In the era of Industrial Internet of Things (IIoT) 5.0, recognizing emotions through speech plays a crucial role in creating advanced and emotionally intelligent systems for better human-machine interactions (HMI) for various IoT applications. These systems are especially valuable in smart manufacturing environments and their respective applications. The proposed research showcase the challenge of improving speech quality for reliable speech emotion recognition (SER) in noisy industrial settings by introducing an Improved Modulation Spectral Subtraction (IMSS) method. The IMSS technique enhances traditional Analysis Modification Synthesis (AMS) frameworks with refined processing in the modulation domain, utilizing advanced noise estimation approaches like the Minimum Statistics (MS) method. To recognize emotions, the study employs a machine learning algorithm based on a convolutional neural network (CNN). The proposed algorithm processes the enhanced speech signals to accurately detect emotional states in speech. The combination of the IMSS method with the CNN model ensures that emotional details in speech are retained, which is essential for effective SER. The proposed technique significantly improves speech clarity and quality, evaluated through objective measures such as the Perceptual Evaluation of Speech Quality (PESQ). The experimental results show notable improvements in speech quality, with an average 14.91% increase in PESQ scores for input signal-to-noise ratios (SNRs) between 0 and 15 dB, along with a 63% reduction in Log Spectral Distance. These findings highlight the method’s effectiveness under various noisy conditions. Spectrogram analysis further demonstrates the IMSS method’s ability to enhance the accuracy and reliability of SER, which is reinforced by the strong performance of the CNN in classification tasks. By optimizing the modulation frame duration to 128 milliseconds, the work approach provides a valuable contribution to adaptive and safety-oriented IIoT 5.0 applications.

The device-to-device (D2D) networks bear a close resemblance to future Internet-of-Things (IoT) networks. IoT plays a significant role in the FinTech industry, especially in data security and context-aware applications. D2D communication in cellular networks has emerged as a competent technology for the upcoming future cellular networks. To improve spectral efficiency and data secrecy, we propose a two-phase network coding-based two-way decode-and-forward (DF) relaying policy for D2D communication. First, a suitable relay node is selected considering the end-to-end signal-to-interference-plus-noise ratio (SINR). Then, an XOR-based DF operation is performed when the signal transmitted by both the D2D users (DUEs) is successfully decoded at the relay node. In order to analyze the performance of the proposed two-way communication system, we derive the outage probability expression as a function of various system parameters, such as the SINR, position of both the relays and DUEs, power splitting factor, number of relays, and data rate. Furthermore, we also derive an expression for the error probability and average throughput of the D2D link to evaluate a fair comparison of the proposed approach with the traditional methods. The analytical results are also validated with the simulation results to justify the efficacy of the proposed method.

Device-to-device (D2D) communication ushers the realization of massive Internet of Things (mIoT) network for smart cities through long term evolution-advanced (LTE-A). In a multicell mm-Wave environment, acute interference from adjacent cells degrades the signal quality. Thus, our work focuses on the minimization of interference from the neighboring cells through optimal resource allocation and power optimization. Rate-splitting multiple access (RSMA) technique is employed where the message to be transmitted is divided into two parts. Two modes for resource allocation are formulated which aims to maximize the throughput under certain interference constraints. The radius of coverage expression is derived for switching of modes to take place. Successive interference cancelation (SIC) is introduced as a power constraint which enhances the overall performance of the network. Subsequently, Lagrange’s dual-optimization method is utilized for optimizing the D2D transmit power and also lowering the computational complexity of the overall network. Simulation results depict the overall performance efficiency in terms of transmission rate and signal strength. The results show that with the increase in difference of transmit power allocated to the common and private message, overall transmission rate also increases significantly. Finally, comparison with existing scheme validates the efficiency of the proposed work.

Data security and resource allocation are two important terms associated with the Internet of Things (IoT). This recent technical evolution has made its mark in industrial applications making the network more flexible and computation friendly through connecting all the devices. As a subset of IoT, the framework of Industrial IoT (IIoT) is based on the huge number of nodes with the continuous process of multiple works at a time. Due to this, multiobjective network, interference in the path always becomes the reason for the loss of network resources as well as the security of data becomes vulnerable. In most of the previous works, dedicated channel states are considered for fixed resources which remains a major issue of IIoT network flexibility along with security. In this article, both the problems are incorporated by calculating the channel security and using convolutional neural network (CNN) optimal channel state extracted for different applications. This results as a fast system with proper utilization of resources and validated with mathematical analysis and simulations.

In this article, we develop a novel approach for detecting patients’ position using the radial basis function of the neural network. This new approach aims to continuously monitor the patients’ health statistics and real-time prediction, even when they are outside of cellular coverage. Our research is driven by an initiative to innovate a novel healthcare system of significant importance for intelligent and efficient medical services. For example, doctors need to remotely monitor any patient’s health with the provided health statistics derived from data collected from battery-powered Internet of Things sensors. To this end, our proposed method has been quantified with a holistic mathematical analysis and extensive simulations considering realistic network situations. Our results have accredited the efficiency in the prediction of localization for the patients’ position for the anticipated intelligent healthcare system.

The future advancement of technology in Internet of Things (IoT) paradigm, Wireless Sensor Networks (WSNs) provide sensing services to connect all the devices. In the upper layer of OSI model designing an energy efficient routing protocol in WSN is a challenge, which can ease the work of Multi-access edge computing (MEC) in IoT applications. The advent of 6G is also playing key role for reliable communication between the sensing elements for IoT applications. These two phenomena are significantly influencing for the progress of next generation Intelligent Transportation System (ITS). Therefore, the proposed work presents a novel method of implementing Distributed Artificial Intelligence (DAI) with neural networks for energy efficient routing as well as a fast response for intra-cluster communication of the nodes to overcome the challenges for ITS. Although there exist several works on the inter-cluster energy-efficient network, our work proposes a new way of implementing the hybrid approach of DAI and Self Organizing Map (SOM). The proposed approach proves to be a better solution in terms of overall energy consumption by the network, along with the computational challenges. Further, the work presents mathematical analysis, simulation results and comparison with the conventional techniques for justification.

SARS-CoV-2 is an intensively investigated virus from the order Nidovirales (Coronaviridae family) that causes COVID-19 disease in humans. Through enormous scientific effort, thousands of viral strains have been sequenced to date, thereby creating a strong background for deep bioinformatics studies of the SARS-CoV-2 genome. In this study, we inspected high-frequency mutations of SARS-CoV-2 and carried out systematic analyses of their overlay with inverted repeat (IR) loci and CpG islands. The main conclusion of our study is that SARS-CoV-2 hot-spot mutations are significantly enriched within both IRs and CpG island loci. This points to their role in genomic instability and may predict further mutational drive of the SARS-CoV-2 genome. Moreover, CpG islands are strongly enriched upstream from viral ORFs and thus could play important roles in transcription and the viral life cycle. We hypothesize that hypermethylation of these loci will decrease the transcription of viral ORFs and could therefore limit the progression of the disease.

The birth of beyond 5G (B5G) and emerge of 6G have made personal and industrial operations more reliable, efficient, and profitable, accelerating the development of the next-generation Internet of Things (IoT). We know, one of the most important key performance indicators in 6G is smart network architecture, and in massive IoT applications, energy-efficient ubiquity networks rely mainly on the intelligence and automation for industrial applications. This article addresses the energy consumption problem with a massive IoT system model with dynamic network architecture or clustering using a multiagent system (MAS) for industrial 6G applications. The work uses distributed artificial intelligence (DAI) to cluster the sensor nodes in the system to find the main node and predict its location. The work initially uses the backpropagation neural network (BPNN) and convolutional neural network (CNN), which are, respectively, introduced for optimization. Furthermore, the work analyzes the correlation of mutual clusters to allocate resources to individual nodes in each cluster efficiently. The simulation results show that the proposed method reduces the waste of resources caused by redundant data, improves the energy efficiency of the whole network, along with information preservation.

Wireless sensor network enhances the classic features of wireless communication with cognitive capabilities for efficient spectrum usage. This work focuses on the dynamic correlation between the secondary users (SUs) based on their statistical behaviour while performing the cooperative communication in cognitive radio sensor network. The proposed approach addresses the problem of uneven and repetitive communication between the SUs in a cooperative communication scenario. The authors’ objective is to use a novel approach based on the Gaussian copula theory and advanced particle swarm optimisation algorithm to analyse the dependencies of time‐varying spectrum sensing behaviour of multiple SUs. Here, time delay in prediction reduces due to the analysis of the dynamic correlation between the time delay in spectrum sensing results for the same set of channels. The simulation results show the performance of the proposed approach outperforming the other well‐known techniques.