Accepted author manuscript, 1.74 MB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - UAV-Assisted Content Caching for Human-Centric Consumer Applications in IoV
AU - Wang, Wen
AU - Xu, Xiaolong
AU - Bilal, Muhammad
AU - Khan, Maqbool
AU - Xing, Yizhou
PY - 2024/2/29
Y1 - 2024/2/29
N2 - With various consumer electronics deployed in Internet of Vehicles (IoV), human-centric consumer in-vehicle applications (e.g., driver assistance, path planning, and healthcare system) can supply high-quality driving experience and enhance travel safety within a short time. In addition, Unmanned Aerial Vehicles (UAV) are expected to be critical to assist terrestrial vehicular networks in delivering delay-sensitive contents of services. However, due to the mutual coupling of trajectory planning of UAVs, serving the same task requests repeatedly in the same area results in wasted resources. Hence, it is challenging to supply high-quality services while ensuring energy-efficient content caching. To solve this dilemma, a content Caching scheme with Trajectory design through differential evolution and Deep Reinforcement learning (CTDR) is introduced. Specifically, a content caching scheme based on differential evolution (DE) is first proposed. Next, a trajectory design optimization based on multi-agent proximal policy optimization (MAPPO) is designed to minimize system energy consumption. Eventually, the superiority of CTDR is demonstrated through various simulated experiments.
AB - With various consumer electronics deployed in Internet of Vehicles (IoV), human-centric consumer in-vehicle applications (e.g., driver assistance, path planning, and healthcare system) can supply high-quality driving experience and enhance travel safety within a short time. In addition, Unmanned Aerial Vehicles (UAV) are expected to be critical to assist terrestrial vehicular networks in delivering delay-sensitive contents of services. However, due to the mutual coupling of trajectory planning of UAVs, serving the same task requests repeatedly in the same area results in wasted resources. Hence, it is challenging to supply high-quality services while ensuring energy-efficient content caching. To solve this dilemma, a content Caching scheme with Trajectory design through differential evolution and Deep Reinforcement learning (CTDR) is introduced. Specifically, a content caching scheme based on differential evolution (DE) is first proposed. Next, a trajectory design optimization based on multi-agent proximal policy optimization (MAPPO) is designed to minimize system energy consumption. Eventually, the superiority of CTDR is demonstrated through various simulated experiments.
KW - Electrical and Electronic Engineering
KW - Media Technology
U2 - 10.1109/tce.2023.3349079
DO - 10.1109/tce.2023.3349079
M3 - Journal article
VL - 70
SP - 927
EP - 938
JO - IEEE Transactions on Consumer Electronics
JF - IEEE Transactions on Consumer Electronics
SN - 0098-3063
IS - 1
ER -