Localization of RFID Tags Using Stochastic Tunneling

This paper presents a novel localization scheme in the 3D wireless domain that employs cross correlation in backscattered signal power from a cluster of radio frequency identification (RFID) tags to estimate their location. Spatially co-located RFID tags, energized by a common tag reader, exhibit correlation in their received signal strength indicator (RSSI) values. Hence, for a cluster of RFID tags, the posterior distribution of their unknown radial separation is derived as a function of the measured RSSI correlations between them. The global maxima of this posterior distribution represent the actual radial separation between the RFID tags. The radial separations are then utilized to obtain location estimates of the tags. However, due to the nonconvex nature of the posterior distribution, deterministic optimization methods that are used to solve true radial separations between tags provide inaccurate results due to local maxima, unless the initial radial separation estimates are within the region of attraction of its global maximum. The proposed RFID localization algorithm called LOCalization Using Stochastic Tunneling (LOCUST) utilizes constrained simulated annealing with tunneling transformation to solve this nonconvex posterior distribution. The tunneling transformation allows the optimization search operation to circumvent or "tunnel” through ill-shaped regions in the posterior distribution resulting in faster convergence to the global maximum. Finally, simulation results of our localization method are presented to demonstrate the theoretical conclusions.

[1]  Jiming Chen,et al.  Wireless Sensor Networks Localization with Isomap , 2009, 2009 IEEE International Conference on Communications.

[2]  Samuel Kotz,et al.  Sums, products, and ratios for downton’s bivariate exponential distribution , 2006 .

[3]  F. Downton.  Bivariate Exponential Distributions in Reliability Theory , 1970 .

[4]  Robert Schöch,et al.  Shipment Localization Kit: An Automated Approach for Tracking and Tracing General Cargo , 2007, International Conference on the Management of Mobile Business (ICMB 2007).

[5]  R. Quentin Grafton,et al.  truncated normal distribution , 2012 .

[6]  K. S. Arun,et al.  Least-Squares Fitting of Two 3-D Point Sets , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Sang Woo Kim,et al.  Improving position estimation on RFID tag floor localization using RFID reader transmission power control , 2009, 2008 IEEE International Conference on Robotics and Biomimetics.

[8]  K. V. S. Rao,et al.  Phase based spatial identification of UHF RFID tags , 2010, 2010 IEEE International Conference on RFID (IEEE RFID 2010).

[9]  Alfred O. Hero,et al.  Manifold learning algorithms for localization in wireless sensor networks , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Nak Young Chong,et al.  Direction Sensing RFID Reader for Mobile Robot Navigation , 2009, IEEE Transactions on Automation Science and Engineering.

[11]  Gordon L. Stüber.  Principles of mobile communication , 1996 .

[12]  W. Wenzel,et al.  Stochastic Tunneling Approach for Global Minimization of Complex Potential Energy Landscapes , 1999 .

[13]  Sarangapani Jagannathan,et al.  Spatial Diversity in Signal Strength based WLAN Location Determination Systems , 2007, 32nd IEEE Conference on Local Computer Networks (LCN 2007).

[14]  Ying Zhang,et al.  Localization from mere connectivity , 2003, MobiHoc '03.

[15]  Suman Banerjee,et al.  The Interdomain Connectivity of PlanetLab Nodes , 2004, PAM.

[16]  N. Reid,et al.  AN OVERVIEW OF COMPOSITE LIKELIHOOD METHODS , 2011 .

[17]  Yixin Chen,et al.  Simulated annealing with asymptotic convergence for nonlinear constrained optimization , 2007, J. Glob. Optim..

[18]  Roy Want,et al.  An introduction to RFID technology , 2006, IEEE Pervasive Computing.

[19]  P.V. Nikitin,et al.  Theory and measurement of backscattering from RFID tags , 2006, IEEE Antennas and Propagation Magazine.

[20]  Raimund Seidel,et al.  The Upper Bound Theorem for Polytopes: an Easy Proof of Its Asymptotic Version , 1995, Comput. Geom..

[21]  Christer Åhlund,et al.  Port-based Multihomed Mobile IPv6: Load-balancing in Mobile Ad hoc Networks , 2007 .

[22]  Cristiano Varin,et al.  Pairwise Likelihood Inference for General State Space Models , 2008 .

[23]  Sarangapani Jagannathan,et al.  R-Factor: A New Parameter to Enhance Location Accuracy in RSSI Based Real-time Location Systems , 2009, 2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[24]  Randolph L. Moses,et al.  On optimal anchor node placement in sensor localization by optimization of subspace principal angles , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[25]  Alfred O. Hero,et al.  Distributed weighted-multidimensional scaling for node localization in sensor networks , 2006, TOSN.

[26]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[27]  B. Gidas.  Nonstationary Markov chains and convergence of the annealing algorithm , 1985 .

[28]  Jamol Pender.  The truncated normal distribution: Applications to queues with impatient customers , 2015, Oper. Res. Lett..

[29]  Shuo-Jye Wu,et al.  Bayesian inference for Rayleigh distribution under progressive censored sample , 2006 .

[30]  Xiang Ji,et al.  Sensor positioning in wireless ad-hoc sensor networks using multidimensional scaling , 2004, IEEE INFOCOM 2004.