In mobile networks, handover mechanisms provide fast and smooth access service for mobile users. However, one of the main challenges in mobile networks is the handover management with increased mobility and bandwidth demand of the required network services. Therefore, in this paper, we propose a MOS-aware (mean opinion score-aware) mobile network handover mechanism based on deep learning to determine the appropriate handover time for real-time video conference services in mobile networks. We construct a wireless network topology with LTE characteristics in a Mininet-WiFi simulation. User equipment (UE) can determine the service-required MOS (Mean Opinion Score) from the proposed deep-learning-based handover mechanism with appropriate handover time. Simulation results show that the proposed scheme provides higher performance than the original A3 handover mechanism. The contribution of this paper is to combine the real-time video conferencing services with a deep-learning-based handover mechanism by predicting MOS values to improve the quality of service for users in mobile networks.

With the advance of technology, there is an increasing variety of Internet of Things (IoT) devices, which could be vulnerable to hacker attacks. Therefore, intrusion detection for these IoT devices is crucial. Federated learning, a novel distributed learning approach, which trains multiple clients together and aggregates their weights through a server to achieve collaborative learning. These weights do not disclose the clients' individual dataset information, thereby preserve client privacy while benefiting from the knowledge of other clients. As the intrusion detection datasets for IoT devices come from different domains, the attack categories in these datasets are often highly dissimilar. Therefore, it is necessary to address the Non-Independent and Identically Distributed (non-IID) nature of the data in federated learning. In this paper, we propose three methods to handle the issues for different numbers of class outputs: maximum value, union, and federated transfer learning.

Network traffic classification is vital for network security. However, the non-IID nature of participant data poses challenges for federated learning in this context. This study examines the impact of non-IID on model performance in federated learning for network traffic classification. We analyze three non-IID data distribution scenarios: distribution-based label imbalance, quantity-based label imbalance, and quantity skew. We evaluate the effect of these distributions on federated learning performance using training models with varying degrees of non-IID data. Our experiments evaluate model accuracy and convergence efficiency. We also compare the influence of different non-IID levels on model performance. Results show that federated learning effectively handles non-IID challenges and performs well across all scenarios. However, increased data heterogeneity or label imbalance reduces model accuracy and convergence efficiency. Models perform relatively poorer with non-uniform label distributions. In conclusion, federated learning is a viable solution for non-IID in network traffic classification, emphasizing the need to consider the impact of data heterogeneity. These insights can guide the application of federated learning in network traffic classification and related fields.

Location estimation for object tracking is one of the important topics in the research of wireless sensor networks (WSNs). Recently, many location estimation or position schemes in WSN have been proposed. In this paper, we will propose the procedure and modeling of location estimation for object tracking in WSN. The designed modeling is a simple scheme without complex processing. We will use Matlab to conduct the simulation and numerical analyses to find the optimal modeling variables. The analyses with different variables will include object moving model, sensing radius, model weighting valueα, and power-level increasing ratiokof neighboring sensor nodes. For practical consideration, we will also carry out the shadowing model for analysis.

In the past few years, the development of Industry 4.0 has driven the demand for automation equipment in factories, thereby requiring better real-time monitoring tools to control the factory environment and equipment. Industrial Internet of Things (IIoT), as an important component of Industry 4.0, utilizes the capabilities of smart machines and real-time analytics to collect relevant data through various sensors, monitors, and other monitoring technologies deployed on devices, in order to improve operational efficiency. However, as the boundary between Information Technology (IT) and Operational Technology (OT) is rapidly disappearing, OT environments now require dedicated solutions for OT and IoT security. Due to the differences in packet features and labels collected in each domain, this study focuses on proposing a joint transfer learning approach combined with an intrusion detection system, and simulates datasets collected from different domains using three OT-related public datasets: Edge-IIoTset, WUSTL-IIOT-2021 Dataset, and Electra dataset (Modbus). It discusses the issues related to different attack types and dataset features. In addition, we compare the effects of different deep learning models on intrusion detection systems. The experimental results demonstrate that the proposed approach of bidirectional federated transfer learning enables cooperative learning of heterogeneous data.