Joint Forecasting-Scheduling for the Internet of Things
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Tarih
2019
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
We 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.
Açıklama
2019 IEEE Global Conference on Internet of Things, GCIoT 2019 -- 4 December 2019 through 7 December 2019 -- -- 159114
Anahtar Kelimeler
Anomaly Detection, Concept Drift, Internet of Things, Intrusion Detection, Online Machine Learning, Autoregressive moving average model, Forecasting, Gateways (computer networks), Long short-term memory, Scheduling, Auto-regressive integrated moving average, Forecasting modeling, Forecasting models, Multi layer perceptron, Network throughput, Optimal choice, Problem modeling, Traffic generation, Internet of things
