The fourth industrial revolution is accelerating industrial automation. In industrial networks, manufacturing processes require hard real-time communication where the latency is less than 1ms. Time-sensitive networking (TSN) technology over Ethernet already supports deterministic delivery for real-time communication. However, TSN technologies over wireless networks are currently in their initial development stage. Therefore, this study presents an overview of TSN research trends in wireless communications. This paper focuses on 5G networks and IEEE 802.11. We summarize standardization trends for TSN in 5G networks and introduce the TSN technologies for 802.11-based WLAN. Then, we introduce the integration scenario of 5GS with WLAN. This study provides insights into wireless communication technologies for wireless TSN.

In autonomous driving technologies, a camera is necessary for establishing a path and detecting an object. Object recognition based on images from several cameras is required to detect impediments in autonomous ships. Furthermore, in order to avoid ship collisions, it is important to follow the movements of recognized ships. In this paper, we use the Singapore Maritime Dataset (SMD) and crawling image for model training. Then, we present four YOLO-based object recognition models and evaluate their performance in the maritime environment. Then, we propose a tracking algorithm to track the identified objects. Specially, in evaluation with high-motion video, the proposed tracking algorithm outperforms deep simple online and real-time tracking (DeepSORT) in terms of object tracking accuracy.

Recently, realistic services like virtual reality and augmented reality have gained popularity. These realistic services require deterministic transmission with end-to-end low latency and high reliability for practical applications. However, for these real-time services to be deterministic, the network core should provide the requisite level of network. To deliver differentiated services to each real-time service, network service providers can classify applications based on traffic. However, due to the presence of personal information in headers, application classification based on encrypted application data is necessary. Initially, we collected application traffic from four well-known applications and preprocessed this data to extract encrypted application data and convert it into model input. We proposed a lightweight transformer model consisting of an encoder, a global average pooling layer, and a dense layer to categorize applications based on the encrypted payload in a packet. To enhance the performance of the proposed model, we determined hyperparameters using several performance evaluations. We evaluated performance with 1D-CNN and ET-BERT. The proposed transformer model demonstrated good performance in the performance evaluation, with a classification accuracy and F1 score of 96% and 95%, respectively. The time complexity of the proposed transformer model was higher than that of 1D-CNN but performed better in application classification. The proposed transformer model had lower time complexity and higher classification performance than ET-BERT.

With the evolution of cellular networks and wireless-local-area-network-based communication technologies, services for smart device users have appeared. With the popularity of 4G and 5G, smart device users can now consume larger bandwidths than before. Consequently, the demand for various services, such as streaming, online games, and video conferences, has increased. For improved quality of experience (QoE), streaming services utilize adaptive bitrate (ABR) algorithms to handle network bandwidth variations. ABR algorithms use network bandwidth history for future network bandwidth prediction, allowing them to perform efficiently when network bandwidth fluctuations are minor. However, in environments with frequent network bandwidth changes, such as wireless networks, the QoE of video streaming often degrades because of inaccurate predictions of future network bandwidth. To address this issue, we utilize the gated recurrent unit, a time series prediction model, to predict the network bandwidth accurately. We then propose a buffer-based ABR streaming technique that selects optimized video-quality settings on the basis of the predicted bandwidth. The proposed algorithm was evaluated on a dataset provided by Zeondo by categorizing instances of user mobility into walking, bus, and train scenarios. The proposed algorithm improved the QoE by approximately 11% compared with the existing buffer-based ABR algorithm in various environments.

Various technologies have been developed for the efficient use of the multiple radio access technologies resource at the radio access network level or other network levels to improve user service quality in mobile communication networks. In long-term evolution, mobile carriers are commercializing radio access network-level traffic aggregation technologies such as licensed-assisted access-long-term evolution, long-term evolution-unlicensed, and long-term evolution-wireless local area network aggregation, which use the multi-accesses of the 3rd Generation Partnership Project and WiFi, and the multipath transmission control protocol–based traffic aggregation technologies at the L3 network level. The standardization of 3rd Generation Partnership Project Release 16, which is scheduled to be completed by 2020, is under progress to support the traffic aggregation technology at the L3 network level through a multi-access 5G network. Multipath transmission control protocol is also considered as a traffic aggregation technology. However, it is difficult to apply the multipath transmission control protocol employment model used in long-term evolution to the 5G network structure as it is due to the change to a common core architecture that accommodates multiple radio access technologies through one common interface. Therefore, this article proposes an optimal 5G system architecture and a multipath transmission control protocol adaptation method to support the access traffic steering function based on multipath transmission control protocol in a 3rd Generation Partnership Project 5G mobile communication network. We have verified the development of the multipath transmission control protocol–based multi-access traffic steering technology by implementing the proposed solution in a commercial server on a testbed based on the 5G system standard of 3rd Generation Partnership Project Release 15. Furthermore, this article defines problems that occur when implementing the multipath transmission control protocol–based multi-access traffic steering system and proposes relevant solutions. Based on the implementation results, it is demonstrated that the proposed multipath transmission control protocol–based multi-access traffic steering system can perform traffic steering in the 3rd Generation Partnership Project 5G network.