UAV deployment and caching scheme based on user preference prediction

被引：0

作者：

Ren J. ^{[1
]}

Tian H. ^{[1
]}

Fan S. ^{[1
]}

Lin Y. ^{[1
]}

Nie G. ^{[1
]}

Li J. ^{[2
]}

机构：

[1] State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing

[2] Academy of Broadcasting Science, National Radio and Television Administration, Beijing

来源：

Tongxin Xuebao/Journal on Communications | 2020年 / 41卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Edge caching; Similarity; Unmanned aerial vehicle; User preference prediction;

D O I：

10.11959/j.issn.1000-436x.2020104

中图分类号：

学科分类号：

摘要：

In order to design an efficient edge caching policy considering spatial heterogeneity and temporal fluctuations of users' content requests, a proactive caching scheme was proposed with UAV's deployment location design based on user preference prediction. Firstly, each user's preference characteristics were predicted based on file similarity and user similarity, and the request time and user location were also predicted when a content request occurs. Thereafter, on the basis of the predicted geographical location, request time and user preference, each UAV's deployment location and the corresponding content placement were determined by virtue of clustering method based on SOM and AGNES. Simulation results show that the proposed scheme outperforms other three comparison schemes in terms of hit ratio and transmission delay. Furthermore, the results also reveal that content preference is correlated with different user features by different weights. Accordingly, different impact weights should be matched with different user features. © 2020, Editorial Board of Journal on Communications. All right reserved.

引用

页码：1 / 13

页数：12

共 44 条

[21] FANG T, TIAN H, ZHANG X, Et al., Context-aware caching distribution and UAV deployment: a game-theoretic approach, Applied Sciences, 8, 10, pp. 1-15, (2018)
[22] LI L, XU Y, ZHANG Z, Et al., A prediction-based charging policy and interference mitigation approach in the wireless powered Internet of things, IEEE Journal on Selected Areas in Communications, 37, 2, pp. 439-451, (2019)
[23] SUN Y, XU D, NG D W K, Et al., Optimal 3D-trajectory design and resource allocation for solar-powered UAV communication systems, IEEE Transactions on Communications, 67, 6, pp. 4281-4298, (2019)
[24] ZENG Y, ZHANG R., Energy-efficient UAV communication with trajectory optimization, IEEE Transactions on Wireless Communications, 16, 6, pp. 3747-3760, (2017)
[25] BRESLAU L, CAO P, FAN L, Et al., Web caching and Zipf-like distributions: evidence and implications, International Conference on Computer Communications, (1999)
[26] HASSLINGER G, NTOUGIAS K, HASSLINGER F, Et al., Performance evaluation for new Web caching strategies combining LRU with score based object selection, Computer Networks, 125, pp. 172-186, (2017)
[27] HE S, TIAN H, LYU X C., Edge popularity prediction based on social-driven propagation dynamics, IEEE Communications Letters, 21, 5, pp. 1027-1030, (2017)
[28] HOU L, LEI L, ZHENG K, Et al., A Q-learning based proactive caching strategy for non-safety related services in vehicular networks, IEEE Internet of Things Journal, 6, 3, pp. 4512-4520, (2019)
[29] WANG R, PENG X, ZHANG J, Mobility-aware caching for content-centric wireless networks: modeling and methodology, IEEE Communications Magazine, 54, 8, pp. 77-83, (2016)
[30] RHEE I., CRAWDAD dataset

← 1 2 3 4 5 →