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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 ...
Joint Forecasting-Scheduling for the Internet of Things
(2019 IEEE Global Conference on Internet of Things, GCIoT 2019, 2019)
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 ...
Development of a Multi-Sensor Fire Detector Based on Machine Learning Models [Makine Öǧrenmesi Modellerine Dayali Çok Sensörlö Bir Yangin Algilayicisi Geliştirilmesi]
(Proceedings - 2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019, 2019)
This paper proposes a method to reduce false positive fire alarms by fusing data from different sensors using a specific machine learning model. We design an electronic circuit with 6 sensors to detect 7 physical sensory ...
Comparative study of forecasting schemes for IoT device traffic in machine-to-machine communication
(ACM International Conference Proceeding Series, 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 ...
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