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In this article, we propose a new method for automatically updating a Wi-Fi indoor positioning model on a cloud server by employing uploaded sensor data obtained from the smartphone sensors of a specific user who spends a lot of time in a given environment (e.g., a worker in the environment). In this work, we attempt to track the user with pedestrian dead reckoning techniques, and at the same time we obtain Wi-Fi scan data from a mobile device possessed by the user. With the scan data and the estimated coordinates uploaded to a cloud server, we can automatically create a pair consisting of a scan and its corresponding indoor coordinates during the user's daily life and update an indoor positioning model on the server by using the information. With this approach, we try to cope with the instability of Wi-Fi-based positioning methods caused by changing environmental dynamics, that is, layout changes and moving or removal of Wi-Fi access points. Therefore, ordinary users (e.g., customers) who do not have rich sensors can benefit from the continually updating positioning model. ACM Reference Format:Daisuke Taniuchi and Takuya Maekawa. 2015. Automatic update of indoor location fingerprints with pedestrian dead reckoning.

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