Urban monitoring in 6th Generation (6 G) networks is vital for ensuring smart city security and efficiency. Traditional methods rely on either standalone Unmanned Aerial Vehicles (UAVs) or Unmanned Ground Vehicles (UGVs), often suffering from limited coverage, intermittent connectivity, and inefficient energy management. Recent works have explored UAV-UGV collaboration to enhance surveillance and communication; however, they lack dynamic communication optimization and energy-efficient coordination. To address these gaps, we propose a novel cooperative framework integrating UAVs equipped with Reconfigurable Intelligent Surfaces (RIS) and UGVs for real-time monitoring. Unlike prior approaches, our system optimizes UAV flight paths and recharging schedules using Deep Reinforcement Learning (DRL) while refining UGV patrol routes with a Genetic Algorithm (GA), ensuring adaptive and continuous surveillance. Additionally, we employ Differential Evolution (DE) for RIS configuration, enhancing data transmission and mitigating urban signal degradation. UAVs further support UGVs by wirelessly recharging them via energy beamforming, reducing dependency on fixed charging stations. By leveraging AI-driven coordination, RIS-assisted communication, and real-time energy optimization, our framework ensures seamless data transmission, reduces latency, and maximizes energy efficiency. Simulation results demonstrate that our approach significantly improves communication reliability, monitoring coverage, and energy consumption compared to existing methods, making it a promising solution for next-generation urban monitoring.

The latest developments in Advanced Driver Assistance Systems (ADAS) have greatly enhanced the comfort and safety of drivers. These technologies can identify driver abnormalities like fatigue, inattention, and impairment, which are essential for averting collisions. One of the important aspects of this technology is automated traffic accident detection and prediction, which may help in saving precious human lives. This study aims to explore critical features related to traffic accident detection and prevention. A public US traffic accident dataset was used for the aforementioned task, where various machine learning (ML) models were applied to predict traffic accidents. These ML models included Random Forest, AdaBoost, KNN, and SVM. The models were compared for their accuracies, where Random Forest was found to be the best-performing model, providing the most accurate and reliable classification of accident-related data. Owing to the black box nature of ML models, this best-fit ML model was executed with explainable AI (XAI) methods such as LIME and permutation importance to understand its decision-making for the given classification task. The unique aspect of this study is the introduction of explainable artificial intelligence which enables us to have human-interpretable awareness of how ML models operate. It provides information about the inner workings of the model and directs the improvement of feature engineering for traffic accident detection, which is more accurate and dependable. The analysis identified critical features, including sources, descriptions of weather conditions, time of day (weather timestamp, start time, end time), distance, crossing, and traffic signals, as significant predictors of the probability of an accident occurring. Future ADAS technology development is anticipated to be greatly impacted by the study’s conclusions. A model can be adjusted for different driving scenarios by identifying the most important features and comprehending their dynamics to make sure that ADAS systems are precise, reliable, and suitable for real-world circumstances.

Wireless Sensor Networks (WSNs) are increasingly deployed in diverse commercial and industrial IoT applications, yet they remain vulnerable to security threats due to their wireless nature and resource constraints. While Information-Centric Networking (ICN) architectures improve data-centric security over conventional IP networks, internal attacks continue to challenge network reliability. To address these issues, we propose EPSTM, an Evolutionary Particle Swarm Optimized Trust Management Scheme, for authenticating IoT nodes and mitigating malicious behavior. The scheme evaluates trust using network-specific metrics, including device proximity, energy consumption, data transmission reliability, and message delivery timing, to guide secure routing decisions. Simulation results demonstrate that the proposed framework outperforms current state-of-the-art techniques, achieving reduced response times, minimized authentication delays, and lower request volumes from compromised nodes. Notably, for a network of 100 nodes, EPSTM achieved an accuracy of 98.66%, highlighting its efficacy in enabling secure, energy-efficient, and trustworthy IoT communication.