hongyu[2]

第一作者：Li, Guo

机构：[1] State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, China; [2] College of Robotics, Beijing Union University, Beijing, 100101, China

第一机构：State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, China

通讯机构：[2]College of Robotics, Beijing Union University, Beijing, 100101, China|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：2025

卷号：6

起止页码：333-350

外文期刊名：International Journal of Cognitive Computing in Engineering

收录：EI(收录号：20250617812362);Scopus(收录号：2-s2.0-85216795732)

语种：英文

外文关键词：Environmental monitoring - Resource allocation - Sensor nodes

摘要：This study presents a machine learning-based approach to forecast Allocative Localization Error (ALE) in Wireless Sensor Networks (WSNs), addressing challenges such as dynamic network topologies and resource constraints. The approach utilizes Radial Basis Function (RBF) models enhanced with advanced optimization algorithms, including Coot Optimization Algorithm (COA), Smell Agent Optimization (SAO), and Northern Goshawk Optimization (NGO) to improve ALE prediction accuracy. Hybrid models (RBCO, RBSO, and RFNG) are developed by integrating these optimization techniques, which refine critical RBF parameters, such as spread and center selection, through iterative optimization. Furthermore, an ensemble framework (RSNC) combines all three optimizers with RBF to achieve superior performance. The proposed methods are validated using R2 and RMSE metrics, demonstrating their ability to minimize ALE, optimize resource allocation, and extend network lifespans. The study highlights the practical applicability of these models in real-world scenarios, such as environmental monitoring and industrial automation, offering enhanced efficiency and economic benefits. The RFNG model, in particular, achieved the lowest Mean Absolute Relative Error (MARE) of 0.049, demonstrating superior performance compared to other approaches in the test section. Moreover, RBNG obtained 0.069 and 0.978 values for the RMSE and R2, respectively, which were the most suitable values compared to other models, namely RBGO, RBSO, RSNC, and RBF. The results indicate that the proposed hybrid models significantly improve the prediction of ALE, leading to more efficient node deployment and better network management. This research provides valuable insights into leveraging machine learning for WSN optimization, benefiting researchers, network engineers, and industries relying on sensor networks for applications such as environmental monitoring, smart cities, and asset tracking. ? 2025