Chu Thi Minh Hue

The rapid growth of the Internet of Vehicles (IoV) has created an urgent need for low‐latency, energy‐efficient and intelligent task processing in highly dynamic vehicular environments. This paper presents a comprehensive review of intelligent offloading approaches for MEC‐enabled IoV networks from an expert systems perspective. First, we systematically classify existing methods into four categories: heuristic‐based, optimization‐based, learning‐based and hybrid intelligent approaches. A comparative analysis is then conducted based on key performance metrics, including latency, energy efficiency, scalability, adaptability and sustainability. Beyond conventional performance evaluation, this review highlights the growing importance of knowledge‐driven decision‐making, explainability and hybrid reasoning mechanisms in enhancing the transparency, reliability and trustworthiness of offloading decisions in safety‐critical vehicular applications. Furthermore, the paper identifies key research challenges, including mobility awareness, real‐time adaptability, privacy preservation, interoperability and carbon‐aware edge intelligence. A conceptual framework integrating deep reinforcement learning with expert systems is also discussed to illustrate future directions toward explainable and sustainable offloading. Finally, open research opportunities are outlined, emphasizing the development of intelligent, adaptive and environmentally sustainable offloading frameworks for next‐generation 6G‐enabled IoV systems.

The development of the healthcare industry is closely tied to the history of human development. The integration of sensing, communication, computing, and control technologies, along with cloud-based solutions, enables the realization of the Internet of Things concept and forms a series of Internet of Healthcare Things (IoHT) applications. However, providing realtime health services is one of the most important challenges for IoHT systems. To address this issue, a fog computing architecture (FC) is proposed as an additional computing layer to support the cloud computing layer, aiming to reduce service response time, computing costs, and energy consumption. In this study, we conduct a comprehensive evaluation to optimize resource allocation for hospitals under the constraints of scale and patient volume, as well as SLA thresholds. Then, we provide recommendations to optimize investment costs for computing infrastructure supporting real-time health services. Finally, we discuss challenges and open issues.

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.