Wireless sensor network node positioning faces challenges when dealing with limited data. Traditional methods rely heavily on either data-driven approaches or artificially designed content, making achieving accurate localization with sparse data difficult. This paper addresses the challenge of precise node localization in wireless sensor networks by introducing a novel approach that combines human cognition with machine learning techniques. Specifically, it aims to improve localization accuracy in scenarios with limited data by integrating Voronoi diagram division and a hybrid regression model. The proposed method involves a two-stage process: offline preparation and online testing. In the offline stage, the Voronoi diagram division is utilized to segment the localization space, reducing the need for manual intervention. The hybrid regression model, termed HWR-SKR, combines Support Vector Regression (SVR) and K-nearest neighbors Regression (KNR) to leverage the strengths of both algorithms. Training and testing are conducted based on RSS (Received Signal Strength) data, incorporating anchor node coordinates and Voronoi cell vertex coordinates. Experimental results demonstrate the effectiveness of the proposed approach in achieving precise node localization with limited data. The HWR-SKR hybrid regression model outperforms individual SVR and KNR models, offering improved accuracy and robustness in real-time positioning tasks. By integrating human cognition through Voronoi diagram division and machine learning techniques via the HWR-SKR hybrid regression model, this study presents a promising solution for accurate node localization in wireless sensor networks, particularly in scenarios with sparse data. The approach offers a practical method for improving localization performance, contributing to advancements in sensor network applications requiring precise spatial awareness.

Mobile devices within Fifth Generation (5G) networks, typically equipped with Android systems, serve as a bridge to connect digital gadgets such as Global Positioning System (GPS), mobile devices, and wireless routers, which are vital in facilitating end-user communication requirements. However, the security of Android systems has been challenged by the sensitive data involved, leading to vulnerabilities in mobile devices used in 5G networks. These vulnerabilities expose mobile devices to cyber-attacks, primarily resulting from security gaps. Zero-permission apps in Android can exploit these channels to access sensitive information, including user identities, login credentials, and geolocation data. One such attack leverages “zero-permission” sensors like accelerometers and gyroscopes, enabling attackers to gather information about the smartphone’s user. This underscores the importance of fortifying mobile devices against potential future attacks. Our research focuses on a new recurrent neural network prediction model, which has proven highly effective for detecting side-channel attacks (SCAs) in mobile devices in 5G networks. We conducted state-of-the-art comparative studies to validate our experimental approach. The results demonstrate that even a small amount of training data can accurately recognize 37.5% of previously unseen user-typed words. Moreover, our tap detection mechanism achieves a 92% accuracy rate, a crucial factor for text inference. These findings have significant practical implications, as they reinforce mobile device security in 5G networks, enhancing user privacy and data protection.