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Comparative Study of Forecasting Schemes for IoT Device Traffic in Machine-to-Machine Communication
(ASSOC COMPUTING MACHINERY, 2019)
We present a comparative study of Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP), 1-Dimensional Convolutional Neural Network (1-D CNN), and Long-Short Term Memory (LSTM) models on the problem ...
A Multiscale Algorithm for Joint Forecasting-Scheduling to Solve the Massive Access Problem of IoT
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020)
The massive access problem of the Internet of Things (IoT) is the problem of enabling the wireless access of a massive number of IoT devices to the wired infrastructure. In this article, we describe a multiscale algorithm ...
An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things
(Institute of Electrical and Electronics Engineers Inc., 2021)
We develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter-based, wrapper-based and ...