Mert NakıpAlperen HelvaCüneyt GüzelişVolkan RodopluGuzelis, CuneytRodoplu, VolkanHelva, AlperenNakip, Mert2025-10-062021978166544431610.1109/WF-IoT51360.2021.95956632-s2.0-85119890461https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119890461&doi=10.1109%2FWF-IoT51360.2021.9595663&partnerID=40&md5=b62493ee21fc131aaa2ea089abc8f2eehttps://gcris.yasar.edu.tr/handle/123456789/8958https://doi.org/10.1109/WF-IoT51360.2021.9595663We develop a novel methodology that discovers the relationship between the forecasting error and the performance of the application that utilizes the forecasts. In our methodology an Artificial Neural Network (ANN) learns this relationship while the forecasting error is kept inside a subspace of the entire space of forecasting errors during training. We apply our methodology to the case of Joint Forecasting-Scheduling (JFS) for the Internet of Things (IoT). Our results hold potential to improve the performance of JFS in next-generation networks and can be applied to a much wider range of problems beyond IoT. © 2021 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessArtificial Neural Network (ann), Forecasting, Internet Of Things (iot), Machine-to-machine (m2m) Communication, Massive Access Problem, Scheduling, Errors, Forecasting, Internet Of Things, Neural Networks, Next Generation Networks, Artificial Neural Network, Forecasting Error, Internet Of Thing, Learn+, Machine-to-machine (m2m), Machine-to-machine (m2m) Communication, Massive Access Problem, Novel Methodology, Performance, Subspace Based, SchedulingErrors, Forecasting, Internet of things, Neural networks, Next generation networks, Artificial neural network, Forecasting error, Internet of thing, Learn+, Machine-to-machine (M2M), Machine-to-machine (M2M) communication, Massive access problem, Novel methodology, Performance, Subspace based, SchedulingMassive Access ProblemSchedulingArtificial Neural Network (ANN)Machine-to-Machine (M2M) CommunicationForecastingInternet of Things (IoT)Conference Object