Accurately positioning wireless sensor network nodes is challenging when data is scarce. Traditional methods heavily rely on data-driven or content-specific approaches, making precise localization difficult. This paper introduces a novel method combining human intelligence and machine learning to address this issue. By integrating the Three-Dimensional (3D) Voronoi diagram division and a hybrid regression model, it aims to enhance localization accuracy with limited data. The proposed approach involves two stages: offline training and online testing. During the offline phase, Voronoi diagram division segments the localization space into smaller regions, reducing the need for human intervention. The hybrid model, called Hybrid Weighted Regression with Support vector regression and K-nearest neighbors Regression (HWR-SKR), combines Support Vector Regression (SVR) and K-nearest Neighbors Regression (KNR) to leverage the strengths of both. Training and testing utilize received signal strength data, anchor node coordinates, and Voronoi cell vertex coordinates. Experiments demonstrate that the proposed method accurately locates nodes even with limited data. The HWR-SKR model outperforms individual SVR and KNR models, improving real-time positioning tasks’ accuracy and stability. This study presents a promising solution for precise node localization in wireless sensor networks, enhancing localization performance and supporting sensor network applications requiring accurate spatial awareness.

Unmanned Aerial Vehicles (UAVs) are enabled to be fast and flexible in managing traffic compared to the conventional methods. However, in emergencies, this system takes more time to identify and clear the traffic because of fixed time control. To overcome this problem, an automated intelligent traffic monitoring and controlling system is designed using YOLO V3 neural architecture and implemented to detect the emergency vehicles from video stream data from UAVs using deep Convolution Neural Network (CNN) along with re-routing algorithm to provide the safest alternate route from current position to destination, in a heavy traffic environment. The real-time visual data collected through UAV video cameras are trained using machine learning algorithms to produce statistical profiles that are used continuously as updated inputs to the existing traffic simulation models for improving predictions. The proposed automated system performs exemplary in recognizing emergency vehicles and diverting them to an alternate route for quick transportation in various scenarios.

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, 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 proved highly effective for detecting side-channel attacks 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.

In the contemporary era, driverless vehicles are a reality due to the proliferation of distributed technologies, sensing technologies, and Machine to Machine (M2M) communications. However, the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy efficient. From existing methods, it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy efficiency. However, the models focus on different aspects separately. There is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence (AI) on autonomous driving and energy efficiency. Towards this end, we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision making. It focuses on both safe driving in highway scenarios and energy efficiency. The deep learning based framework is realized with many models used for localization, path planning at high level, path planning at low level, reinforcement learning, transfer learning, power control, and speed control. With reinforcement learning, state-action-feedback play important role in decision making. Our simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.