Journal
2018 IEEE 15TH INTERNATIONAL CONFERENCE ON MOBILE AD HOC AND SENSOR SYSTEMS (MASS)
Volume -, Issue -, Pages 166-174Publisher
IEEE
DOI: 10.1109/MASS.2018.00037
Keywords
Indoor Localization; Wi-Fi Fingerprint; RSSI; Deep Reinforcement Learning; Deep Q-Network; Dynamic Environment
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The location-based services for Internet of Things (IoTs) have attracted extensive research effort during the last decades. Wi-Fi fingerprinting with received signal strength indicator (RSSI) has been widely adopted in vast indoor localization systems due to its relatively low cost and the potency for high accuracy. However, the fluctuation of wireless signal resulting from environment uncertainties leads to considerable variations on RSSIs, which poses grand challenges to the fingerprintbased indoor localization regarding positioning accuracy. In this paper, we propose a top-down searching method using a deep reinforcement learning agent to tackle environment dynamics in indoor positioning with Wi-Fi fingerprints. Our model learns an action policy that is capable to localize 75% of the targets in an area of 25000m(2) within 0.55m.
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