Browsing by Author "Gul, Baran Can"

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Citation - WoS: 15
Citation - Scopus: 21
Comparative Study of Forecasting Schemes for IoT Device Traffic in Machine-to-Machine Communication
(ASSOC COMPUTING MACHINERY, 2019) Mert Nakip; Baran Can Gul; Volkan Rodoplu; Cuneyt Guzelis; Gul, Baran Can; Rodoplu, Volkan; Guzelis, Cuneyt; Nakip, Mert
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 of forecasting the traffic generation patterns of individual Internet of Things (IoT) devices in Machine-to-Machine (M2M) communication. We classify IoT traffic into four classes: Fixed-Bit Periodic (FBP) Variable-Bit Periodic (VBP) Fixed-Bit Aperiodic (FBA) and Variable-Bit Aperiodic (VBA). We show that LSTM outperforms all of the other models significantly in the symmetric Mean Absolute Percentage Error (sMAPE) measure for devices in the VBP class in our simulations. Furthermore we show that LSTM has almost the same performance in this metric for the FBA class as MLP and 1-D CNN. While the training time per IoT device is the highest for LSTM all of the forecasting models have reasonable training times for practical implementation. Our results suggest an architecture in which an IoT Gateway predicts the future traffic of IoT devices in the FBP VBP and FBA classes and pre-allocates the uplink wireless channel for these classes in advance in order to alleviate the Massive Access Problem of M2M communication.
Citation - WoS: 3
Citation - Scopus: 6
Predictability of Internet of Things Traffic at the Medium Access Control Layer Against Information-Theoretic Bounds
(Institute of Electrical and Electronics Engineers Inc., 2022) Mert Nakıp; Baran Can Gul; Volkan Rodoplu; Cüneyt Güzeliş; Guel, Baran Can; Gul, Baran Can; Rodoplu, Volkan; Guzelis, Cuneyt; Nakip, Mert
Most of the existing Medium Access Control (MAC) layer protocols for the Internet of Things (IoT) model the traffic generated by each IoT device via random arrivals such as those in a Poisson process. Under this model since it is implied that IoT device traffic cannot be predicted only reactive MAC-layer protocols in which the network responds to the current traffic are viable. In contrast recent work has demonstrated that the traffic generated by an individual IoT device can be predictable thus enabling predictive network protocols at the MAC layer. In this paper we investigate information-theoretic bounds on the predictability of IoT traffic of individual devices. To this end first we compare the performance achieved by the following state-of-the-art forecasters on individual IoT device traffic: Logistic Regression Multi-Layer Perceptron (MLP) 1-Dimensional Convolutional Neural Network (1D CNN) and Long Short Term Memory (LSTM) as well as MLP under feature selection based on Analysis of Variance (ANOVA) and Auto-Correlation Function (ACF). Second we quantify the gap between the performance of these forecasters against information-theoretic bounds as follows: For IoT devices that generate a fixed number of bits at each generation instance we measure the gap between the forecasting accuracy and the information-theoretic bound established by Fano's inequality on the probability of correct prediction. Our empirical results show that existing forecasting schemes perform close to the information-theoretic bound in this case. For IoT devices that generate a variable number of bits we measure the gap between the Mean Square Error (MSE) and the estimation-theoretic counterpart to Fano's inequality. Our empirical results show that the performance of existing forecasting schemes is far from the information-theoretic bound in this case. This work motivates the machine learning community to develop forecasting schemes that approach information-theoretic bounds. Furthermore this work is expected to impact the development of predictive MAC-layer protocols that exploit these bounds. © 2022 Elsevier B.V. All rights reserved.