01413nas a2200157   4500000000100000000000100001008004100002260000900043100001700052700001900069700001800088700001500106700001900121245010300140520101200243       2021                            d  bIEEE1 aAlper Saylam1 aRifat Cikmazel1 aNur Kelesoglu1 aMert Nakip1 aVolkan Rodoplu00aEnergy-Efficient Indoor Positioning for Mobile Internet of Things Based on Artificial Intelligence3 a<p>We develop an energy-efficient indoor positioning system based on Artificial Intelligence (AI). In our system, first, at the positioning layer, a Multi-Layer Perceptron (MLP) estimates the current indoor position of an IoT device based on positioning indicators obtained from the anchors. Second, at the forecasting layer, a pair of MLPs estimate the future positions of the device based on the past position estimates obtained when the device woke up as well as the forecast positions of the device during the sleep periods. Third, the device is awakened to send a positioning beacon at intervals over which a significant displacement is predicted to occur by the forecasting layer. Our results demonstrate that our indoor positioning system saves significant energy via adaptive sleep cycles whose duration is determined by the &nbsp;prediction of a significant displacement. This work establishes a foundation for indoor positioning that utilizes AI-based positioning and trajectory forecasting.</p>