A radio map self-updating algorithm based on mobile crowd sensing

• Published in: Journal of network and computer applications Vol. 194; p. 103225
• Main Authors: Liu, Xi, Cen, Jian, Hu, Huanzhong, Yu, Zongwei, Huang, Yuanxin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.11.2021
• Subjects: Radio map; Self-updating; Indoor localization; Mobile crowd sensing
• ISSN: 1084-8045; 1095-8592
• DOI: 10.1016/j.jnca.2021.103225

The high cost of maintaining radio map is a major hurdle for wide application of WLAN fingerprint-based indoor localization. The development of mobile crowd sensing provides new possibilities, however the features of normal users such as moving freely and non -professional bring new challenges. In this paper, a radio map self-updating algorithm is proposed to resolve three key problems: the localization accuracy, determination of fingerprints need to be updated, and capture of new fingerprints. First we design the localization matrix mechanism and periodic adaptive estimate algorithm to ensure the localization accuracy. Second we propose the fingerprint integrity assessment algorithm to detect the access points changed and the periodic adaptive estimate algorithm to decide the update period for each reference point. Finally we design the active fingerprint collecting mode to update the radio map efficiently. The algorithm proposed has been deployed for real-world testing over 30 days, our studies show that it detects the network changes in indoor environment correctly in 98% cases, and automatically judges the localization accuracy in 95% cases. Meanwhile, the localization accuracy is stable and improved by over 40% even after long terms of deployment, and the overhead of user terminals is reduced over 40%.