Jul 2026· Italian National Conference on Sensors· Vol 26· 0 citations· 43 references
Medicine
Abstract
The Internet of Vehicles (IoV) is a decentralized network architecture that enables autonomous driving, real-time applications, infotainment services, and seamless vehicle-to-everything communication. While infotainment systems enhance the user experience by providing entertainment and navigation features, their high data demands can cause network congestion, potentially delaying mission-critical messages and compromising safety and reliability. Further, the increasing volume of connected devices and data traffic exacerbates these challenges, resulting in high latency, low throughput, and reduced network efficiency. To address this, we propose a novel lightweight adaptive network slicing strategy with four dedicated slices and priority-weighted dynamic bandwidth allocation for IoV networks to mitigate congestion and ensure the availability of the required bandwidth for critical communications. The proposed mechanism defines four distinct network slices, allocating resources to balance infotainment and mission-critical needs. Simulation results demonstrate that our approach achieves ultra-low latency (<5 ms), near-zero packet loss (<0.5%), and high throughput (53 Mbps for infotainment), significantly outperforming existing methods, and ensuring reliable communication for safety-critical tasks while improving spectrum utilization. Findings validate implementing network slicing in IoV environments, paving the way for efficient, congestion-free, and high-performance vehicular networks.
SOVANET+ is presented, an extended scheduling technique that jointly accounts for service criticality, network load, and wireless link quality to allocate resources adaptively across coexisting Vehicle-to-Everything (V2X) services, supporting its viability for next-generation intelligent transportation systems.
Athanasios Kanavos, Gerasimos Papanikolaou-Ntais, A. Kaloxylos· Electronics· 0 citations
Network slicing is an enabling technology of fifth-generation (5G) mobile networks that enables several autonomous logical networks to exist on a common physical infrastructure. One key issue of the paradigm is the admission control mechanism which slice requests are accepted to achieve the best performance of the system and still ensure quality-of-service (QoS) guarantees. In this paper, the authors provide a comparative study of the current methods of admission control and introduce a new approach, Priority-Aware Deep Q-Network with Dynamic Threshold Adaptation (PA-DQN-DTA). Our method (as opposed to the traditional methods which tie admission control to resource allocation) addresses intelligent admission decisions only. The suggested scheme uses inter-slice and intra-slice priorities via a mathematically defined admission probability functional. The outcomes of simulation in four assessment scenarios show that parameter calibration is of the essence: the balanced configuration reaches acceptance ratios of 11.812.9, and QoE in all scenarios is above 98.8%. Besides, this paper presents an in-depth analysis of parameter tuning and describes the space of tradeoffs between the acceptance ratio, the QoE preservation, and the resource usage. Concrete recommendations are made and implications to the realistic 5G deployment are discussed.
A. S. Mahore, C. N. Deshmukh· International Conference Com...· 0 citations
The rapid advancement of wireless communication has led to the emergence of the fifth-generation (5G) network, which aims to provide ultra-reliable low-latency communication (URLLC) while ensuring high data transmission rates. Optimizing data transmission in 5G networks is critical for supporting real-time applications such as autonomous vehicles, telemedicine, industrial automation, and smart cities. This paper explores various techniques and strategies to enhance data transmission efficiency, minimize latency, and improve reliability in 5G networks. We analyze the key performance indicators (KPIs) that influence data transmission, including bandwidth utilization, network slicing, and multiple access techniques. Furthermore, we discuss the role of edge computing, artificial intelligence (AI)-driven network management, and adaptive modulation techniques in optimizing data transmission. The paper also highlights the impact of interference management, energy efficiency considerations, and security protocols on 5G network performance. We conduct a comprehensive literature survey to examine existing optimization techniques and propose an improved methodology leveraging AI-driven resource allocation and dynamic spectrum sharing. Through simulation and analytical results, we demonstrate the effectiveness of the proposed approach in reducing end-to-end latency and improving network reliability. The findings contribute to the ongoing efforts in optimizing 5G networks and lay the foundation for future research in beyond-5G (B5G) and sixth-generation (6G) communication systems.
William Hughes, Marta Silva· International Journal of Dat...· 0 citations
The rapid advancement of wireless communication has led to the emergence of the fifth-generation (5G) network, which aims to provide ultra-reliable low-latency communication (URLLC) while ensuring high data transmission rates. Optimizing data transmission in 5G networks is critical for supporting real-time applications such as autonomous vehicles, telemedicine, industrial automation, and smart cities. This paper explores various techniques and strategies to enhance data transmission efficiency, minimize latency, and improve reliability in 5G networks. We analyze the key performance indicators (KPIs) that influence data transmission, including bandwidth utilization, network slicing, and multiple access techniques. Furthermore, we discuss the role of edge computing, artificial intelligence (AI)-driven network management, and adaptive modulation techniques in optimizing data transmission. The paper also highlights the impact of interference management, energy efficiency considerations, and security protocols on 5G network performance. We conduct a comprehensive literature survey to examine existing optimization techniques and propose an improved methodology leveraging AI-driven resource allocation and dynamic spectrum sharing. Through simulation and analytical results, we demonstrate the effectiveness of the proposed approach in reducing end-to-end latency and improving network reliability. The findings contribute to the ongoing efforts in optimizing 5G networks and lay the foundation for future research in beyond-5G (B5G) and sixth-generation (6G) communication systems.
Kenji Sato· International Journal of Mod...· 0 citations
Ensuring deterministic and reliable communication is essential for in-vehicle networks supporting autonomous driving and safety-critical functions. Time-Sensitive Networking has emerged as a key enabler for such systems. Among its mechanisms, the IEEE 802.1Qcr Asynchronous Traffic Shaper (ATS) offers fine-grained traffic control without requiring global time synchronization. However, the practical deployment of ATS in Automotive Ethernet networks remains challenging due to the difficulty of parameter configuration. The performance of ATS strongly depends on the appropriate setting of key parameters such as the Committed Information Rate (CIR) and Committed Burst Size (CBSz), which are highly sensitive to both network topology and traffic workload. Conventional approaches relying on static configuration or empirical tuning may face difficulties in ensuring QoS when network conditions change. This paper proposes a method for automated, high-precision optimization of ATS parameters in automotive networks. We analyze the impact of key parameters—CIR and CBSz—on delay and frame loss, and develop a machine learning model to select optimal settings under dynamic traffic conditions. Our results reveal that proper ATS parameter configuration is essential for deterministic latency and reliability in Automotive Ethernet networks. CIR mainly governs bandwidth, affecting queuing delay and frame loss, while CBSz balances delay reduction against burst-induced congestion. Furthermore, tree-based ensemble models such as LightGBM and Gradient Boosting achieved high prediction accuracy and QoS satisfaction under varying traffic conditions.
Taisei Isobe, Han Nay Aung, Yasuhiro Yamasaki et al.· Annual International Compute...· 0 citations