Deep Learning-Based Multiparametric Predictions for IoT

Muhammad Ateeq, Muhammad Khalil Afzal, Muhammad Naeem, Muhammad Shafiq and Jin-Ghoo Choi
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Muhammad Ateeq: Department of Computer Science and IT, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
Muhammad Khalil Afzal: Department of Computer Science, COMSATS University Islamabad Wah Campus, Wah Cantt 47040, Pakistan
Muhammad Naeem: Department of Electrical and Computer Engineering, COMSATS University Islamabad Wah Campus, Wah Cantt 47040, Pakistan
Muhammad Shafiq: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
Jin-Ghoo Choi: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea

Sustainability, 2020, vol. 12, issue 18, 1-12

Abstract: Wireless Sensor Networks (WSNs) and Internet of Things (IoT) often suffer from error-prone links when deployed in resource-constrained industrial environments. Reliability is a critical performance requirement of loss-sensitive applications, and Signal-to-Noise Ratio (SNR) is a key indicator of successful communications. In addition to the improvement of the physical layer through modulation and channel coding, machine learning offers adaptive solutions by configuring various communication parameters dynamically. In this paper, we apply a Deep Neural Network (DNN) to predict SNR and Packet Delivery Ratio (PDR). Analysis results based on a real dataset show that the DNN can predict SNR and PDR at the accuracy of up to 96 % and 98 % , respectively, even when trained with very small fraction (≤10%) of data. Moreover, a common subset of features turns out to be useful in predicting both SNR and PDR so as to encourage considering both metrics jointly. We may control the transmission power in the dynamic and adaptive manner when we have predictable SNR and PDR, and thus fulfill the reliability requirements with energy conservation. This can help in achieving sustainable design for the communication system.

Keywords: Internet of Things; wireless sensor networks; signal-to-noise ratio; packet delivery ratio; power conservation; deep neural networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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