Deep convolutional neural network based secure wireless voice communication for underground mines

Dey, Prasanjit; Kumar, Chandan; Mitra, Mitrabarun; Mishra, Ramesh Kumar; Chaulya, S.K.; Prasad, G.M.; Mandal, Sujit; Banerjee, G.

doi:10.1007/s12652-020-02700-w

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…IoT multimedia applications increasingly rely on security as a vital component [16]. The Internet of Things (IoT) is vulnerable to security threats from both applications and humans because the IoT is designed to be able to execute user-implemented applications widely [17]. It is possible to hack into applications and sensors, and hateful people can access the Internet of Things (IoT) and perform violent service assaults.…”

Section: Overview Of Security In a Wireless Environmentmentioning

confidence: 99%

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Analysis of the Security of Internet of Multimedia Things in Wireless Environment

Haddad,

Mustafa,

Salih

et al. 2023

JCSANDM

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The Internet of Things (IoT) and real-time flexibility improve people’s lives, and IoT applications rely heavily on multimedia sensors and devices. An interconnected network of IoT multimedia devices has made the Internet of Medical Things (IoMT). It creates massive data distinct from what the Internet of Things (IoT) produced. Smart traffic monitoring and smart hospitals are only a few examples of real-time deployment applications. IoMT data and decision-making must be made quickly since it directly impacts human life. The security heterogeneity of optimization issues is a significant challenge for enabling multimedia applications on the IoT. The IoMT has difficulty achieving low-cost data collecting while maintaining data security. An Internet of Multimedia Things in a wireless environment (IoMT-WE) system decreases the bandwidth and privacy risk caused by the revocation list, ensures the integrity of batch verification information, and corresponds with Vehicular ad hoc network (VANET) security performance. The proposed method uses random subsampling and chaotic convolution to collect numerous images. The sampling method is safe since the measurement matrix is controlled by chaos. As part of the IoMT architecture, wireless multimedia sensor nodes can be more easily deployed over the long term for real-time multimedia. The Wireless Multimedia Sensor Network (WMSN) comprises nodes that can capture both multimedia and non-multimedia data. The ioMT-WE system has been tested and found to be secure and effective.

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Section: Overview Of Security In a Wireless Environmentmentioning

confidence: 99%

“…As shown in Equation (17), υ DG k is the one-time usage of a pseudorandom identifier throughout the whole cryptographic transaction R l . In Cipher Block Chaining (CBC) mode, users employed an Advanced Encryption Standard AES with a key length of 128 bits to produce a challenge.…”

Section: Setup Phasementioning

confidence: 99%

Analysis of the Security of Internet of Multimedia Things in Wireless Environment

Haddad,

Mustafa,

Salih

et al. 2023

JCSANDM

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“…In addition, Dey et al (2021b) have presented a safe architecture for training the model securely. Similarly, Dey et al (2021c) have introduced a deep network-based secure communication channel in the mining site to secure communication in underground mines. Muduli et al (2019) have conducted a comprehensive survey on deploying wireless sensor network technology in the enclosed region of the mine site.…”

Section: Introductionmentioning

confidence: 99%

t-SNE and variational auto-encoder with a bi-LSTM neural network-based model for prediction of gas concentration in a sealed-off area of underground coal mines

et al. 2021

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A deep learning network is introduced to predict concentrations of gases in the underground coal mine enclosed region using various IoT-enabled gas sensors installed in a metallic gas chamber. The air is sucked automatically at specific intervals from the sealed-off site utilizing a solenoid valve, suction pump, and programmed microprocessor. The gas sensors monitor the gas content in the underground coal mine and communicate gas concentration to the surface server room through a wireless network and cloud storage media. The t-SNE_VAE_bi-LSTM model is proposed in this study as a prediction model that combines the t-SNE, VAE, and bi-LSTM networks. The proposed model's t-SNE method aims to minimize the dimensionality of the recorded gas concentration; the presented model's VAE layer intends to retrieve the inner characteristics of low-dimension gas concentration. Finally, the given model's Bi-LSTM layer tries to forecast the concentrations of CH4, CO2, CO, O2, and H2 gases. The proposed model's prediction accuracy is compared with the existing two models, namely auto-regressive integrated average moving (ARIMA) and chaos time series (CHAOS). The experiment findings demonstrate that the t-SNE_VAE_bi-LSTM model forecasted mean square error (MSE) is more accurate, and it has lesser MSE value of 0.029 and 0.069 for CH4; 0.037 and 0.019 for CO2; 0.092 and 0.92 for CO; 1.881 and 1.892 for O2; and 1.235 and 1.200 for H2 than the ARIMA and CHAOS models, respectively.

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Voice Compression Coding Scheme of Ground Penetrating Wireless Communication in Mines Based on Fuzzy Delta Modulation

Zhao¹,

Ma²,

Zhang³

et al. 2023

Lecture Notes on Data Engineering and Communications Technologies

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Deep convolutional neural network based secure wireless voice communication for underground mines

Cited by 7 publications

References 29 publications

Analysis of the Security of Internet of Multimedia Things in Wireless Environment

Analysis of the Security of Internet of Multimedia Things in Wireless Environment

t-SNE and variational auto-encoder with a bi-LSTM neural network-based model for prediction of gas concentration in a sealed-off area of underground coal mines

Voice Compression Coding Scheme of Ground Penetrating Wireless Communication in Mines Based on Fuzzy Delta Modulation

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