Joint Forecasting-Scheduling for the Internet of Things

2019 IEEE Global Conference on Internet of Things, GCIoT 2019 -- 4 December 2019 through 7 December 2019 -- -- 159114We present a joint forecasting-scheduling (JFS) system, to be implemented at an IoT Gateway, in order to alleviate the Massive Access Problem of the Internet of Things. The existing proposals to solve the Massive Access Problem model the traffic generation pattern of each IoT device via random arrivals. In contrast, our JFS system forecasts the traffic generation pattern of each IoT device and schedules the transmissions of these devices in advance. The comparison of the network throughput of Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP) and Long-Short Term Memory (LSTM) forecasting models reveals that the optimal choice of the forecasting model for JFS depends heavily on the proportions of distinct IoT device classes that are present in the network. Simulations show that our JFS system scales up to 1000 devices while achieving a total execution time under 1 second. This work opens the way to the design of scalable joint forecasting-scheduling solutions at IoT Gateways. © 2019 IEEE.Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK: 118E277ACKNOWLEDGMENT This work was supported by TÜBITAK (Scientific and Technological Research Council of Turkey) under the 1001 Program Grant # 118E277.This work was supported by TUBITAK (Scientific and Technological Research Council of Turkey) under the 1001 Program Grant # 118E277