Erdem ÇakanVolkan RodopluCüneyt GüzelişRodoplu, VolkanGuzelis, CuneytCakan, Erdem2025-10-062024254266052542-66052543-153610.1016/j.iot.2024.1013412-s2.0-85203026773https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203026773&doi=10.1016%2Fj.iot.2024.101341&partnerID=40&md5=aed35fdafcf5ff52e95e3adafa8cb731https://gcris.yasar.edu.tr/handle/123456789/8136https://doi.org/10.1016/j.iot.2024.101341The Massive Access Problem of the Internet of Things stands for the access problem of the wireless devices to the Gateway when the device population in the coverage area is excessive. We develop a hybrid model called Data Fusion Integrated Network Forecasting Scheme Classifier (DFI-NFSC) using a Multi-Layer Perceptron (MLP) Decomposition architecture specifically designed to address the Massive Access Problem. We utilize our custom error metric to display throughput and energy consumption results. These results are obtained by emulating the Joint Forecasting–Scheduling (JFS) system on a single IoT Gateway and distinguishing between ARIMA LSTM and MLP forecasters of the JFS system. The outcomes indicate that the DFI-NFCS method plays a notable role in improving performance and mitigating challenges arising from the dynamic fluctuations in the diversity of device types within an IoT gateway's coverage zone. © 2024 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessArtificial Neural Network (ann), Emulation, Internet Of Things (iot), Joint Forecasting–scheduling, Massive Access, Medium Access Control (mac) Layer, Predictive NetworkArtificial Neural Network (ANN)Internet of Things (IoT)EmulationPredictive NetworkMedium Access Control (MAC) LayerJoint Forecasting-SchedulingJoint Forecasting–SchedulingMassive AccessArticle