Elman-Based Secure Data Transmission Quality Prediction for Complex IoT Networks

Yin, Fagen

doi:https://doi.org/10.1155/2021/4132622

Mobile Information Systems

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Abstract Introduction Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

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Artificial Intelligence for Next-Generation Wireless Networks

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Research Article | Open Access

Volume 2021 | Article ID 4132622 | https://doi.org/10.1155/2021/4132622

Elman-Based Secure Data Transmission Quality Prediction for Complex IoT Networks

Fagen Yin¹

Academic Editor: Qinglin Zhao

Received18 Jun 2021

Revised28 Jul 2021

Accepted17 Aug 2021

Published26 Aug 2021

Abstract

The information age has brought earth-shaking changes. For interconnection of all things, the data transmission has widely employed the Internet of Things (IoT). The IoT transmission faces complex environments. The secure data transmission is very important for mobile IoT networks. The secure data transmission quality prediction is investigated for mobile IoT networks. The probability of strictly positive secrecy capacity (SPSC) is used to evaluate the secure data transmission quality, and the expressions are first derived. Then, employing Elman network, a secure data transmission quality intelligent prediction approach is proposed. The extensive simulations are run to evaluate the proposed approach. The simulation results show that the Elman-based approach can achieve a higher quality precision than other methods. The Elman-based approach also can achieve a lower time complexity.

1. Introduction

With the explosive growth of mobile applications, Internet of things (IoT) networks are widely used to transmit data [1]. The fifth generation (5G) mobile communication also has been widely used in mobile IoT networks [2, 3]. Different 5G applications widely use sea-land-air mobile communication networks [4, 5]. The global and diversified application will provide quick and convenient services for IoT users. However, due to IoT mobility and the diversity of IoT networks, the physical layer security (PLS) of 5G mobile IoT networks is facing many challenges [6].

PLS of 5G IoT networks is a research hot spot [7]. Low-complexity schemes for IoT PLS were presented in [8]. In [9], power control mechanism and antenna transmission scheme were used to realize the secure data transmission in cognitive wiretap networks. Considering the mobile healthcare networks, Xu et al. [10] investigated the PLS performance using the deep learning method. In [11], the authors used compressed sensing and cooperative schemes to achieve the secure transmission. Considering the user and relay selection, Fan et al. [12] analyzed two criteria and investigated the achievable PLS performance. The authors of [13] analyzed the upper and lower bounds on PLS performance over dependent fading channels.

The IoT data transmission faces a wide variety of scenarios and complex environments. The PLS issue is more and more serious. However, predicting and evaluating the secure data transmission quality are very difficult. Recently, machine learning techniques are applied in 5G wireless communications [14, 15]. In medical IoT, support vector machine (SVM) model was used to train data privacy [16]. High-performance visual tracking was achieved by an extreme learning machine (ELM) model in [17]. In [18], general regression (GR) model was used to evaluate the video transmission quality. The radial basis function (RBF) network was optimized to reconstruct the image in [19].

The studies of secure data transmission quality prediction are rare. So, our paper investigates the secure data transmission quality prediction of mobile IoT networks. The main contributions are given as follows.(1)With amplify-and-forward (AF) relaying scheme, we use SPSC to evaluate secure data transmission quality and derive the exact expressions.(2)To realize real-time analysis of secure data transmission quality, we propose a secure data transmission quality prediction approach based on the Elman neural network. The proposed approach is compared with ELM, GR, and RBF methods.(3)Through the extensive simulations, we verify the derived results. Compared with different methods, the quality assessment effect of Elman-based approach is better, and time complexity is lower.

2. The IoT System Model

The system has a mobile source (S), mobile destination (D), mobile eavesdropper (E), and mobile relay (R). Figure 1 shows the system model.

First, MR receives the signal r_SR aswhere is Gaussian noise.

In the second time slot, D and E receive the signals r_Rk, k ∈ {D, E}, as

The received SNR W_SRk is given aswhere

W_SRk is very complex. We approximate W_SRk as [22]

Bloch et al. [23] give the instantaneous secrecy capacity as

3. Secure Data Transmission Quality Analysis

The probability of SPSC F_SPSC is used to evaluate the secure data transmission quality. We will give the analysis.

According to the (6), F_SPSC is given as

With the help of [24], we obtain the PDF and CDF of W_SRAk as follows:

Substituting (8) and (9) into (7), F_SPSC is expressed as

4. Secure Data Transmission Quality Prediction Approach

4.1. Data Sets

T_i = (X_i, y_i). The input X_i includes 5 indicators. X_i is given as

The output y_i is the SPSC. By using (11), the corresponding y_i can be obtained.

4.2. Network Design

Figure 2 shows the Elman neural network [25].

4.3. Predictive Evaluation

For PP testing data, MSE and AE are used to evaluate the prediction effect:

5. Numerical Results

In this section, E = 1 and μ = W_RD/W_RE (in decibels).

With parameters in Table 1, we evaluate the SPSC performance with = 10 dB in Figure 3. Simulation results show the following: (1) increasing u improves the SPSC performance; (2) for Nakagami channels, the secure data transmission quality is the best. This is because a higher u improves the S ⟶ R⟶ D channel while degrading the S⟶R⟶E channel.

In Figures 4–11, ELM, GR, and RBF methods are compared with the Elman approach. Table 2 gives the simulation parameters. The MSE and AE of Elman approach are 0.00014 and 0.011, which are the lowest MSE and AE in the five methods. This is because Elman is a typical dynamic recurrent neural network and can adapt to the time-varying characteristics by adding a context layer.

The MSE is compared in Figure 12. Compared with GR, Elman has a better MSE performance, but the running time is longer than GR. Furthermore, compared with other methods, Elman has a higher quality precision and a lower time complexity.

6. Conclusion

This paper investigated the SPSC prediction of mobile IoT Networks. The exact expressions for SPSC were derived. Furthermore, based on the Elman network, we proposed an intelligent secure data transmission quality prediction algorithm. The theoretical analysis showed the following: (1) the SPSC performance over Nakagami channels was the best; (2) compared with different methods, the Elman algorithm can achieve a higher quality precision.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon reasonable request and with permission of funders.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research was supported by the National Natural Science Foundation of China (no. 11664043).

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Copyright

Copyright © 2021 Fagen Yin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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