Research Article
Improving Positioning Accuracy Using WLAN Optimization for Location Based Services and Cognitive Radio Networks
@INPROCEEDINGS{10.1007/978-3-030-93398-2_54, author={Sohaib Bin Altaf Khattak and Min Jia and Qing Guo and Xuemai Gu}, title={Improving Positioning Accuracy Using WLAN Optimization for Location Based Services and Cognitive Radio Networks}, proceedings={Wireless and Satellite Systems. 12th EAI International Conference, WiSATS 2021, Virtual Event, China, July 31 -- August 2, 2021, Proceedings}, proceedings_a={WISATS}, year={2022}, month={1}, keywords={Access point Cognitive radios Geo-location database Indoor localization Location based services WLAN optimization}, doi={10.1007/978-3-030-93398-2_54} }- Sohaib Bin Altaf Khattak
Min Jia
Qing Guo
Xuemai Gu
Year: 2022
Improving Positioning Accuracy Using WLAN Optimization for Location Based Services and Cognitive Radio Networks
WISATS
Springer
DOI: 10.1007/978-3-030-93398-2_54
Abstract
Positioning information not only benefits the localization systems but also improve the performance of geo-location based cognitive radio (CR) networks. Most researchers focus on other aspects of CR and databases but rarely discuss the fact of how the positioning information can influence the performance of CR systems in indoor environments. WLAN is the most common technology used for indoor positioning. Optimization of WLAN access points (APs) can enhance accuracy of the localization systems. In this paper, we present an optimization algorithm for WLAN localization system. The proposed scheme estimates the optimal density of the APs required to meet the coverage demands and optimize their deployment to enhance the localization accuracy. One of our main contributions is the APs hearability-based reference points (RPs) clustering technique. Its uniqueness lies in the fact that not all installed APs participate in the localization process for all RPs. Finally, we analyze the variables governing the optimization process and the trade-off between cost, computation, and accuracy. Extensive simulations are conducted to validate the effectiveness of our algorithm. Our approach reduces the mean positioning error by 25% and the maximum error by 44% compared to the previous algorithm’s performance.
