A novel mobility prediction scheme for outdoor crowded scenario using Fuzzy C-means

机译：基于模糊C-均值的户外拥挤场景移动性预测新方案

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摘要

Forecasting the users movement and behavior is extremely valuable for communication networks to support the explosive mobile data in the outdoor crowded area, in the respects such as network deployment, resource allocation and mobility management. Due to the large users number and complex individual behavior, it is difficult to accurately predict the user's movement. In this paper, we take an academic campus as a study example and propose a novel mobility prediction scheme based on data mining algorithm. First, we divide the whole area into several prediction areas based on the number of mobile users, and divide the prediction time into several periods according to the scenario feature. Then, we classify the trajectories of the mobile users into groups based on Fuzzy C-means (FCM) clustering, and discover the frequent mobility patterns in each prediction area at different periods using sequence pattern mining. Finally, we determine the group for the new user and find the most matched mobility pattern to predict its future location. Simulation results show that the proposed scheme achieves a better performance compared with exiting schemes in terms of the handoff numbers and dwell time.

机译：在网络部署，资源分配和移动性管理等方面，预测用户的移动和行为对于通信网络在室外拥挤的区域中支持爆炸性移动数据非常有价值。由于用户数量众多且个人行为复杂，因此难以准确预测用户的运动。本文以某大学校园为研究案例，提出了一种基于数据挖掘算法的移动性预测方案。首先，我们根据移动用户的数量将整个区域划分为几个预测区域，然后根据场景特征将预测时间划分为几个时段。然后，我们基于模糊C均值（FCM）聚类将移动用户的轨迹分类为组，并使用序列模式挖掘在每个周期的不同区域发现频繁的移动性模式。最后，我们为新用户确定组，并找到最匹配的移动性模式以预测其未来位置。仿真结果表明，与现有方案相比，该方案在切换次数和驻留时间上均具有更好的性能。

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来源
《2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications》|2017年|1-5|共5页
会议地点 Montreal(CA)
作者
Pengbo Yang; Xi Li; Hong Ji; Heli Zhang;
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作者单位

Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, P.R. China;

Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, P.R. China;

Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, P.R. China;

Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, P.R. China;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Trajectory; Prediction algorithms; Mobile communication; Clustering algorithms; Data mining; Classification algorithms; History;

机译：轨迹;预测算法;移动通信;聚类算法;数据挖掘;分类算法;历史记录;;

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A novel mobility prediction scheme for outdoor crowded scenario using Fuzzy C-means

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