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This study presents a new method for estimating the physical distance between two locations using Wi-Fi signals from APs observed by Wi-Fi signal receivers such as smartphones. We assume that a Wi-Fi signal strength vector is observed at location A and another Wi-Fi signal strength vector is observed at location B. With these two Wi-Fi signal strength vectors, we attempt to estimate the physical distance between locations A and B. In this study, we estimate the physical distance based on supervised machine learning and do not use labeled training data collected in an environment of interest. Note that, because signal propagation is greatly affected by obstacles such as walls, precisely estimating the distance between locations A and B is difficult when there is a wall between locations A and B. Our method first estimates whether or not there is a wall between locations A and B focusing on differences in signal propagation properties between 2.4 GHz and 5 GHz signals, and then estimates the physical distance using a neural network depending on the presence of walls. Because our approach is based on Wi-Fi signal strengths and does not require a site survey in an environment of interest, we believe that various context-aware applications can be easily implemented based on the distance estimation technique such as low-cost indoor navigation, the analysis and discovery of communities and groups, and Wi-Fi geo-fencing. Our experiment revealed that the proposed method achieved an MAE of about 3-4 meters and the performance is almost identical to an environment-dependent method, which is trained on labeled data collected in the same environment.


Tomoya Nakatani, Takuya Maekawa, Masumi Shirakawa, and Takahiro Hara: Estimating The Physical Distance Between Two Locations With Wi-Fi Received Signal Strength Information Using Obstacle-aware Approach, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Vol. 2, Issue 3, No. 130 (Sept. 2018). (Presented at UbiComp 2018)