Big Data Communication Research Articles

Replicated multistage interconnection networks: QoS evaluation for parallel and distributed computing

Introduction to big data becomes very important with dealing in high-performance parallel distributed computing, especially in those systems where communication among a number of processors is required. The current paper takes into consideration different topologies for the Shuffle Exchange Network (SEN) in order to attain optimal data transfer among such scenarios. SEN topologies are a key feature in connecting several processors and implementing the data transfer among them where a single processor fails to handle the load. The study hereby reports in the performance, reliability, and cost analysis of these topologies. These topologies, some of which are advocated to have better performance in many studies, include replicated networks. Our research demystifies claims made in earlier papers that replicated networks have inflated reliability and higher costs due to their additional links. Researchers are therefore guided on accurate performance data that will lead them to make optimum choices of SEN topologies for targeted applications. These findings further highlight that the trade-offs between reliability and cost must be carefully considered during network design so as to arrive at better results for big data communication and computation in parallel and distributed systems. This work provides important insights into the correct evaluation of SEN topologies, helping to correct the misleading facts and to have better network selection for various real-time application realizations.

A new era of ICTs for combating symptoms of neurodevelopmental disorders

Information Communication Technology (ICT) is an umbrella definition, where the purpose of its creation and use is to facilitate communication, storage, and transfer of big data and the development and use of the technology that supports it. In contemporary research, several studies have observed attempts to incorporate them in populations with neurodevelopmental disorders (NDDs) to detect/assess, intervene, and improve deficits for the inclusion of these individuals in education and society. This review attempts to explore new universal and mainstream technology and to broaden and provide data for employing it in future research or intervention in the school setting as a complementary addition to education combating core symptoms of the three most recurring NDDs including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and developmental coordination disorder (DCD).

Multi-level index construction method based on master-slave blockchains.

Master-slave blockchain is a novel information processing technology that is domain-oriented and uses efficient cryptography principles for trustworthy communication and storage of big data. Existing indexing methods primarily target the creation of a single-structured blockchain, resulting in extensive time and memory requirements. As the scale of domain data continues to grow exponentially, master-slave blockchain systems face increasingly severe challenges with regards to low query efficiency and extended traceback times. To address these issues, this paper propose a multi-level index construction method for the master-slave blockchain (MLI). Firstly, MLI introduces a weight matrix and partitions the entire master-slave blockchain based on the master chain structure, the weight of each partition is assigned. Secondly, for the master blockchain in each partition, a master chain index construction method based on jump consistent hash (JHMI) is proposed, which takes the key value of the nodes and the number of index slots as input and outputs the master chain index. Finally, a bloom filter is introduced to improve the column-based selection function and build a secondary composite index on the subordinate blockchain corresponding to each master block. Experimental results on three constraint conditions and two types of datasets demonstrate that the proposed method reduce the index construction time by an average of 9.28%, improve the query efficiency by 12.07%, and reduce the memory overhead by 24.4%.

Digital Marketing Evaluation of Applied Undergraduate Talent Training with E-commerce using Big Data Mining and Communication Technology Support

Digital Marketing Evaluation of Applied Undergraduate Talent Training with E-commerce using Big Data Mining and Communication Technology Support

디지털 전환 시대의 스마트 양식장 입지 타당성 평가방안 연구

The seriousness of the domestic fisheries industry has fundamental problems, starting mainly due to the depletion of fishery resources, the aging of fishing workers, and the aging of fishing boats and equipment, and the production of coastal fishing (capturing and fishing methods) continues to decline every year due to problems such as illegal overfishing, global warming, and environmental pollution. Despite this situation, global seafood consumption is increasing and the per capita meat consumption is approaching. To this end, a continuous and stable supply system can be established only by further expanding the smart management system that further develops the existing aquaculture industry (farm culture) beyond the traditional fishing method and innovatively improves the inland aquaculture on land. As a result, smart farming will be able to quickly recognize changes in aquaculture conditions, water temperature, and water quality by utilizing artificial intelligence (AI), big data technology, information and communication technology (ICT), and the Internet of Things (IoT), respond to the situation, and engage in stable aquaculture.

Chi-Square Detective Ensembled Cardinal Gradient Bootstrap Aggregating Classifier for Secured Big Data Communication

Chi-Square Detective Ensembled Cardinal Gradient Bootstrap Aggregating Classifier for Secured Big Data Communication

Digital Marketing Evaluation of Applied Undergraduate Talent Training with E-commerce using Big Data Mining and Communication Technology Support

Digital Marketing Evaluation of Applied Undergraduate Talent Training with E-commerce using Big Data Mining and Communication Technology Support

Multilayer Joint Optimization of Packet Size and Adaptive Transmission Scheduling of Wireless Sensor Networks for Mechanical Vibration Monitoring

Limitations pertaining to the storage capacity and bandwidth of wireless sensor networks (WSNs) for mechanical vibration monitoring (MVM) pose difficulties in the realization of reliable and energy-efficient communication of big data. To prolong the lifetime of battery-limited WSNs, this paper proposes a multi-layer joint optimization of packet size and adaptive transmission scheduling (ATS) to jointly optimize the energy consumption of acquisition nodes from the storage and transmission layers. First, owing to the frequent storage and transmission of massive mechanical vibration data in WSNs, the packet size significantly affects the energy consumption during storage and transmission. The proposed packet size optimization based on the lowest energy consumption (PSO-LEC) model optimizes the energy consumption of acquisition nodes from three aspects: writing, reading, and transmission of data. Second, when transmitting massive vibration data frequently, the direct beacon frame conflict and indirect beacon frame conflict markedly induce data conflict and delay problems. Therefore, adaptive transmission scheduling based on the time division multiple access (TDMA/ATS) method is proposed to optimize the errors in data collision and transmission delay. Thus, the packet size and ATS are considered jointly to improve the network lifetime for MVM. Comprehensive experimental results indicate that the transmission reliability and energy-efficiency of the proposed scheme are significantly improved, and the network life is effectively prolonged for MVM.

A Searchable Encryption with Forward/Backward Security and Constant Storage

Dynamic searchable encryption satisfies users’ needs for ciphertext retrieval on semi-trusted servers, while allowing users to update server-side data. However, cloud servers with dynamically updatable data are vulnerable to information abuse and file injection attacks, and current public key-based dynamic searchable encryption algorithms are often complicated in construction and high in computational overhead, which is not efficient for practical applications. In addition, the client’s storage costs grow linearly with the number of keywords in the database, creating a new bottleneck when the size of the keyword set is large. To solve the above problems, a dynamic searchable encryption scheme that uses a double-layer structure, while satisfying forward and backward security, is proposed. The double-layer structure maintains a constant client-side storage cost while guaranteeing forward and backward security and further reduces the algorithm overhead by avoiding bilinear pairings in the encryption and decryption operations. The analysis results show that the scheme is more advantageous in terms of security and computational efficiency than the existing dynamic searchable encryption scheme under the public key cryptosystem. It is also suitable for the big data communication environment.

Using Part of Business Analytics for Learning and Working

The purpose of this study was to demonstrate some parts of business analysis, more specifically, to show the application of knowledge in business management and real-life situation. The research explores some aspects of business analysis, including enterprise management, foundations of business analysts, planning and monitoring, and data science statistics. Business analysis is a crucial discipline in the growth and development of business through implementing change. This paper covers the overview of business analysis and the application of knowledge in real life and business management. This brief demonstrates a summary of the business analysis programme, including the business analysis foundations and statistics for data science. The primary concepts outlined in business analysis foundations include business competencies, enterprise analysis, requirements, and solutions. Similarly, the paper covers statistics for data science, where vital concepts such as regression analysis, numerical and categorical variables, fundamentals, distribution, and hypothesis testing are presented. In addition, the analysis presents the most exciting discovery during the course elaborating on the birth and development of business analysis from the 1940s until today. Additionally, the paper covers the most crucial information presented in the course: business analysis application and its benefits in organizations. Also, it presents the application of business knowledge in daily life to define needs and solve problems. Furthermore, business analytics knowledge is applied during doctoral research and personal healthcare management. The brief covers the practical application of business analysis skills in large corporations such as Apple Inc., including big data analysis, HR management, communication, and manufacturing. Besides large corporations, business analytics skills apply in small companies in mitigating risks, operation analysis, and market analysis. Lastly, the paper demonstrates the practical application of knowledge in individual entrepreneurship, such as innovation analysis, revenue generation, system analysis, and mind mapping.

ORTHOGONAL REGRESSED STEEPEST DESCENT DEEP PERCEPTIVE NEURAL LEARNING FOR IoT- AWARE SECURED BIG DATA COMMUNICATION

The Internet of Things (IoT) is a collection of interconnected intelligent devices that exists within the larger network known as the Internet. With the increasing popularity of IoT devices, massive data is generated day by day. The collected data need to be continuously uploaded to the cloud server. Besides, the transmission of data in the cloud environment is performed via the internet, which faces numerous threats. However, the security issue always lacks an effective big data communication. Therefore, a novel technique called Orthogonal Regressed Steepest Descent Deep Structured Perceptive Neural Learning based Secured Data Communication (ORSDDSPNL-SDC) is introduced with higher accuracy and lesser time consumption. The ORSDDSPNL-SDC technique comprises three phases, namely registration, user authentication, and secure data communication. In the ORSDDSPNL-SDC technique, the registration phase is carried out for creating the new ID, and password for each user in the cloud. The IoT device's data is then sent to a cloud server by the cloud user for storage. After that, the orthogonal regressed steepest descent multilayer deep perceptive neural learning is applied to examine the user_ ID with already registered ID based on Szymkiewicz–Simpson coefficient. Then the Maxout activation function is to classify the user as authorized or unauthorized. Finally, the steepest descent function is applied for minimizing the classification error and increasing the classification accuracy. In this way, the authorized or unauthorized user is identified. Then the secured communication is performed with the authorized cloud users. Experimental evaluation is carried out on the factors such as classification accuracy, classification time and error rate, and space complexity with respect to a number of users. The qualitative results and discussion indicate that the proposed ORSDDSPNL-SDC offers elevated performance with regard to achieving higher classification accuracy and minimum error as well as computation time when compared to the existing methods.

Retracted: Respiratory Care of Big Data Communication to Prevent Respiratory Tract Infection Nursing Analysis of Patients with Heart Failure.

[This retracts the article DOI: 10.1155/2022/4310841.].

Autonomous Vehicles: The Cybersecurity Vulnerabilities and Countermeasures for Big Data Communication

The possible applications of communication based on big data have steadily increased in several industries, such as the autonomous vehicle industry, with a corresponding increase in security challenges, including cybersecurity vulnerabilities (CVs). The cybersecurity-related symmetry of big data communication systems used in autonomous vehicles may raise more vulnerabilities in the data communication process between these vehicles and IoT devices. The data involved in the CVs may be encrypted using an asymmetric and symmetric algorithm. Autonomous vehicles with proactive cybersecurity solutions, power-based cyberattacks, and dynamic countermeasures are the modern issues/developments with emerging technology and evolving attacks. Research on big data has been primarily focused on mitigating CVs and minimizing big data breaches using appropriate countermeasures known as security solutions. In the future, CVs in data communication between autonomous vehicles (DCAV), the weaknesses of autonomous vehicular networks (AVN), and cyber threats to network functions form the primary security issues in big data communication, AVN, and DCAV. Therefore, efficient countermeasure models and security algorithms are required to minimize CVs and data breaches. As a technique, policies and rules of CVs with proxy and demilitarized zone (DMZ) servers were combined to enhance the efficiency of the countermeasure. In this study, we propose an information security approach that depends on the increasing energy levels of attacks and CVs by identifying the energy levels of each attack. To show the results of the performance of our proposed countermeasure, CV and energy consumption are compared with different attacks. Thus, the countermeasures can secure big data communication and DCAV using security algorithms related to cybersecurity and effectively prevent CVs and big data breaches during data communication.

Propulsion Energy Minimization for Fixed-Wing-UAV-Assisted Full-Duplex Mobile Relaying via Hybrid Trajectory Design

This paper solved a propulsion energy minimization problem subject to a total data-bit constraint of an unmanned aerial vehicle (UAV)-enabled full-duplex mobile relaying system, where a fixed-wing UAV is dispatched as a full-duplex mobile relay to assist data transfer from a source to a destination. Here, three trajectory flying modes are used, namely, the UAV first flies along a circular path above the source, next flies to the destination along a straight line, and finally flies along a circular path above the destination until all the data-bits have been transferred. Since the propulsion energy minimization problem is a nonconvex mixed integer programming problem, its closed-form solution is not tractable, and hence, it is transformed to three subproblems so as to simplify its solution. After solving the three subproblems, an iterative algorithm is proposed to achieve a suboptimal solution to the propulsion energy minimization problem, leading to a new hybrid circular/straight trajectory (HCST) design. Computer simulations are conducted to validate the effectiveness of the proposed HCST design. It is shown that the proposed HCST design performs well in terms of energy saving for the long distance and big data communication cases compared with the straight or circular flight trajectory design.

Respiratory Care of Big Data Communication to Prevent Respiratory Tract Infection Nursing Analysis of Patients with Heart Failure.

Heart failure is the final stage of the development of heart disease, with a high mortality and disability rate. It poses a serious threat to human health and brings tremendous pressure to human society. Preventing respiratory infections in patients with heart failure is also the first priority of care. This article is aimed at studying the nursing analysis of respiratory tract care based on big data exchanges to prevent respiratory tract infections in patients with heart failure. This article uses benchmark and sample collection. Studies have shown that for Pseudomonas aeruginosa, its resistance to ampicillin, amoxicillin/clavulanic acid, cefazolin, cefuroxime, ceftriaxone, cefotaxime, and cefoxitin has reached more than 80%. It is also suitable for piperacillin, ticarcillin/clavulanic acid, piperacillin/tazobactam, cefepime, aztreonam, gentamicin, tobramycin, ciprofloxacin, and levofloxacin. The resistance rate of stars is within 10%-30%. These antibiotics are effective and can be used for clinical treatment. The drug resistance rates of ceftazidime, imipenem, meropenem, and amikacin were all lower than 10%, and the drug resistance rates of ceftazidime and imipenem were much lower than those reported in the 2016 literature. These antibiotics have become the most effective drugs for the treatment of Pseudomonas aeruginosa infections. Basically, good communication of respiratory care data is realized, thereby preventing respiratory care analysis of patients with heart failure.

Smart grid reliability evaluation and assessment

PurposeSmart grid is an integration between traditional electricity grid and communication systems and networks. Providing reliable services and functions is a critical challenge for the success and diffusion of smart grids that needs to be addressed. The purpose of this study is to determine the critical criteria that affect smart grid reliability from the perspective of users and investigate the role big data plays in smart grid reliability.Design/methodology/approachThis study presents a model to investigate and identify criteria that influence smart grid reliability from the perspective of users. The model consists of 12 sub-criteria covering big data management, communication system and system characteristics aspects. Multi-criteria decision-making approach is applied to analyze data and prioritize the criteria using the fuzzy analytic hierarchy process based on the triangular fuzzy numbers. Data was collected from 16 experts in the fields of smart grid and Internet of things.FindingsThe results show that the “Big Data Management” criterion has a significant impact on smart grid reliability followed by the “System Characteristics” criterion. The “Data Analytics” and the “Data Visualization” were ranked as the most influential sub-criteria on smart grid reliability. Moreover, sensitivity analysis has been applied to investigate the stability and robustness of results. The findings of this paper provide useful implications for academicians, engineers, policymakers and many other smart grid stakeholders.Originality/valueThe users are not expected to actively participate in smart grid and its services without understanding their perceptions on smart grid reliability. Very few works have studied smart grid reliability from the perspective of users. This study attempts to fill this considerable gap in literature by proposing a fuzzy model to prioritize smart grid reliability criteria.

로봇 시뮬레이터를 이용한 와이어 아크 적층 제조 공정의 가상 공간 적층 모션 제어 구현에 관한 연구

Recently, industrial manufacturing has developed into additive manufacturing, benefiting from multi-item small-sized production and effective manufacturing. Importantly, Wire Arc Additive Manufacturing, which uses metal wires, is attracting worldwide attention for its high-quality metal product technology. Technological innovation that combines virtual physics with reality through big data communication, such as process variables along with Wire Arc Additive Manufacturing, is an essential task for implementing smart manufacturing technology. Due to the characteristic of Wire Arc Additive Manufacturing, numerous variable conditions exist, making it difficult to standardize robot

Decision Support System in a Memristor-Based Mobile CIM Architecture Applied on Big Data Computation and Storage

Data computation and storage are essential parts of developing big data applications. The memristor device technology could remove the speed and energy efficiency bottleneck in the existing data processing. The present experimental work investigates the decision support system in a new architecture, computation-in-memory (CIM) architecture, which can be utilized to store and process big data in the same physical location at a faster rate. The decision support system is used for data computation and storage, with the aims of helping memory units read, write, and erase data and supporting their decisions under big data communication ambiguities. Data communication is realized within the crossbar by the support of peripheral controller blocks. The feasibility of the CIM architecture, adaptive read, write, and erase methods, and memory accuracy were investigated. The integrated circuit emphasis (SPICE) simulation results show that the proposed CIM architecture has the potential of improving the computing efficiency, energy consumption, and performance area by at least two orders of magnitude. CIM architecture may be used to mitigate big data processing limits caused by the conventional computer architecture and complementary metal-oxide-semiconductor (CMOS) transistor process technologies.

A relationship matrix resolving model for identifying vital nodes based on community in opportunistic social networks

AbstractThe introduction of the 5G Internet and big data communication into human society underpins the opportunistic social networks (OSNs) to survive. The storage‐carry‐forward mechanism refers to a data transmission strategy of OSNs, seeking any data transmission opportunities to communicate in the mobile process. Accordingly, identifying influential nodes is considered critical to OSNs and an essential prerequisite for developing efficient collaborative transmission algorithms. According to conventional OSNs, however, the node identification methods exhibit several defects (eg, low recognition accuracy, high time complexity, and poor performance in complexity networks). To address the mentioned defects, this study builds a relationship matrix resolving model to identify vital nodes by complying with community, which is capable of effectively identifying influential nodes in the network. The proposed model iterates by drawing upon the community features in the network to yield the optimal solution that satisfies the set condition, that is, the influential nodes in the community. As revealed from the experimental results, the method exhibits high performance.

Silicon photonic transceivers in the field of optical communication

Silicon photonics has developed rapidly in recent years, which has received widespread attention due to the fact that it can overcome the bandwidth bottleneck in optical communications. This paper mainly introduces the key applications of silicon photonics in the field of communication. Through a detailed description of optical transceiver modules in the coherent optical communication and data center, the advantages of silicon optical technology in the field of communication are introduced, such as high speed, low power consumption, high bandwidth and cost-effective. The problems of fabrication, packaging, light source integration and related devices in the current applications of silicon photonics are briefly analyzed. In the future, silicon photonics technology is expected to have better application prospects in long-distance transmission, big data communication and other communication applications.