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Dongfeng Fu

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Review Open access 2026

A Comprehensive Review on Integrated Sensing, Communication, and Computing in Internet of Vehicles through Unmanned Aerial Vehicle: Recent Advances, Key Technologies, and Future Directions

: With the rapid development of fifth generation (5G), 5G-Advanced, edge computing, and early sixth generation (6G) technologies, the Internet of Vehicles (IoV) is evolving toward highly connected, intelligent, and delay-sensitive transportation services. Nevertheless, ground infrastructure still faces coverage holes, blockage, overloaded roadside units (RSUs), limited backhaul, and weak service continuity in urban canyons, crowded intersections, long highway segments, rural roads, and emergency areas. Unmanned aerial vehicles (UAVs) can provide flexible aerial relay, mobile sensing, temporary coverage, and lightweight edge computing support for these scenarios. This survey reviews UAV-assisted IoV from an integrated sensing, communication, and computing (ISCC) perspective. It first summarizes representative air-ground network architectures, including multi-UAV collaboration, vehicle-road-cloud collaborative edge computing, blockchain-supported edge intelligence, ISCC-oriented networking, low-altitude digital twins, and low Earth orbit (LEO) satellite-assisted networking. It then analyzes key technologies, including task offloading, dynamic resource allocation, low-latency and 6G-enabled communication, UAV endurance optimization, security and privacy protection, and intelligent algorithm-digital twin integration. Different from descriptive summaries, this review emphasizes the coupling among sensing quality, communication reliability

Chao He, Dongfeng Fu, Xin Xie et al. · Computers, Materials & C... · 0 citations

Conference Aug 2026

Joint DQN Optimization of Task Offloading and Resource Allocation for Low-AoI in IoV

With the advancement of autonomous driving and smart navigation, Internet of Vehicles (IoV) systems face stringent requirements for real-time data delivery and processing reliability. Traditional metrics cannot fully capture information timeliness due to network dynamics and packet loss. Existing approaches also struggle with the coupling between task offloading and resource allocation, lacking adaptability in dynamic IoV environments. To address these issues, we propose a joint optimization scheme using a deep Q-network (DQN). Specifically, we build an IoV system model incorporating V2V and V2I communication, and formulate an optimization problem to minimize the average age of information (AAoI) under delay, bandwidth, computing, and energy constraints. We then design a mixed-action DQN algorithm with dual-network architecture, experience replay, and an action mask mechanism to enhance training stability and environmental adaptability. Simulation results show that our DQN-based scheme achieves the lowest AAoI among Random, Greedy, A2C, and DDQN, with reductions of 29.5%, 8.9 %, 7.1 %, and $\mathbf{7. 6 \%}$, respectively. It also exhibits superior delay and energy performance, confirming its effectiveness for dynamic IoV task offloading and resource allocation.

Chao He, Wanting Wang, Dongfeng Fu et al. · 2026 International Conferenc... · 0 citations