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Multi-Sensor Fire Detector based on Trend Predictive Neural Network
(IEEE, 2019)
In this paper, we propose a Trend Predictive Neural Network (TPNN) model, which uses the sensor data and the trend of that data in order to classify the fire situation. We implemented TPNN for data of multi-sensor fire ...
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 ...
Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection
(Institute of Electrical and Electronics Engineers Inc., 2021)
We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings. The rTPNN model significantly differs from the ...
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 ...