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Strategic Data Offloading for 5G and Beyond for Internet of Vehicles Networks: Current Trends and Future Directions
Author(s) -
Vu Khanh Quy,
Abdellah Chehri,
Vi Hoai Nam,
Chu Thi Minh Hue,
Dang Van Anh,
Nguyen Minh Quy
Publication year - 2025
Publication title -
ieee open journal of the communications society
Language(s) - English
Resource type - Magazines
eISSN - 2644-125X
DOI - 10.1109/ojcoms.2025.3611958
Subject(s) - communication, networking and broadcast technologies
Mobility has long been a driving force behind human progress, continuously evolving alongside technological advancements. The proliferation of 5th-generation (5G) and the anticipated development of 6th-generation (6G) communication technologies, alongside advancements in artificial intelligence (AI), have significantly transformed the Internet of Vehicles (IoV) ecosystem. Despite these innovations, the computational demands of IoV applications have surged, placing substantial strain on backhaul network infrastructure and leading to increased service response times, computational costs, and energy consumption. To address these challenges, strategic data offloading for IoV has emerged as a critical research domain. Effective offloading mechanisms enhance computational efficiency by optimizing task distribution across network layers. However, determining what to offload, where to offload, and how to execute offloading optimally remains a pivotal challenge. This paper presents a comprehensive review of offloading strategies in 5G and beyond for IoV networks, systematically examining key decision-making principles, architectural frameworks, and optimization methodologies. Through an extensive comparative analysis, the most effective offloading strategies are identified, while their trade-offs in terms of latency, energy efficiency, scalability, and security are evaluated. Additionally, critical challenges are discussed, and future research directions essential for fostering greener and more intelligent IoV ecosystems are outlined. By advancing offloading techniques, this research demonstrates their role in enhancing the performance, sustainability, and adaptability of next-generation transportation systems, ultimately driving the evolution of smarter mobility solutions while improving traffic flow analysis and network responsiveness.

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