Mobile context inference using two-layered Bayesian networks for smartphones

Recently, mobile context inference becomes an important issue. Bayesian probabilistic model is one of the most popular probabilistic approaches for context inference. It efficiently represents and exploits the conditional independence of propositions. However, there are some limitations for probabilistic context inference in mobile devices. Mobile devices relatively lacks of sufficient memory. In this paper, we present a novel method for efficient Bayesian inference on a mobile phone. In order to overcome the constraints of the mobile environment, the method uses two-layered Bayesian networks with tree structure. In contrast to the conventional techniques, this method attempts to use probabilistic models with fixed tree structures and intermediate nodes. It can reduce the inference time by eliminating junction tree creation. To evaluate the performance of this method, an experiment is conducted with data collected over a month. The result shows the efficiency and effectiveness of the proposed method.

[1]  Young-Koo Lee,et al.  Semi-Markov conditional random fields for accelerometer-based activity recognition , 2010, Applied Intelligence.

[2]  Sung-Bae Cho,et al.  Modular Bayesian Networks for Inferring Landmarks on Mobile Daily Life , 2006, Australian Conference on Artificial Intelligence.

[3]  David Poole,et al.  MULTIPLY SECTIONED BAYESIAN NETWORKS AND JUNCTION FORESTS FOR LARGE KNOWLEDGE‐BASED SYSTEMS , 1993, Comput. Intell..

[4]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[5]  Jian Xun Peng,et al.  An efficient feature selection method for mobile devices with application to activity recognition , 2011, Neurocomputing.

[6]  Paolo Bellavista,et al.  Context-aware semantic discovery for next generation mobile systems , 2006, IEEE Communications Magazine.

[7]  Kristian G. Olesen,et al.  Maximal Prime Subgraph Decomposition of Bayesian Networks , 2001, FLAIRS.

[8]  Gordon Bell,et al.  MyLifeBits: a personal database for everything , 2006, CACM.

[9]  Andreas Krause,et al.  SenSay: a context-aware mobile phone , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[10]  Jukka Riekki,et al.  Context Representation and Reasoning in Pervasive Computing: a Review , 2009 .

[11]  R. G. Cowell.  Object-oriented Bayesian networks for DNA mixture analyses , 2006 .

[12]  Finn Verner Jensen,et al.  Inference in Multiply Sectioned Bayesian Networks with Extended Shafer-Shenoy and Lazy Propagation , 1999, UAI.

[13]  Miguel A. Labrador,et al.  Centinela: A human activity recognition system based on acceleration and vital sign data , 2012, Pervasive Mob. Comput..

[14]  Paul Dourish,et al.  What we talk about when we talk about context , 2004, Personal and Ubiquitous Computing.

[15]  Sung-Bae Cho,et al.  Landmark detection from mobile life log using a modular Bayesian network model , 2009, Expert Syst. Appl..

[16]  Tapio Seppänen,et al.  Bayesian approach to sensor-based context awareness , 2003, Personal and Ubiquitous Computing.

[17]  Sung-Bae Cho,et al.  Modular Bayesian Network Learning for Mobile Life Understanding , 2008, IDEAL.

[18]  Carlos Guestrin,et al.  Efficient Principled Learning of Thin Junction Trees , 2007, NIPS.

[19]  Gregory F. Cooper,et al.  A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .

[20]  Unto K. Laine,et al.  Comparison of classifiers in audio and acceleration based context classification in mobile phones , 2011, 2011 19th European Signal Processing Conference.

[21]  Patrick Robertson,et al.  Issues and Requirements for Bayesian Approaches in Context Aware Systems , 2005, LoCA.

[22]  Grigoris Antoniou,et al.  A Survey of Semantics-Based Approaches for Context Reasoning in Ambient Intelligence , 2007, AmI Workshops.

[23]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[24]  Claudio Bettini,et al.  COSAR: hybrid reasoning for context-aware activity recognition , 2011, Personal and Ubiquitous Computing.

[25]  Pierre-Henri Wuillemin,et al.  Object Oriented Bayesian Networks A Framework for Topdown Specification of Large Bayesian Networks a , 2000 .

[26]  Avi Pfeffer,et al.  Object-Oriented Bayesian Networks , 1997, UAI.

[27]  Charles F. Hockett,et al.  A mathematical theory of communication , 1948, MOCO.

[28]  Sung-Bae Cho,et al.  Predicting user Activities in the Sequence of Mobile Context for Ambient Intelligence Environment using Dynamic Bayesian Network , 2010, ICAART.

[29]  Adnan Darwiche,et al.  Inference in belief networks: A procedural guide , 1996, Int. J. Approx. Reason..

[30]  José A. Gámez,et al.  Incremental compilation of Bayesian networks , 2002, UAI.

[31]  Jani Mäntyjärvi,et al.  Managing Context Information in Mobile Devices , 2003, IEEE Pervasive Comput..

[32]  Gary M. Weiss,et al.  The Impact of Personalization on Smartphone-Based Activity Recognition , 2012, AAAI 2012.

[33]  Weihua Sheng,et al.  Motion- and location-based online human daily activity recognition , 2011, Pervasive Mob. Comput..

[34]  Surapa Thiemjarus,et al.  Accurate Activity Recognition Using a Mobile Phone Regardless of Device Orientation and Location , 2011, 2011 International Conference on Body Sensor Networks.

[35]  Jian Xun Peng,et al.  Practical automated activity recognition using standard smartphones , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications Workshops.

[36]  Kathryn B. Laskey,et al.  Network Fragments: Representing Knowledge for Constructing Probabilistic Models , 1997, UAI.

[37]  Jaeyoung Yang,et al.  Activity Recognition Based on RFID Object Usage for Smart Mobile Devices , 2011, Journal of Computer Science and Technology.

[38]  Mika Raento,et al.  ContextPhone: Platform for Context-Aware Mobile Applications , 2005 .