The Joint Optimization of Caching and Content Delivery in Air-Ground Cooperation Environment

Mobile edge computing offers a promising approach for providing computation and storage services to user terminals. However, the computational resources deployed on the base stations or fixed locations are insufficient for temporary emergency scenarios. To expand mobile edge computing capacity, an a...

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Bibliographic Details
Published in:IEEE internet of things journal 2024-11, p.1-1
Main Authors: Bai, Jingpan, Zhu, Silei, Chen, Yuan, Chen, Yunhao
Format: Article
Language:English
Subjects:
Air to ground communication
Air-ground cooperation
Autonomous aerial vehicles
Caching
Content delivery
Costs
Delays
Internet of Things
Long short term memory
Optimization
Prediction algorithms
Resource management
Resource scheduling
Trajectory planning
UAV
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Summary:Mobile edge computing offers a promising approach for providing computation and storage services to user terminals. However, the computational resources deployed on the base stations or fixed locations are insufficient for temporary emergency scenarios. To expand mobile edge computing capacity, an air-ground cooperation architecture that leverages the low cost, rapid deployment, and mobility of low-altitude platform is proposed. A joint optimization strategy for air-ground cooperation caching and content delivery is introduced to reduce delays caused by limited wireless backhaul capacity, energy constraints of edge nodes in air (ENAs), and repeated content delivery. This strategy incorporates trajectory planning of UAVs, transmission power allocation, downlink bandwidth allocation, content caching, and user association. Content popularity is predicted using an LSTM network based on historical data. We employ the block coordinate descent (BCD) method to address the optimization problem and design the popularity prediction-based air-ground cooperation caching and content delivery (PP-AG3C) algorithm. Numerical simulations show that our algorithm outperforms benchmark algorithms in average delivery delay, data transmission energy, and cache hit rate. When the number of user terminals is 60, compared with PP-AG3C algorithm, The average data transmission energy consumption of TPCU-AG3C algorithm, TCU-AG3C algorithm, TC-AG3C algorithm, RT-AG3C algorithm and FT-AG3C algorithm increased by 30.79%, 49.41%, 76.70%, 152.85% and 51%, respectively.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3490612