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The promise of automated-driving cars cause the automotive and consumer electronics (CE) sector to rethink what it means to drive, but the relationship between the car and the consumer. Recent trend in Internet of Vehicle Things (IoVT) promotes robust interactions in between humans and vehicles which altimetry points to enhance human abilities such as hearing or emotion awareness as a part of safety concern. The voice-based interactions (speech recognition, stress monitoring) will improve in-time awareness of the vehicle status. Unfortunately, the existing modulation domain speech enhancement techniques achieve low satisfactory performance in detecting humans stress emotions where the environmental noise is inevitable and varies with every passing location of vehicle. In this direction, we propose frontend processing framework, in particular to stress emotion detection cases in different non-stationary noisy environments. This study encompasses three Inter-related issues: (i) analysis, modification, and synthesis of noisy speech emotion in modulation domain in realtime background noise, (ii) extracting set of Mel-frequency cepstral coefficients (MFCC) features from noisy speech stimuli for speech emotion recognition, and (iii) evaluation of overall system performance by means of objective parameters, and confusion matrix in adverse environments using speech emotion database Interactive Emotional Dyadic Motion Capture (IEMOCAP)

Securing wireless mesh networks (WMNs) is a crucial issue due to its intrinsic characteristics. Several endangered features might emerge due to the exposure of the networks to a diversity of medium access control (MAC) layers such as distributed denial of service (DDoS) attacks, false reply attacks, and other identity attacks. Against these attacks, the determination of existing techniques is insufficient to ensure the complete security solutions to protect the backbone network at multiple levels. As a result, effective, scalable, and integrated security solutions for WMNs are required. In WMNs, protecting legitimate gateway nodes and internal mesh routers against malicious attacks at the MAC layer remains a difficult problem. Our proposed trust-based security mechanism includes distributed authentication and deauthentication algorithms that validates backbone mesh routers as well as gateway nodes. Particularly, this proposed model targets DDoS attacks in the network. The proposed DDoS attack prevention mechanism (DAPM) uses distributed authentication and deauthentication algorithms to build trusted group heads for managing secure data communication in the network. Our research and practical results show that the proposed mechanism decreases the severity of malicious nodes and strengthens the security compared to existing centralized schemes such as digital signature authentication (DSA-Mesh, MENSA, Mobisec, and AHKM). The experimental solutions show the significance of the proposed work with 10% to 12% of better performance than the existing techniques.

In recent years, decentralized applications such as Distributed Ledger Technologies and blockchain have evolved as suitable applications for secure sharing of information in a decentralized fashion using privacy preserving techniques like zero-knowledge protocols. However, the biggest issue with the traditional zero-knowledge protocols on a blockchain ledger is their slow performance on big data. This paper presents the advance zero-knowledge ledger by replacing their range-proof technique with the most efficient range-proof technique based on the improved inner product based zero-knowledge proofs. Moreover, this technique allows the aggregation of multiple range-proofs into a single range-proof, which makes the current zero-knowledge ledger system more efficient than the existing one.

Recent studies have shown that computed tomography (CT) scan images can characterize COVID-19 disease in patients. Several deep learning (DL) methods have been proposed for diagnosis in the literature, including convolutional neural networks (CNN). But, with inefficient patient classification models, the number of 'False Negatives' can put lives at risk. The primary objective is to improve the model so that it does not reveal 'Covid' as 'Non-Covid'. This study uses Dense-CNN to categorize patients efficiently. A novel loss function based on cross-entropy has also been used to improve the CNN algorithm's convergence. The proposed model is built and tested on a recently published large dataset. Extensive study and comparison with well-known models reveal the effectiveness of the proposed method over known methods. The proposed model achieved a prediction accuracy of 93.78%, while false-negative is only 6.5%. This approach's significant advantage is accelerating the diagnosis and treatment of COVID-19.

Internet of Things (IoT) has revolutionized the digital world by connecting billions of electronic devices over the internet. IoT devices play an essential role in the modern era when conventional devices become more autonomous and smart. On the one hand, high-speed data transfer is a major issue where the 5G-enabled environment plays an important role. On the other hand, these IoT devices transfer the data by using protocols based on centralized architecture and may cause several security issues for the data. Merging artificial intelligence to 5G wireless systems solves several issues such as autonomous robots, self-driving vehicles, virtual reality, and engender security problems. Building trust among the network users without trusting third party authorities is the system's primary concern. Blockchain emerged as a key technology based on a distributed ledger to maintain the network's event logs. Blockchain provides a secure, decentralized, and trustless environment for IoT devices. However, integrating IoT and blockchain also has several challenges; for example, major challenge is low throughput. Currently, the ethereum blockchain network can process approximately 12 to 15 transactions per second, while IoT devices require relatively higher throughput. Therefore, blockchains are incapable of providing functionality for a 5G-enabled IoT based network. The limiting factor of throughput in the blockchain is their network. The slow propagation of transactions and blocks in the P2P network does not allow miners and verifiers to fastly mine and verify new blocks, respectively. Therefore, network scalability is the major issue of IoT based blockchains. In this work, we solved the network scalability issue using blockchain distributed network while to increase the throughput of blockchain, this article uses the Raft consensus algorithm. Another most important issue with IoT networks is privacy. Unfortunately, the blockchain distributed ledgers are public and sensitive information is available on the network for everyone are private, but in such cases, third party editing is not possible without revealing the original contents. To solve privacy issues, we used zkLedger as a solution that is based on zero knowledge-based cryptography.

The COVID-19 pandemic has caused hundreds of thousands of deaths, millions of infections worldwide, and the loss of trillions of dollars for many large economies. It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope. Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses. In the same vein, a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of, and role of age groups and gender on, the probability of COVID-19 infection. This study aimed to review, analyze, and critically appraise published works that report on various factors to explain their relationship with COVID-19. Such studies span a wide range, including descriptive analyses, ratio analyses, cohort, prospective and retrospective studies. Various studies that describe indicators to determine the probability of infection among the general population, as well as the risk factors associated with severe illness and mortality, are critically analyzed and these findings are discussed in detail. A comprehensive analysis was conducted on research studies that investigated the perceived differences in vulnerability of different age groups and genders to severe outcomes of COVID-19. Studies incorporating important demographic, health, and socioeconomic characteristics are highlighted to emphasize their importance. Predominantly, the lack of an appropriated dataset that contains demographic, personal health, and socioeconomic information implicates the efficacy and efficiency of the discussed methods. Results are overstated on the part of both exclusion of quarantined and patients with mild symptoms and inclusion of the data from hospitals where the majority of the cases are potentially ill.

The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. Machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state-of-the-art machine learning algorithms, namely Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy.

Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.

In developed nations, the advent of distributed ledger technology is emerging as a new instrument for improving the traditional system in developing nations. Indeed, adopting blockchain technology is a necessary condition for the coming future of organizations. The distributed ledger technology provides better transparency and visibility. This study investigated the features that may influence the behavioral intention of energy experts to implement the distributed ledger technology for the energy management of developing countries. The proposed model is based on the Technology Acceptance Model construct and the diffusion of the innovation construct. Based on a survey of 178 experts working in the energy sector, the proposed model was tested using structural equation modeling. The findings showed that perceived ease of use, perceived usefulness, attitude, and cost saving had a positive and significant impact during the blockchain technology adoption. However, innovativeness showed a positive effect on the perceived ease of use whereas an insignificant impact on the perceived usefulness. The present study offers a holistic model for the implementation of innovative technologies. For the developers, it suggest rising disruptive technology solutions.

In the modern world, there is a huge increase in the usage of various technologies which may produce heterogeneous data. Using the latest security algorithms like Triple Data Encryption Standard (TDES), Rivest Shamir Adleman (RSA), Advanced Encryption Standard (AES) these (heterogeneous) data are transferred over the internet. Despite of such good security mechanisms, there exist vulnerabilities to the user data. In the recent years, researchers started the usage of bio-inspired computing models towards security. In this paper, two such bio-inspired computing models namely splicing system and Deoxyribonucleic acid (DNA) sequencing were used. We proposed a novel cryptosystem using contextual array splicing system for DNA sequenced input data. In this process, we have designed Turing machine(s) that perform(s) the conversion of a binary input to DNA sequence and the cryptosystem operation using contextual array splicing system. Next, the developed novel cryptosystem is applied in the domain of Medical Internet of Things (MIoT). Further, we had investigated the proof of correctness. Next, the proposed cryptosystems are proved to be Turing computable. Finally, the security analysis and evaluation of the proposed cryptosystem has been studied.

Cloudlet is an important part of providing cloud services in Mobile Edge Computing (MEC) with sustainability. As the number of mobile users grows rapidly in the current era, the load in the cloudlet becomes very high. The cloudlet is considered in the middle layer for providing cloud services with low latency and energy efficiency. Hence the task allocation and scheduling inside of the cloudlet is a challenging job. In the recent past, many research works conducted without considering real-time network parameters. In this work, a heuristic load balancing strategy is designed and analyzed to minimize the task completion time and makespan, which enhances the efficiency of cloud services. The proposed method is considered a dynamic task allocation strategy to the cloudlet concerning network bandwidth, network delay, energy efficiency, and this approach is compared with the queue-based task allocation strategy. The experiment is conducted to compare the proposed method with the recently developed standard algorithms over the synthetic dataset. Experimental results of the proposed work show a significant improvement in task scheduling in terms of energy and execution cost, as well as time, compared to the existing methods. The proposed method outperforms the standard algorithm in most of the observed cases with sustainability.

Wearable wireless sensors, or Wireless Body Sensor Network (WBSN), is one of the most recent fields of research in healthcare applications. The sensors are placed on the human body to detect the critical medical parameters and transmit the collected data to a centralized node. Though the devices work at low energy and power constraints, maintenance of such devices is a crucial aspect. In light of this requirement, an efficient medium access control (MAC) protocol should be utilized to extend the life time of the network. In this paper, we propose a new “Priority based Energy Efficient (PrEE) MAC protocol” incorporated with various priorities in predefined devices based on data sensing and normal devices changing to priority when the value is more than the threshold, by modifying IEEE 802.15.4 header bits. The PrEE MAC protocol is designed based on the IEEE 802.15.4 and its Superframe structure. The simulation results show that the energy efficiency and lifetime of the proposed MAC protocol is more as compared to the existing MAC protocol.

In this paper, we present a clustering-based energy aware secure routing protocol that computes trustworthy and energy efficient secure routes for wireless ad hoc networks (WANETs). Clustering in ad hoc networking paradigm is very useful for achieving scalability and robustness as it helps to save the bandwidth, to take simpler routing decisions, thus results in decreased energy dissipation of the network. WANETs are often deployed in such places where recharge or replace of energy sources is difficult. Further, unpredicted mobility and quasi-static nature of nodes and dynamic network topology yield a serious need for battery awareness during routing. In light of this, we first provide a definition of network lifetime based on application-specific criteria. Also a mathematical model for calculating residual battery power has been framed to propose an energy aware routing protocol. Trust-based scheme is used to assure secure end-to-end delivery of packets in the network. Extensive simulation has be...