Weighted Centroid Localization Algorithm: Theoretical Analysis and Distributed Implementation

Information about primary transmitter location is crucial in enabling several key capabilities in cognitive radio networks, including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. Compared to other proposed non-interactive localization algorithms, the weighted centroid localization (WCL) scheme uses only the received signal strength information, which makes it simple to implement and robust to variations in the propagation environment. In this paper we present the first theoretical framework for WCL performance analysis in terms of its localization error distribution parameterized by node density, node placement, shadowing variance, correlation distance and inaccuracy of sensor node positioning. Using this analysis, we quantify the robustness of WCL to various physical conditions and provide design guidelines, such as node placement and spacing, for the practical deployment of WCL. We also propose a power-efficient method for implementing WCL through a distributed cluster-based algorithm, that achieves comparable accuracy with its centralized counterpart.

[1]  Zhigang Cao,et al.  A Semi Range-Based Iterative Localization Algorithm for Cognitive Radio Networks , 2010, 2009 IEEE Wireless Communications and Networking Conference.

[2]  Larry J. Greenstein,et al.  Non-interactive localization of cognitive radios based on dynamic signal strength mapping , 2009, 2009 Sixth International Conference on Wireless On-Demand Network Systems and Services.

[3]  R. Salomon,et al.  Coarse-grained localization: extended analyses and optimal beacon distribution , 2005, 2005 IEEE Conference on Emerging Technologies and Factory Automation.

[4]  Michel Barbeau,et al.  Centroid Localization of Uncooperative Nodes in Wireless Networks Using a Relative Span Weighting Method , 2010, EURASIP J. Wirel. Commun. Netw..

[5]  H. Vincent Poor,et al.  Mobile element assisted cooperative localization for wireless sensor networks with obstacles , 2010, IEEE Transactions on Wireless Communications.

[6]  Mohamed-Slim Alouini,et al.  On the Energy Detection of Unknown Signals Over Fading Channels , 2007, IEEE Transactions on Communications.

[7]  Marvin K. Simon,et al.  Probability Distributions Involving Gaussian Random Variables: A Handbook for Engineers, Scientists and Mathematicians , 2006 .

[8]  J. C. Hayya,et al.  A Note on the Ratio of Two Normally Distributed Variables , 1975 .

[9]  Tarek F. Abdelzaher,et al.  Range-free localization schemes for large scale sensor networks , 2003, MobiCom '03.

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

[11]  John G. Proakis,et al.  Digital Communications , 1983 .

[12]  Danijela Cabric,et al.  Performance analysis of weighted centroid algorithm for primary user localization in cognitive radio networks , 2010, 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers.

[13]  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).

[14]  Alec Wolman,et al.  A Location-Based Management System for Enterprise Wireless LANs , 2007, NSDI.

[15]  H. Vincent Poor,et al.  Range-Free Localization with the Radical Line , 2010, 2010 IEEE International Conference on Communications.

[16]  Hongwei Zhang,et al.  GS3: scalable self-configuration and self-healing in wireless networks , 2002, PODC '02.

[17]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Ainslie,et al.  CORRELATION MODEL FOR SHADOW FADING IN MOBILE RADIO SYSTEMS , 2004 .

[19]  Jakob Salzmann,et al.  Strategies to overcome border area effects of coarse grained localization , 2009, 2009 6th Workshop on Positioning, Navigation and Communication.

[20]  Koen Langendoen,et al.  Distributed localization in wireless sensor networks: a quantitative compariso , 2003, Comput. Networks.

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

[22]  Quan Pan,et al.  AWCL: Adaptive weighted centroid target localization algorithm based on RSSI in WSN , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[23]  Mohamed F. Younis,et al.  Overlapping Multihop Clustering for Wireless Sensor Networks , 2009, IEEE Transactions on Parallel and Distributed Systems.

[24]  Makoto Takizawa,et al.  A Survey on Clustering Algorithms for Wireless Sensor Networks , 2010, 2010 13th International Conference on Network-Based Information Systems.

[25]  Xiaowei Li,et al.  Mean Shift Based Collaborative Localization with Dynamically Clustering for Wireless Sensor Networks , 2009, 2009 WRI International Conference on Communications and Mobile Computing.

[26]  Larry J. Greenstein,et al.  Sensor-assisted localization in cellular systems , 2007, IEEE Transactions on Wireless Communications.

[27]  F. Golatowski,et al.  Weighted Centroid Localization in Zigbee-based Sensor Networks , 2007, 2007 IEEE International Symposium on Intelligent Signal Processing.

[28]  P. Pérez,et al.  Tracking multiple objects with particle filtering , 2002 .