Smart probabilistic fingerprinting for WiFi-based indoor positioning with mobile devices

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• 作者：Igor Bisio ^{igor.bisio@unige.it" class="auth_mail" title="E-mail the corresponding author} ; Fabio Lavagetto ^{fabio.lavagetto@unige.it" class="auth_mail" title="E-mail the corresponding author} ; Mario Marchese ^{mario.marchese@unige.it" class="auth_mail" title="E-mail the corresponding author} ; Andrea Sciarrone ; ^{andrea.sciarrone@unige.it" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Indoor positioning ; WiFi fingerprinting ; Computational and energy efficiency ; Smartphones
• 刊名：Pervasive and Mobile Computing
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：31
• 期：Complete
• 页码：107-123
• 全文大小：1203 K

文摘

Different positioning schemes are based on the probability $p\left(o\right|l)$ to have an observation vector $o$ at a Reference Point (RP) l$l$, based on Gaussian probabilities. This paper presents an approach to speed-up the $p\left(o\right|l)$ computation without any approximation. The consequent positioning scheme is called Smart P-FP. The comparison between Traditional   (without any $p\left(o\right|l)$ acceleration) and Smart   P-FP is performed over different smartphones. The saved energy is about 90% for a large number of Access Points (APs) but is significant even with few APs: more than 86% with 3$3$ APs. The proposed $p\left(o\right|l)$ computation is beneficial to any $p\left(o\right|l)$-based positioning scheme.