Abstract
Taking advantage of widely deployed access points (AP), WiFi fingerprint based localization is of importance in indoor internet-of-things (IOT) environments. Nevertheless, spatio-temporal variation is one of its intractable problems, indicating severely environmental dynamics and uncertainty of decision. In this case, the localization accuracy drops significantly. In this paper, we attempt to overcome effects of spatio-temporal variations from two aspects: filtering of the training data and selection of partially valuable APs for matching in test phase. The key idea is to match partial unaffected measurements with `clean' unaffected fingerprints. Bayesian framework and category model are presented for WiFi fingerprints. Two binary hidden variables with different dimensions are introduced to identify singular fingerprints and affected measurements respectively by employing expectation-maximization (EM) algorithms. EM based filter and simultaneous AP selection and localization are then proposed to obtain an optimal matching. Experimental results show that our proposed scheme greatly improves the localization accuracy in severely dynamic indoor environments.
Highlights
Over the past decade, indoor localization has drawn much attention to numerous academic and industrial researchers ([1])
Since the WiFi access point (AP) has been widely deployed for communication purpose all over the world, WiFi fingerprint based indoor localization could be executed without extra infrastructure ([2], [3]), which is very helpful for both terminals and sensor networks in internet-of-things (IOT) applications [4]
Simultaneous AP selection and localization is proposed for test data to obtain an optimal matching, where Bayesian framework and category model are presented, and AP selection is accomplished by another binary hidden variable associated with a shifting probability
Summary
Indoor localization has drawn much attention to numerous academic and industrial researchers ([1]). Since the WiFi access point (AP) has been widely deployed for communication purpose all over the world, WiFi fingerprint based indoor localization could be executed without extra infrastructure ([2], [3]), which is very helpful for both terminals and sensor networks in internet-of-things (IOT) applications [4]. WiFi fingerprint based indoor localization schemes contain two phases: training phase and testing phase. Localization in dynamic environments will be reviewed in related work. F. Zhao et al.: Probabilistic Approach for WiFi Fingerprint Localization in Severely Dynamic Indoor Environments. A filter is proposed for training dataset based on EM algorithm with a binary hidden variable to identify and remove abnormal fingerprints;. Simultaneous AP selection and localization is proposed for test data to obtain an optimal matching, where Bayesian framework and category model are presented, and AP selection is accomplished by another binary hidden variable associated with a shifting probability.
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