A Multilateration-based Localization Scheme for Adhoc Wireless Positioning Networks Used in Information-oriented Construction

We have developed an adhoc wireless positioning network (AWPN) scheme for information-oriented construction that can temporarily and easily provide a positioning area in both indoor and outdoor environments. The particular requirements of AWPNs are easy deployment and easy configuration because the network should be removed on completion of construction and must frequently accompany the active construction section as it shifts from area to area. In this paper, we propose a localization scheme based on the multilateration method to fulfill the requirements of such AWPNs. The proposed scheme locally and dynamically calibrates the attenuation coefficient defined in the path loss model for radio wave propagation in order to reflect local, fresh propagation characteristics. The results of evaluations conducted on the efficacy of our proposed scheme indicate that it reduces the average error by 30% relative to the conventional approach.

[1]  Tian He,et al.  StarDust: a flexible architecture for passive localization in wireless sensor networks , 2006, SenSys '06.

[2]  Bill N. Schilit,et al.  Place Lab: Device Positioning Using Radio Beacons in the Wild , 2005, Pervasive.

[3]  Yunhao Liu,et al.  LANDMARC: Indoor Location Sensing Using Active RFID , 2004, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[4]  Dan Komosny,et al.  Multilateration and Flip Ambiguity Mitigation in Ad-hoc Networks , 2012 .

[5]  Sipra Das Bit,et al.  Localization with enhanced location accuracy using RSSI in WSN , 2011, 2011 Fifth IEEE International Conference on Advanced Telecommunication Systems and Networks (ANTS).

[6]  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).

[7]  Qiang Yang,et al.  Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation , 2008, IEEE Transactions on Mobile Computing.

[8]  Isao Yamada,et al.  An Indoor Position Estimation Method by Maximum Likelihood Algorithm Using Received Signal Strength , 2008 .

[9]  PROPAGATION DATA AND PREDICTION METHODS FOR THE PLANNING OF INDOOR RADIOCOMMUNICATION SYSTEMS AND RADIO LOCAL AREA NETWORKS IN THE FREQUENCY RANGE 900 MHz TO 100 GHz , 1997 .

[10]  Mikkel Baun Kjærgaard.  A Taxonomy for Radio Location Fingerprinting , 2007, LoCA.

[11]  José D. P. Rolim,et al.  Multilateration: Methods For Clustering Intersection Points For Wireless Sensor Networks Localization With Distance Estimation Error , 2012, ArXiv.

[12]  Stefano Chessa,et al.  A Novel Approach to Indoor RSSI Localization by Automatic Calibration of the Wireless Propagation Model , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.