Multi-hop Mobility Prediction

详细信息    查看全文

• 作者：Zhiyong Yu ; Zhiwen Yu ; Yuzhong Chen
• 关键词：Multi ; hop mobility prediction ; Markov model ; GPS trajectories ; Crowd sensing
• 刊名：Mobile Networks and Applications
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：21
• 期：2
• 页码：367-374
• 全文大小：1,005 KB
• 参考文献：1.Zheng Y, Zhang L, Ma Z, Xie X, Ma W-Y (2011) Recommending friends and locations based on individual location history. ACM Trans Web 5:1–44. doi:10.​1145/​1921591.​1921596 CrossRef
  2.Xiong H, Zhang D, Zhang D, Gauthier V (2012) Predicting mobile phone user locations by exploiting collective behavioral patterns. In: 9th International Conference on Ubiquitous Intelligence and Computing. doi: 10.​1109/​uic-atc.​2012.​28
  3.Guo B, Chen H, Yu Z, Xie X, Huangfu S, Zhang D (2015) FlierMeet: a mobile crowdsensing system for cross-space public information reposting, tagging, and sharing. IEEE Trans Mob Comput 14:2020–2033. doi:10.​1109/​tmc.​2014.​2385097 CrossRef
  4.Burke JA, Estrin D, Hansen M, Parker A. Ramanathan N, Reddy S, Srivastava MB (2006) Participatory sensing, WSW’06 at SenSys’06, October 31, Boulder, Colorado, USA
  5.Resch B, Calabrese F, Biderman A, Ratti C (2008) An approach towards real-time data exchange platform system architecture. In: 2008 I.E. International Conference on Pervasive Computing and Communications (PerCom). IEEE, pp 153–159
  6.Qu Y, Yu Z, Zheng X (2015) Buy4Me: a delivery system via mobility prediction based on mobile crowd sensing. In: 7th International Symposium on UbiCom Frontiers - Innovative Research, Systems and Technologies, August 10–14, Beijing, China
  7.Yu Z, Wang H, Bin Guo GT, Mei T (2015) Supporting serendipitous social interaction using human mobility prediction. IEEE Trans Hum Mach Syst. doi:10.​1109/​thms.​2015.​2451515
  8.Carroll JM, Neale DC, Isenhour PL, Rosson MB, McCrickard DS (2003) Notification and awareness: synchronizing task-oriented collaborative activity. Int J Hum Comput Stud 58:605–632. doi:10.​1016/​S1071-5819(03)00024-7 CrossRef
  9.Ma H, Zhao D, Yuan P (2015) Opportunities in mobile crowd sensing. IEEE Commun Mag 52:29–35. doi:10.​1109/​MCOM.​2014.​6871666 CrossRef
  10.Pirozmand P, Wu G, Jedari B, Xia F (2014) Human mobility in opportunistic networks: characteristics, models and prediction methods. J Netw Comput Appl 42:45–58. doi:10.​1016/​j.​jnca.​2014.​03.​007 CrossRef
  11.Liu GY, Gerald MQ (1995) A predictive mobility management algorithm for wireless mobile computing and communications. In: IEEE International Conference on Universal Personal Communications, pp 268–272
  12.Liu T, Bahl P, Chlamtac I (1998) Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks. IEEE J Sel Areas Commun 16:922–936. doi:10.​1109/​49.​709453 CrossRef
  13.Xu B, Wolfson O (2003) Time-series prediction with applications to traffic and moving objects databases. In: MobiDe’03. ACM Press, New York, pp 56–60
  14.Lin M, Hsu W-J (2014) Mining GPS data for mobility patterns: a survey. Pervasive Mob Comput 12:1–16. doi:10.​1016/​j.​pmcj.​2013.​06.​005 CrossRef
  15.Song L, Kotz D, Jain R, He X (2006) Evaluating next-cell predictors with extensive Wi-Fi mobility data. IEEE Trans Mob Comput 5:1633–1649. doi:10.​1109/​TMC.​2006.​185 CrossRef
  16.Mathew W, Raposo R, Martins B (2012) Predicting future locations with hidden Markov models. In: UbiComp’12. ACM Press, New York, pp 911–918
  17.Baumann P, Kleiminger W, Santini S (2013) The influence of temporal and spatial features on the performance of next-place prediction algorithms. In: ACM international joint conference on Pervasive and ubiquitous computing (UbiComp’13). doi: 10.​1145/​2493432.​2493467
  18.Cho S-B (2015) Exploiting machine learning techniques for location recognition and prediction with smartphone logs. Neurocomputing. doi:10.​1016/​j.​neucom.​2015.​02.​079
  19.McInerney J, Zheng J, Rogers A, Jennings NR (2013) Modelling heterogeneous location habits in human populations for location prediction under data sparsity. In: ACM international joint conference on pervasive and ubiquitous computing, pp 469–478
  20.Do TMT, Gatica-Perez D (2012) Contextual conditional models for smartphone-based human mobility prediction. In: UbiComp’12. ACM Press, USA, pp 163
  21.Scellato S, Musolesi M, Mascolo C, Latora V, Campbell AT (2011) NextPlace: a spatio-temporal prediction framework for pervasive systems. In: Pervasive Computing. Springer, Berlin Heidelberg, pp 152–169
  22.Noulas A, Scellato S, Lathia N, Mascolo C (2012) Mining user mobility features for next place prediction in location-based services. In: IEEE 12th International Conference on Data Mining. doi: 10.​1109/​icdm.​2012.​113
  23.Monreale A, Pinelli F, Trasarti R, Giannotti F (2009) WhereNext: a location predictor on trajectory pattern mining. In: KDD ‘09. ACM Press, New York, pp 637–646
  24.Chon Y, Shin H, Talipov E, Cha H (2012) Evaluating mobility models for temporal prediction with high-granularity mobility data. In: IEEE International Conference on Pervasive Computing and Communications. doi: 10.​1109/​percom.​2012.​6199868
  25.Krumm J, Horvitz E (2006) Predestination: inferring destinations from partial trajectories. In: Pervasive computing. Springer, Berlin Heidelberg, pp 243–260
  26.Alvarez-Garcia JA, Ortega JA, Gonzalez-Abril L, Velasco F (2010) Trip destination prediction based on past GPS log using a Hidden Markov Model. Expert Syst Appl 37:8166–8171. doi:10.​1016/​j.​eswa.​2010.​05.​070 CrossRef
  27.Salzberg SL (1994) C4.5: programs for machine learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993. Mach Learn 16:235–240. doi:10.​1007/​BF00993309 MathSciNet
  28.McInerney J, Zheng J, Rogers A, Jennings NR (2013) Modelling heterogeneous location habits in human populations for location prediction under data sparsity. In: UbiComp’13. ACM Press, New York, pp 469
  
• 作者单位：Zhiyong Yu (1)
  Zhiwen Yu (2)
  Yuzhong Chen (1)
  
  1. Fuzhou University, Fuzhou, China
  2. Northwestern Polytechnical University, Xi’an, China
  
• 刊物类别：Computer Science
• 刊物主题：Computer Communication Networks
  Electronic and Computer Engineering
  Business Information Systems
  
• 出版者：Springer Netherlands
• ISSN：1572-8153

文摘

With the occurrence of large-scale human trajectories, which imply spatial and temporal patterns, the subject of mobility prediction has been widely studied. A number of approaches are proposed to predict the next location of a user. In this paper, we expect to lengthen the temporal dimension of prediction results beyond one hop. To predict the future locations of a user at every time unit within a specified time, we propose a Markov-based multi-hop mobility prediction (Markov–MHMP) algorithm. It is a hybrid approach that considers multiple factors including personal habit, weekday similarity, and collective behavior. On a GPS dataset, our approach performs prediction better than baseline and state-of-the-art approaches under several evaluation criteria.