Alper SaylamNur KelesogluRifat Orhan CikmazelMert NakıpVolkan RodopluSaylam, AlperCikmazel, Rifat OrhanRodoplu, VolkanKelesoglu, NurNakip, MertM.S. Obaidat , S. Bilgen , K.-F. Hsiao , P. Nicopolitidis , S. Oktug , Y. Guo2025-10-062021978166544913710.1109/CITS52676.2021.96182312-s2.0-85123780230https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123780230&doi=10.1109%2FCITS52676.2021.9618231&partnerID=40&md5=1868dfbb2c2ed208ae6a19f99068389ehttps://gcris.yasar.edu.tr/handle/123456789/9044https://doi.org/10.1109/CITS52676.2021.9618231We develop an algorithm called "Dynamic Positioning Interval based on Reciprocal Forecasting Error (DPIRFE)"for energy-efficient mobile Internet of Things (IoT) Indoor Positioning (IP). In contrast with existing IP algorithms DPIRFE forecasts the future trajectory of a mobile IoT device by using machine learning and dynamically adjusts the positioning interval based on the reciprocal instantaneous forecasting error thereby dynamically trading off transmit energy consumption against forecasting error. We compare the performance of DPIRFE with respect to the total transmit energy consumption and the average forecasting error against Constant Positioning Interval (CPI) and Positioning Interval based on Displacement (PID) algorithms. Our results show that DPI-RFE significantly outperforms both of these benchmark algorithms with respect to transmit energy consumption while achieving a competitive average forecasting error performance. These results open the way to the design of machine learning based trajectory forecasting algorithms that can be utilized for energy-efficient positioning in next-generation wireless networks. © 2022 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessArtificial Intelligence (ai), Energy-efficient, Indoor Positioning, Internet Of Things (iot), Machine Learning, Mobility Prediction, Benchmarking, Energy Utilization, Errors, Forecasting, Indoor Positioning Systems, Internet Of Things, Internet Protocols, Learning Algorithms, Machine Learning, Artificial Intelligence, Average Forecasting Error, Energy Efficient, Energy-consumption, Error Algorithms, Forecasting Error, Indoor Positioning, Internet Of Thing, Mobile Internet, Mobility Predictions, Energy EfficiencyBenchmarking, Energy utilization, Errors, Forecasting, Indoor positioning systems, Internet of things, Internet protocols, Learning algorithms, Machine learning, Artificial intelligence, Average forecasting error, Energy efficient, Energy-consumption, Error algorithms, Forecasting error, Indoor positioning, Internet of thing, Mobile Internet, Mobility predictions, Energy efficiencyEnergy-efficientInternet of Things (IoT)Mobility PredictionArtificial Intelligence (AI)Indoor PositioningMachine LearningArtificial I Ntelligence (AI)Conference Object