无线网络中的移动预测综述

王莹; 苏壮

doi:10.11959/j.issn.1000-436x.2019159

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无线网络中的移动预测综述

综述 | 更新时间：2024-06-05

- 无线网络中的移动预测综述
- Survey of mobility prediction in wireless network
- 通信学报 2019年40卷第8期页码：157-168
- 作者机构：
  
  1. 北京邮电大学信息与通信工程学院，北京 100876
  2. 北京邮电大学网络与交换技术国家重点实验室，北京 100876
- 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。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2017YFE0120600)
- DOI：10.11959/j.issn.1000-436x.2019159
  中图分类号： TP391
- 网络出版日期：2019-08，
  
  纸质出版日期：2019-08-25
- 稿件说明：
移动端阅览
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.
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. DOI： 10.11959/j.issn.1000-436x.2019159.

Ying WANG, Zhuang SU. Survey of mobility prediction in wireless network[J]. Journal on communications, 2019, 40(8): 157-168. DOI： 10.11959/j.issn.1000-436x.2019159.

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。

Abstract

Smart cities based on cloud computing

big data and artificial intelligence have become important development trends.Mobile prediction is the key technology of smart city.Firstly

in order to summarize the mobile prediction methods and its applications in wireless network

the importance and feasibility of mobile prediction were stated.And the datasets of mobile prediction were introduced.Secondly

the users’ trajectory characteristics and the method of mobile prediction were summarized and compared.Finally

the problems and challenges for mobility prediction were pointed out.

关键词

Keywords

references

ZHENG Y , CAPRA L , WOLFSON O , et al . Urban computing:concepts,methodologies,and applications [J ] . ACM Transactions on Intelligent Systems ＆ Technology , 2014 , 5 ( 3 ): 1 - 55 .

GONZÁLEZ M C , HIDALGO C A . Understanding individual human mobility patterns [J ] . Nature , 2008 , 453 ( 7196 ): 779 - 782 .

SONG C , BARABÁSI A L . Limits of predictability in human mobility [J ] . Science , 2010 , 327 ( 5968 ): 1018 - 1021 .

WANG R , PENG X , ZHANG J , et al . Mobility-aware caching for content-centric wireless networks:modeling and methodology [J ] . IEEE Communications Magazine , 2016 , 54 ( 8 ): 77 - 83 .

LU X , QU Z , LIO P , et al . Directional communication with movement prediction in mobile wireless sensor networks [J ] . Personal and Ubiquitous Computing , 2014 , 18 ( 8 ): 1941 - 1953 .

SHEVADE U , CHEN Y C , QIU L , et al . Enabling high-bandwidth vehicular content distribution [J ] .,2010. Proceedings of ACM CoNEXT , 2010 .

DAVID K , TRISTAN H , TLYA A , et al . CRAWDAD dataset dartmouth/campus [R ] .(2009-09-09)[2019-08-08 ] .

AHMAD R , LIN Z . CRAWDAD dataset rice/context [R ] .(200705-23 )[2019-08-08 ] .

KURUVATTI N P , ZHOU W , SCHOTTEN H D . Mobility prediction of diurnal users for enabling context aware resource allocation [C ] // Vehicular Technology Conference . IEEE , 2016 : 1 - 5 .

LI B , ZHANG H , LU H . User mobility prediction based on Lagrange's interpolation in ultra-dense networks [C ] // International Symposium on Personal,Indoor,and Mobile Radio Communications . IEEE , 2016 : 1 - 6 .

LV Q , QIAO Y , ANSARI N , et al . Big data driven hidden Markov model based individual mobility prediction at points of interest [J ] . IEEE Transactions on Vehicular Technology , 2017 , 66 ( 6 ): 5204 - 5216 .

GUDMUNDSSON J , VALLADARES N . A GPU approach to subtrajectory clustering using the Fréchet distance [J ] . IEEE Transactions on Parallel and Distributed Systems , 2015 , 26 ( 4 ): 924 - 937 .

JEONG J , LEE K , ABDIKAMALOV B , et al . TravelMiner:on the benefit of path-based mobility prediction [C ] // IEEE International Conference on Sensing,Communication,and Networking . IEEE , 2016 : 1 - 9 .

XU G , GAO S , DANESHMAND M , et al . A survey for mobility big data analytics for geolocation prediction [J ] . IEEE Wireless Communications , 2017 ( 99 ): 2 - 10 .

于瑞云 , 夏兴有 , 李婕 , 等 . 参与式感知系统中基于社会关系的移动用户位置预测算法 [J ] . 计算机学报 , 2015 , 38 ( 2 ): 374 - 385 .

YU R Y , XIA X Y , LI J , et al . Social-aware mobile user location prediction algorithm in participatory sensing systems [J ] . Chinese Journal of Computers , 2015 , 38 ( 2 ): 374 - 385 .

ZHENG Y , XIE X , MA W . Geo life:a collaborative social networking service among user,location and trajectory [J ] . Bulletin of the IEEE Computer Society Technical Committee on Data Engineering , 2010 , 33 ( 2 ): 32 - 39

YANG J , XU J , XU M , et al . Predicting next location using a variable order Markov model [C ] // The 5th ACM SIGSPATIAL International Workshop on GeoStreaming . ACM , 2014 : 37 - 42 .

PERERA A S , PERERA A . Passenger mobility prediction using a taxi service dataset [C ] // IEEE International Conference on Industrial and Information Systems . IEEE , 2017 : 1 - 6 .

CHENG Y , QIAO Y , YANG J . An improved Markov method for prediction of user mobility [C ] // International Conference on Network and Service Management . IEEE , 2017 : 394 - 399 .

GIDÓFALVI G , DONG F . When and where next:individual mobility prediction [C ] // The First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems . ACM , 2012 : 57 - 64 .

VAHEDIAN A , ZHOU X , TONG L , et al . Forecasting gathering events through continuous destination prediction on big trajectory data [C ] // The 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems . ACM , 2017 : 1 - 10 .

ELDAW M H S , LEVENE M , ROUSSOS G . Collective suffix tree-based models for location prediction [C ] // The 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication . ACM , 2013 : 441 - 450 .

MENZ L , HERBERTH R , LUO C , et al . An improved method for mobility prediction using a Markov model and density estimation [C ] // 2018 IEEE Wireless Communications and Networking Conference (WCNC) . IEEE , 2018 : 1 - 6 .

YAP K L , CHONG Y W . Optimized access point selection with mobility prediction using hidden Markov model for wireless network [C ] // Ninth International Conference on Ubiquitous and Future Networks . IEEE , 2017 : 38 - 42 .

HAO J , ZIMMERMANN R , MA H . GTube:geo-predictive video streaming over HTTP in mobile environments [C ] // The 5th ACM Multimedia Systems Conference . ACM , 2014 : 259 - 270 .

ZHANG C , ZHANG K , YUAN Q , et al . Gmove:group-level mobility modeling using geo-tagged social media [C ] // The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM , 2016 : 1305 - 1314 .

乔少杰 , 金琨 , 韩楠 , 等 . 一种基于高斯混合模型的轨迹预测算法 [J ] . 软件学报 , 2015 , 26 ( 5 ): 1048 - 1063 .

QIAO S J , JIN K , HAN N , et al . Trajectory prediction algorithm based on Gaussian mixture model [J ] . Journal of Software , 2015 , 26 ( 5 ): 1048 - 1063 .

JIA Y , WANG Y , JIN X , et al . Location prediction:a temporal-spatial Bayesian model [J ] . ACM Transactions on Intelligent Systems and Technology (TIST) , 2016 , 7 ( 3 ):31.

YUAN Q , ZHANG W , ZHANG C , et al . PRED:periodic region detection for mobility modeling of social media users [C ] // The Tenth ACM International Conference on Web Search and Data Mining . ACM , 2017 : 263 - 272 .

MATEKENYA D , ITO M , SHIBASAKI R , et al . Enhancing location prediction with big data:evidence from dhaka [C ] // The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing:Adjunct . ACM , 2016 : 753 - 762 .

SOMAA F , ADJIH C , EL KORBI I , et al . A Bayesian model for mobility prediction in wireless sensor networks [C ] // International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks . IEEE , 2016 : 1 - 7 .

CHEN J , MA L , XU Y . Support vector machine based mobility prediction scheme in heterogeneous wireless networks [J ] . Mathematical Problems in Engineering , 2015 ( 9 ): 1 - 10 .

YANG J , DAI C , DING Z . A scheme of terminal mobility prediction of ultra dense network based on SVM [C ] // International Conference on Big Data Analysis . IEEE , 2017 : 837 - 842 .

MUSTAFA A M , ABUBAKR O M , AHMADIEN O , et al . Mobility prediction for efficient resources management in vehicular cloud computing [C ] // International Conference on Mobile Cloud Computing,Services,and Engineering . IEEE , 2017 : 53 - 59 .

PECHER P , HUNTER M , FUJIMOTO R . Data-driven vehicle trajectory prediction [C ] // The 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation . ACM , 2016 : 13 - 22 .

JIANG R , SONG X , FAN Z , et al . Deep ROI-based modeling for urban human mobility prediction [J ] . Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies , 2018 , 2 ( 1 ):14.

JIANG X , DE SOUZA E N , PESARANGHADER A , et al . Trajectorynet:an embedded gps trajectory representation for point-based classification using recurrent neural networks [J ] . arXiv preprint,arXiv:1705.02636 , 2017 .

FAN X L , GUO L , HAN N , et al . A deep learning approach for next location prediction [C ] // The 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design . 2018 : 69 - 74 .

LIAO J , LIU T , LIU M , et al . Multi-context integrated deep neural network model for next location prediction [J ] . IEEE Access , 2018 , 6 : 21980 - 21990 .

FENG J , LI Y , ZHANG C , et al . DeepMove:predicting human mobility with attentional recurrent networks [C ] // The 2018 Conference on World Wide Web . 2018 : 1459 - 1468 .

陈少权 . 基于改进 LCSS 的移动用户轨迹相似性查询算法研究 [J ] . 移动通信 , 2017 , 41 ( 6 ): 77 - 82 .

CHEN S Q . Research on query algorithm of mobile user’s trajectory similarity based on improved LCSS [J ] . Mobile Communications , 2017 , 41 ( 6 ): 77 - 82 .

CUI H Y , YIN X L . Mining users’ mobility patterns based on apriori [C ] // 2015 4th International Conference on Mechatronics,Materials,Chemistry and Computer Engineering . 2015 : 1 - 6 .

BIN X , KUN Z , YUNCHUN Z , et al . Trajectory prediction algorithm for mobile target based on trajectory similarity [J ] . Computer Engineering , 2018 , 44 ( 9 ): 177 - 183 .

XU M , WANG D , LI J . DESTPRE:a data-driven approach to destination prediction for taxi rides [C ] // The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing . ACM , 2016 : 729 - 739 .

YING J J C , LEE W C , WENG T C , et al . Semantic trajectory mining for location prediction [C ] // The 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems . ACM , 2011 : 34 - 43 .

ROCHA C L , BRILHANTE I R , LETTICH F , et al . Tpred:a spatio-temporal location predictor framework [C ] // The 20th International Database Engineering ＆ Applications Symposium . ACM , 2016 : 34 - 42 .

XING Z , TIAN H , CHEN T , et al . Feature tendency based location prediction in LBSNs [C ] // International Conference on Advanced Communication Technology . IEEE , 2016 : 665 - 671 .

HUNG C C , PENG W C , LEE W C . Clustering and aggregating clues of trajectories for mining trajectory patterns and routes [J ] . The VLDB Journal—The International Journal on Very Large Data Bases , 2015 , 24 ( 2 ): 169 - 192 .

HADACHI A , BATRASHEV O , LIND A , et al . Cell phone subscribers mobility prediction using enhanced Markov chain algorithm [C ] // Intelligent Vehicles Symposium Proceedings . IEEE , 2014 : 1049 - 1054 .

LEI P R , SHEN T J , PENG W C , et al . Exploring spatial-temporal trajectory model for location prediction [C ] // IEEE Mobile Data Management (MDM) , 2011 : 58 - 67 .

YING J J C , LEE W C , TSENG V S . Mining geographic-temporal-semantic patterns in trajectories for location prediction [J ] . ACM Transactions on Intelligent Systems and Technology (TIST) , 2013 , 5 ( 1 ):2.

LIU X , HUANG Q , LI Z , et al . The impact of MTUP to explore online trajectories for human mobility studies [C ] // The 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility . ACM , 2017 :6.

SU C , WU Q , XIE X . Next check-in location prediction combines with collaborative filtering [J ] . Destech Transactions on Engineering and Technology Research , 2016 : 1 - 6 .

LIAN D , XIE X , ZHENG V W , et al . CEPR:a collaborative exploration and periodically returning model for location prediction [J ] . ACM Transactions on Intelligent Systems and Technology (TIST) , 2015 , 6 ( 1 ):8.

ASSAM R , SEIDL T . Context-based location clustering and prediction using conditional random fields [C ] // The 13th International Conference on Mobile and Ubiquitous Multimedia . ACM , 2014 : 1 - 10 .

ZHANG X , LI Z , WANG S , et al . Location prediction of social images via generative model [C ] // The 5th ACM on International Conference on Multimedia Retrieval . ACM , 2015 : 275 - 282 .

WICKRAMASURIYA D S , PERUMALLA C A , DAVASLIOGLU K , et al . Base station prediction and proactive mobility management in virtual cells using recurrent neural networks [C ] // Wireless and Microwave Technology Conference . IEEE , 2017 : 1 - 6 .

HU Z , LU Z , WEN X , et al . Stochastic geometry based performance analysis of delayed mobile data offloading with mobility prediction in dense IEEE 802.11 Networks [J ] . IEEE Access , 2017 , 5 : 23060 - 23068 .

VASILAKOS X , AL-KHALIDI M , SIRIS V A , et al . Mobility-based proactive multicast for seamless mobility support in cellular network environments [C ] // The Workshop on Mobile Edge Communications . ACM , 2017 : 25 - 30 .

MERAH A F , SAMARAH S , BOUKERCHE A , et al . A sequential patterns data mining approach towards vehicular route prediction in VANETs [J ] . Mobile Networks and Applications , 2013 , 18 ( 6 ): 788 - 802 .

ABANI N , BRAUN T , GERLA M . Proactive caching with mobility prediction under uncertainty in information-centric networks [C ] // The 4th ACM Conference on Information-Centric Networking . ACM , 2017 : 88 - 97 .

ZHANG F , LIU G , ZHAO B , et al . Reducing the network overhead of user mobility-induced virtual machine migration in mobile edge computing [J ] . Software:Practice and Experience , 2019 , 49 ( 4 ): 673 - 693 .

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