通信学报 ›› 2019, Vol. 40 ›› Issue (8): 157-168.doi: 10.11959/j.issn.1000-436x.2019159
王莹1,2,苏壮1,2
修回日期:
2019-08-08
出版日期:
2019-08-25
发布日期:
2019-08-30
作者简介:
王莹(1976- ),女,陕西西安人,博士,北京邮电大学教授、博士生导师,主要研究方向为无线资源管理与优化、5G关键技术、物联网技术等。|苏壮(1994- ),男,河南许昌人,北京邮电大学硕士生,主要研究方向为景区路径规划及景点推荐算法。
基金资助:
Ying WANG1,2,Zhuang SU1,2
Revised:
2019-08-08
Online:
2019-08-25
Published:
2019-08-30
Supported by:
摘要:
基于云计算、大数据和人工智能的智慧城市是未来重要的发展趋势,移动预测技术是智慧城市重点关注的技术。为总结目前的移动预测方法以及各种方法在无线网络的应用,首先阐述了移动预测的重要性和可行性,介绍了移动预测的数据分类及获取,然后总结和对比了移动预测中用户的轨迹特征和移动预测方法,最后指出了移动预测面对的问题和挑战。
中图分类号:
王莹,苏壮. 无线网络中的移动预测综述[J]. 通信学报, 2019, 40(8): 157-168.
Ying WANG,Zhuang SU. Survey of mobility prediction in wireless network[J]. Journal on Communications, 2019, 40(8): 157-168.
表1
4种常用移动预测数据源对比"
获取方式 | 精度范围 | 特点 | 数据集 |
GPS | 约10 m全球 | 室外比较准确,室内容易出现轨迹缺失;设备耗能高;采样密集 | GeoLife Dataset |
T-Drive Dataset | |||
Bike Sharing Dataset | |||
Taxi Service Dataset | |||
Phonetic Dataset | |||
Synthetic Dataset | |||
Epfl Mobility Dataset | |||
Wi-Fi | 30~100 mWi-Fi覆盖区域 | 精度适中,覆盖较密集 | ETH Zurich Dataset |
Dartmouth Campus Dataset | |||
Yonsei Lifemap Dataset | |||
GSM | 100~500 m 基站覆盖区域 | 定位误差大,处理简单 | Rice Context Dataset |
APP签到 | 约10 m全球 | 终端启动APP时通过GPS采集,数据不连续,稀疏 | Gowalla Dataset |
Foursquare Dataset | |||
BrightKite Dataset |
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