无线网络中的移动预测综述

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

摘要/Abstract

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。

关键词: 移动预测, 智慧城市, 数据挖掘, 无线网络, 运动趋势

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.

Key words: mobility prediction, smart city, data mining, wireless network, movement tendency

中图分类号:

TP391

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.

图/表 9

表1

图1

图2

图3

图4

图5

图6

图7

表2

参考文献 62

[1]	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.
[2]	GONZáLEZ M C , HIDALGO C A . Understanding individual human mobility patterns[J]. Nature, 2008,453(7196): 779-782.
[3]	SONG C , BARABáSI A L . Limits of predictability in human mobility[J]. Science, 2010,327(5968): 1018-1021.
[4]	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.
[5]	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.
[6]	SHEVADE U , CHEN Y C , QIU L ,et al. Enabling high-bandwidth vehicular content distribution[J].,2010. Proceedings of ACM CoNEXT, 2010.
[7]	DAVID K , TRISTAN H , TLYA A ,et al. CRAWDAD dataset dartmouth/campus[R].(2009-09-09)[2019-08-08].
[8]	AHMAD R , LIN Z . CRAWDAD dataset rice/context[R].(200705-23 )[2019-08-08].
[9]	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.
[10]	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.
[11]	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.
[12]	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.
[13]	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.
[14]	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.
[15]	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.
[16]	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
[17]	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.
[18]	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.
[19]	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.
[20]	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.
[21]	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.
[22]	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.
[23]	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.
[24]	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.
[25]	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.
[26]	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.
[27]	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.
[28]	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.
[29]	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.
[30]	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.
[31]	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.
[32]	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.
[33]	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.
[34]	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.
[35]	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.
[36]	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.
[37]	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.
[38]	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.
[39]	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.
[40]	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.
[41]	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.
[42]	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.
[43]	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.
[44]	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.
[45]	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.
[46]	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.
[47]	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.
[48]	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.
[49]	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.
[50]	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.
[51]	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.
[52]	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.
[53]	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.
[54]	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.
[55]	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.
[56]	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.
[57]	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.
[58]	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.
[59]	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.
[60]	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.
[61]	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.
[62]	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.

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

网络技术/结构	数据类型	应用
		无缝切换
LTE	基站日志	内容主动缓存流媒体智能调控
Wi-Fi/UDN	Wi-Fi日志/基站日志	AP/微基站部署无缝切换
D2D	GPS轨迹	节点选择内容主动缓存
MEC	基站日志	任务迁移
		内容主动缓存
WSN	GPS轨迹	定向通信
VENET	GPS轨迹	路线规划并线警告交通流预测