Three Dimensional Localization in Wireless Sensor Networks using the Adapted Multi-Lateration Technique Considering Range Measurement Errors


Kuruoglu G. S. , Erol M., Oktug S. F.

IEEE Globecom Workshops (Gc Workshops 2009), Hawaii, United States Of America, 30 November - 04 December 2009, pp.239-240 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/glocomw.2009.5360717
  • City: Hawaii
  • Country: United States Of America
  • Page Numbers: pp.239-240

Abstract

Localization play a key role in location based services such as parking assistance, location dependent advertising, people tracking, security management, etc. Usually, mobile users are the driving force of those applications and the third dimension plays an important role on the functionality of the system. Accurate localization in three dimensional space requires fast and robust algorithms. The widely recognized lateration technique uses anchor locations and distance measurements to locate a user. In real life, distance measurements are distorted due to environmental factors and it is well known that distance measurement errors affect the accuracy of the estimated location. Moreover, the complexity of the localization technique is a significant issue. Mobile users of emerging technologies such as wearable computers demand "light-weight" techniques. In this paper, we propose 3D-AML (Three Dimensional Adapted Multi-Lateration) to provide light-weight and accurate localization. 3D-AML uses the common concept of intersecting circles in two dimensional environments in form of intersecting spheres in 3D. 3D-AML also uses geometric properties to estimate the location of a sensor node. Hence, its time complexity is reasonably low by using arithmetic operations. We compare 3D-AML to the conventional multi-lateration technique of GPS. We show that the 3D-AML method has lower localization error than the multi-lateration technique for noisy measurements that are modeled with Gaussian distribution with varying standard deviations.