A predictive localization algorithm based on RBF neural network for wireless sensor networks

The motion trajectory of nodes in indoor environment is relatively fixed because of the spatial constraint. In addition, mobile node usually moves according to some rules of its own. The localization error would increase when mobile nodes in indoor wireless sensor networks cannot receive the location information sent from anchor nodes due to some unknown transient disturbance. To minimize the localization error, we propose a predictive localization algorithm based on RBF neural network (PLRNN). The algorithm extracts and learns the intrinsic moving rules of mobile nodes. Through the extracted moving features, the location of mobile nodes can be predicted. Simulation results confirm that this algorithm can realize predictive localization with higher accuracy in blind period.

[1]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[2]  B. R. Badrinath,et al.  Ad hoc positioning system (APS) using AOA , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[3]  I. Benkhelifa,et al.  Speed and direction Prediction-based Localization for mobile wireless sensor networks , 2012, The 5th International Conference on Communications, Computers and Applications (MIC-CCA2012).

[4]  Xiaohua Yang,et al.  Set Pair Analysis Based on Phase Space Reconstruction Model and Its Application in Forecasting Extreme Temperature , 2013 .

[5]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[6]  Hao Wu,et al.  A Survey on Localization in Wireless Sensor Networks , 2011 .

[7]  Javad Rezazadeh,et al.  Mobile Wireless Sensor Networks Overview , 2012 .

[8]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[9]  Injong Rhee,et al.  SLAW: A New Mobility Model for Human Walks , 2009, IEEE INFOCOM 2009.

[10]  Deborah Estrin,et al.  Robust range estimation using acoustic and multimodal sensing , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[11]  Wang-Chien Lee,et al.  Prediction-based strategies for energy saving in object tracking sensor networks , 2004, IEEE International Conference on Mobile Data Management, 2004. Proceedings. 2004.

[12]  Yudong Zhang,et al.  Improved Radio Frequency Identification Indoor Localization Method via Radial Basis Function Neural Network , 2014 .

[13]  Tracy Camp,et al.  SMOOTH: a simple way to model human walks , 2010, MOCO.