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Mobile Information Systems
Volume 2016 (2016), Article ID 6923931, 11 pages
Research Article

Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network

Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor, Malaysia

Received 8 January 2016; Revised 18 March 2016; Accepted 9 May 2016

Academic Editor: Hua Lu

Copyright © 2016 Zahid Farid et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In indoor environments, WiFi (RSS) based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies) in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs). This model exploits machine learning, in particular Artificial Natural Network (ANN) techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic Algorithm (GA) based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications.