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The arithmetic and logic unit (ALU) is a key element of complex circuits and an intrinsic part of the most widely recognized complex circuits in digital signal processing. Also, recent attention has been brought to reversible logic and quantum-dot cellular automata (QCA) because of their intrinsic capacity to decrease energy dissipation, which is a crucial need for low-power digital circuits. QCA will be the preferred technology for developing the subsequent generation of digital systems. These technologies played a substantial role in the design of the ALU for operations such as multiplication, subtraction, and division. In developing reversible logic and QCA technologies, the ALU is frequently studied as a central unit. Implementing an efficient ALU with low quantum cost and a small number of cells based on an efficient reversible block can solve all previous issues. Therefore, this research constructs a Feynman gate, a Fredkin gate, and full adder circuits using reversible logic and QCA technology. Using all of the specified circuits, a 20-operation ALU is constructed. The power consumption of the proposed ALU under various energy ranges demonstrated significant improvements over earlier designs. [ABSTRACT FROM AUTHOR]

Digital signal processing (DSP) is used in computer processing to conduct different signal-processing tasks. The DSPs are used in the series numbers representing a continuous variable in a domain such as time, area, or frequency. The multiply-accumulate (MAC) unit is crucial in various DSP applications, including convolution, discrete cosine transform (DCT), Fourier Transform, etc. Thus, all DSPs contain a critical MAC unit in signal processing. The MAC unit conducts multiplication and accumulation operations for continuous and complicated DSP application processes. On the other hand, in the MAC structure, the stability of the circuit and the occupied area pose some significant challenges. However, high-performance quantum technology can easily overcome all the previous shortcomings. Hence, this paper suggests an efficient MAC for DSP applications using a Vedic multiplier, half adder, and accumulator based on quantum technology. All the proposed structures have used a single-layer layout without rotated cells. The suggested architecture is designed and validated based on the QCADesigner 2.0.3 tool. The findings revealed that all the developed circuits have a simple architecture with fewer quantum cells, optimal area, and low latency. [ABSTRACT FROM AUTHOR]

In the distributed database systems, the relations needed by a query can be kept in several locations. This process significantly increases potential corresponding Query Execution Plans (QEP's) for a user query. Henceforth, in addition to the expense of local computing, the charge of transferring data between different cloud sites should also be considered. It does not sound logical to investigate all potential query plans in a high setting like this. The best query plan (regarding cost) must be generated for processing a given query. A new hybrid multi-objective genetic and bat algorithm, a Multi-Objective Genetic Algorithm with BAT (MOGABAT), is used in the present article to produce the best query plans. The functionality comparison is made on different join graph structures, among MOGABAT, Multi-Objective BAT (MOBAT), and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The obtained results have shown that the quality of generated query plans is enhanced for the join graph structures. Nevertheless, more execution time is needed. [ABSTRACT FROM AUTHOR]

Cloud computing enables businesses to decrease the total costs by outsourcing their required services. Therefore, it provides a new challenge of data protection regarding reliability, integrity, and confidentiality because of outsourcing. As a result, cloud security is becoming a key differentiator and competitive edge between cloud providers. Nowadays, the use of blockchain in cloud computing is one of the most common innovations that can solve cloud computing security problems. Blockchain is a decentralized data management technology to provide security, anonymity, and data integrity without any third-party organization. This work presents a comprehensive study of cloud computing security concerns and blockchain solutions to address these concerns. Applying different filters and searching international databases, 21 articles from reputable journals are found and reviewed. We examine cloud security in three categories: data integration, trust, and privacy. The results show that blockchain provides a successful platform in this regard. However, security issues are one of the most important challenges that still need further study. The article also provides a roadmap framework for future research and action. [ABSTRACT FROM AUTHOR]

Internet of things has become an essential principle of human life with the widespread acceptance of intelligent environments, where everyday objects can communicate through the internet. Mobility is the most critical factor in today's internet of things devices to apply in real-world applications. Also, proper routing protocol plays a vital role in communication and reduces devices' energy consumption. The clustering approach is one of the efficient routing techniques to improve energy consumption and enhance network life. Due to the NP-Hard nature of clustering, a krill herd optimization algorithm is proposed in this paper to select the cluster head nodes and intermediate nodes required for routing. The simulation results using NS-3 confirmed that the proposed technique performs better than particle swarm optimization and cuckoo search in terms of network lifetime. The proposed technique increases the total network lifetime by at least 11.1% compared to the current clustering methods. [ABSTRACT FROM AUTHOR]

Quantum-dot Cellular Automata (QCA) is novel prominent nanotechnology. It promises a substitution to Complementary Metal–Oxide–Semiconductor (CMOS) technology with a higher scale integration, smaller size, faster speed, higher switching frequency, and lower power consumption. It also causes digital circuits to be schematized with incredible velocity and density. The full adder, compressor, and multiplier circuits are the basic units in the QCA technology. Compressors are an important class of arithmetic circuits, and researchers can use quantum compressors in the structure of complex systems. In this paper, first, a novel three-input multi-layer full-adder in QCA technology is designed, and based on it, a new multi-layer 4:2 compressor is presented. The proposed QCA-based full-adder and compressor uses an XOR gate. The proposed design offers good performance regarding the delay, area size, and cell number comparing to the existing ones. Also, in this gate, the output signal is not enclosed, and we can use it easily. The accuracy of the suggested circuits has been assessed with the utilization of QCADesigner 2.0.3. The results show that the proposed 4:2 compressor architecture utilizes 75 cell and 1.25 clock phases, which are efficient than other designs. [ABSTRACT FROM AUTHOR]

Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a network of physical items implanted with software, sensors, etc., to link and exchange data with other devices. These devices vary in complexity from common household items to sophisticated industrial instruments. It would be challenging to choose an appropriate IoT service based on the requirements of the vast pool of accessible services with similar capabilities, given the growth of IoT-based service providers in the market. A suitable selection may be made using quality-of-service (QoS) parameters that characterize a service. IoT has several benefits over traditional communication systems. Also, it is a component of a safe and smart city system known as the Industrial Internet of Things (IIoT) which is particularly useful in the industrial field. However, it suffers from various issues such as high costs, energy consumption, and long delays. The production scheduling problem is one of the main issues in IIoT, and it is an NP-hard problem regarding cost and energy efficiency. Therefore, a meta-heuristic algorithm based on the elephant herd optimization algorithm is proposed to minimize resource costs, conversion costs, and the cost of continuous development delays. By combining the clan updating factor, separating operator, and the proposed algorithm, we created an effective and efficient method to solve the issue of production scheduling. Many experiments are performed to determine the performance of industrial environments. The outcomes demonstrate that the suggested technique can optimize planning and achieve cost reduction, efficient energy consumption, and latency decrease. [ABSTRACT FROM AUTHOR]

With the galloping progress of the Internet of Things (IoT) and related technologies in multiple facets of science, distribution environments, namely cloud, edge, fog, Internet of Drones (IoD), and Internet of Vehicles (IoV), carry special attention due to their providing a resilient infrastructure in which users can be sure of a secure connection among smart devices in the network. By considering particular parameters which overshadow the resiliency in distributed environments, we found several gaps in the investigated review papers that did not comprehensively touch on significantly related topics as we did. So, based on the resilient and dependable management approaches, we put forward a beneficial evaluation in this regard. As a novel taxonomy of distributed environments, we presented a well-organized classification of distributed systems. At the terminal stage, we selected 37 papers in the research process. We classified our categories into seven divisions and separately investigated each one their main ideas, advantages, challenges, and strategies, checking whether they involved security issues or not, simulation environments, datasets, and their environments to draw a cohesive taxonomy of reliable methods in terms of qualitative in distributed computing environments. This well-performed comparison enables us to evaluate all papers comprehensively and analyze their advantages and drawbacks. The SLR review indicated that security, latency, and fault tolerance are the most frequent parameters utilized in studied papers that show they play pivotal roles in the resiliency management of distributed environments. Most of the articles reviewed were published in 2020 and 2021. Besides, we proposed several future works based on existing deficiencies that can be considered for further studies. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a new information technology sector in which each device may receive and distribute data across a network. Industrial IoT (IIoT) and related areas, such as Industrial Wireless Networks (IWNs), big data, and cloud computing, have made significant strides recently. Using IIoT requires a reliable and effective data collection system, such as a spanning tree. Many previous spanning tree algorithms ignore failure and mobility. In such cases, the spanning tree is broken, making data delivery to the base station difficult. This study proposes an algorithm to construct an optimal spanning tree by combining an artificial bee colony, genetic operators, and density correlation degree to make suitable trees. The trees’ fitness is measured using hop count distances of the devices from the base station, residual energy of the devices, and their mobility probabilities in this technique. The simulation outcomes highlight the enhanced data collection reliability achieved by the suggested algorithm when compared to established methods like the Reliable Spanning Tree (RST) construction algorithm in IIoT and the Hop Count Distance (HCD) based construction algorithm. This proposed algorithm shows improved reliability across diverse node numbers, considering key parameters including reliability, energy consumption, displacement probability, and distance. [ABSTRACT FROM AUTHOR]