A Distance-Based Maximum Likelihood Estimation Method for Sensor Localization in Wireless Sensor Networks

Node localization is an important supporting technology in wireless sensor networks (WSNs). Traditional maximum likelihood estimation based localization methods (MLE) assume that measurement errors are independent of the distance between the anchor node and a target node. However, such an assumption may not reflect the physical characteristics of existing measurement techniques, such as the widely used received signal strength indicator. To address this issue, we propose a distance-based MLE that considers measurement errors that depend on distance values in this paper. The proposed distance-based MLE is formulated as a complicated nonlinear optimization problem. An exact solution is developed based on first-order optimal condition to improve the efficiency of search. In addition, a two-dimensional search method is also presented. Simulation experiments are performed to demonstrate the effectiveness of this localization. The simulation results show that the distance-based localization method has better localization accuracy compared to other range-based localization methods.

[1]  Azzedine Boukerche,et al.  Localization in time and space for wireless sensor networks: A Mobile Beacon approach , 2008, 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks.

[2]  Ang Gao,et al.  Cosine Theorem-based DV-Hop Localization Algorithm in Wireless Sensor Networks , 2011 .

[3]  Lirui Zhang,et al.  A novel D-S based secure localization algorithm for wireless sensor networks , 2014, Secur. Commun. Networks.

[4]  Kui Wu,et al.  Sensor localization with Ring Overlapping based on Comparison of Received Signal Strength Indicator , 2004, 2004 IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE Cat. No.04EX975).

[5]  Kuo-Feng Ssu,et al.  An Energy Efficient Protocol for Target Localization in Wireless Sensor Networks , 2009 .

[6]  Juan Carlos Augusto,et al.  Management of Uncertainty and Spatio-Temporal Aspects for Monitoring and Diagnosis in a Smart Home , 2008 .

[7]  Deborah Estrin,et al.  GPS-less low-cost outdoor localization for very small devices , 2000, IEEE Wirel. Commun..

[8]  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).

[9]  Rajesh K. Gupta,et al.  Sensor localization with deterministic accuracy guarantee , 2011, 2011 Proceedings IEEE INFOCOM.

[10]  Boon-Hee Soong,et al.  A New Lower Bound on Range-Free Localization Algorithms in Wireless Sensor Networks , 2011, IEEE Communications Letters.

[11]  D. Herrero,et al.  Range-only fuzzy Voronoi-enhanced localization of mobile robots in wireless sensor networks , 2011, Robotica.

[12]  Srdjan Capkun,et al.  Secure Location Verification with Hidden and Mobile Base Stations , 2008, IEEE Transactions on Mobile Computing.

[13]  Ismail Güvenç,et al.  A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques , 2009, IEEE Communications Surveys & Tutorials.

[14]  Di Wu,et al.  Localization Algorithms for Wireless Sensor Retrieval , 2010, Comput. J..

[15]  Yan Guo,et al.  Localization of Wireless Sensor Network Based on Genetic Algorithm , 2013, Int. J. Comput. Commun. Control.

[16]  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).