Big IoT Research Articles

Security of Big Data over IoT Environment by Integration of Deep Learning and Optimization

This is especially true given the spread of IoT, which makes it possible for two-way communication between various electronic devices and is therefore essential to contemporary living. However, it has been shown that IoT may be readily exploited. There is a need to develop new technology or combine existing ones to address these security issues. DL, a kind of ML, has been used in earlier studies to discover security breaches with good results. IoT device data is abundant, diverse, and trustworthy. Thus, improved performance and data management are attainable with help of big data technology. The current state of IoT security, big data, and deep learning led to an all-encompassing study of the topic. This study examines the interrelationships of big data, IoT security, and DL technologies, and draws parallels between these three areas. Technical works in all three fields have been compared, allowing for the development of a thematic taxonomy. Finally, we have laid the groundwork for further investigation into IoT security concerns by identifying and assessing the obstacles inherent in using DL for security utilizing big data. The security of large data has been taken into consideration in this article by categorizing various dangers using a deep learning method. The purpose of optimization is to raise both accuracy and performance.

Wireless Multifunctional Display Platform for Visual Communication Design Based on IoT Big Data

An important role of display visual design is advertising and advertising communication, which is an integrated medium, that is, a medium for communicating information. Visual communication design is the most direct way of information transmission in modern society. In today’s increasingly complex and diversified information, visual communication design has been more and more widely used in terms of form and function. On the display platform, the use of visual elements and the expression of visual communication design allows us to examine the different spatial experiences that visual communication design brings to people from a new perspective and adjust the way it is communicated, so as to find a scientific expression. This paper focused on the role of visual communication in display design and discussed its specific application in display design. The use of visual elements in the display space provides a new visual platform for spatial information, making the space design more connotative and cultural. For the smooth experience of media fusion design, it is necessary to ensure that information can be transmitted quickly and smoothly in every link of its processing. In the data write throughput experiment of the microperceptual data layer, the maximum write speed increased by 85.2% compared with the direct write method using the TSBPS method. The findings demonstrated that the IOT-HSQM model’s strategy of first queuing and then segmentation writes may more effectively fulfill the demands of high bandwidth than straight data transmission to HDFS (Hadoop Distributed File System).

Blockchain of Resource-Efficient Anonymity Protection with Watermarking for IoT Big Data Market

According to the ever-growing supply and demand of IoT content, IoT big data in diversified applications are deemed a valuable asset by private and public sectors. Their privacy protection has been a hot research topic. Inspired by previous work on bounded-error-pruned IoT content market, we observe that the anonymity protection with robust watermarking can be developed by further pruning data for better resource-efficient IoT big data without violating the required quality of sensor service or quality of decision-making. In this paper, resource-efficient anonymity protection with watermarking is thus proposed for data consumers and owners of IoT big data market via blockchain. Our proposed scheme can provide the IoT data with privacy protections of both anonymity and ownership in IoT big data market with resource efficiency. The experiments of four different-type IoT datasets with different settings included bounded-errors, sub-stream sizes, watermark lengths, and ratios of data tampering. The performance results demonstrated that our proposed scheme can provide data owners and consumers with ownership and anonymity via watermarking the IoT big data streams for lossless compressibility. Meanwhile, the developed DApp with our proposed scheme on the Ethereum blockchain can help data owners freely share and trade with consumers in convenience with availability, reliability, and security without mutual trust.

Research on the Use of Task-Based Language Teaching Method in English Language Teaching Based on Big Data 5G IoT Scenario

The state implemented the compulsory education standard for full-time students in 2001, which clearly stipulates that English teachers should promote special language teaching methods, develop comprehensive language use skills, and propose teaching methods such as practice, cooperation, and communication. The task-based language teaching method has become a commonly used method in English teaching, and it is necessary to study the application of task-based language teaching method in English courses and try to apply the task-based language teaching method effectively. This study analyzes the needs of English teaching in the context of big data 5G IoT scenario, new curriculum reform, and theories related to English teaching methods in order to explore new methods of English teaching, verify its feasibility and necessity, and prepare reference materials for future grammar teaching research.

Construction of School-Enterprise Cooperation Practice Teaching System under the Big Data Internet of Things Industry Collaborative Innovation Platform.

The school-enterprise cooperation practice teaching system has increasingly become the core of the field of education research on the basis of the Internet and mobile communications. Based on the big data IoT theory and relying on the industrial collaborative innovation platform, this study designs and implements a general service platform for school-enterprise cooperation practice teaching for IoT applications. The platform is divided into two parts: the IoT data transmission part and the Web practice teaching service section. On the Internet of Things big data transmission platform, after the big data processing provided by the intelligent industry collaborative innovation end is completed, it disguises as a web practical teaching service platform built by the Internet of things framework based on B/S architecture. It solves the problem of data transmission on the order of millions. During the simulation process, the platform realizes flexible deployment and automatic integration of online upgrades. The code management script module based on scripts such as SQL, Python, and shell can complete the online platform. The automatic upgrade finally achieves the goals of the platform being easy to maintain, simple to deploy, and flexible in business logic. The experimental results show that starting from the actual situation of a digital intelligent collaborative innovation platform in a university, the practical teaching system designed and implemented adopts the object-oriented development method, and has a three-tier architecture of the collaborative innovation platform. In addition, the functional modularization and standardization reach 88.7% and 79.4%, respectively, effectively improving the performance of the practical teaching system.

Problems and Countermeasures of Coal Mine Engineering Management under the Background of Big Data and IoT

In the context of the era of big data and the Internet of Things, there are certain engineering management problems in the development of my country’s coal mine enterprise engineering management. Based on the research content of domestic coal enterprises in the construction and engineering management of production and engineering, comprehensive research and analysis of problems at different stages of different stages, optimizing the improvement of coal mine engineering management levels, were done. In the process of various factors that affect the safety of coal mine safety production and the high-quality development of enterprises, this article focuses on the problems and hidden dangers affecting the management of coal mine engineering management and focuses on improving measures to improve the safety of coal mines and improve corporate safety benefits. After conducting research, an evaluation and analysis model, a comprehensive research on the various factors that affect the safety of coal mine, conducted a comprehensive research and conducted research on factors and measures affecting engineering management. A comprehensive data analysis model and factor management model based on coal mine engineering management are constructed. In the context of big data and the Internet of Things, targeted evaluation and analysis based on different security risks and engineering management issues were proposed with feasible opinions and measures.

Methods of Nonlinear Time Series Cycle Analysis in Big Data Environment and IoT Application

There have been a lot of changes in nonlinear time series analysis techniques over the last few decades. They came from their time series background and have grown some of these techniques to try to fill in a gap in the ability to model and predict certain types of data, like chaotic and fixed systems. It is possible to find a lot of these systems all over different places in both natural and human worlds. This study explains how these techniques came to be, what they are based on, and how some of them can predict what will happen in the future. This study is trying to figure out how they work and what they could be used for. The contribution of this study is a different way to look at time series data. It looks at how the data is made and how big the time series is. Here, we talk about how the model comes together in terms of order. In this example, we look at the time series of the sectorial indices on the National Stock Exchange (India) to show that the model is getting close. Convergence also points out that, when we fit the same number of data points to the same model, we get the same thing every time. This shows that a true model correctly predicted the value of the future and that data always comes together in nature. A study found that the size of a big data time series should be taken into account when looking at the time series’ realization. This means that the DGP intervals for each study period can be the best representative intervals for that time period.

A Framework for Analysis of Incompleteness and Security Challenges in IoT Big Data

Data quality (DQ) is gaining traction as a new area to focus on for increasing organisational effectiveness. Despite the fact that the implications of poor data quality are often felt in the day-to-day operations of businesses, only a small percentage of companies use particular approaches for measuring and monitoring data quality. In this paper, the focus is on the efficiency and incompleteness of IOT big data and since security is the major concern in large clusters, map reduce technique is proposed in order to overcome the issues and challenges faced on regular basis while dealing with huge volume of information. Dealing with veracity is need of an hour and therefore, the work in this paper can be categorised into analysis, observation, proposing model and testing its accuracy and performance.

An ontology oriented service framework for social IoT

Social IoT (S-IoT) is a robust paradigm linked with big data and IoT for the sharing and interaction of various devices and services to obtain the desired goal. In recent applications, it is a challenging task to maintain the trust between various linked devices for the growth and creation of IoT systems. Therefore, this paper presents a separation architecture based on the Quality of Services by managing the trust between the IoT devices. The proposed model is novel based on the selection of the attribute set against the separated class. The training and classification are done by using the ontology architecture. The data architecture is processed by a k-means algorithm for the generation of the class labels into three different groups. The groups have been labeled using a Fuzzy logic inference engine and a Genetic Algorithm has been used for optimization. The performance measure has been evaluated using multiple multiclass classifiers for accuracy, True Positive Rate, (TPR), and False Positive Rate (FPR).The simulation results elucidate that accuracy of the proposed technique is improved by 10.7%, FPR by 6%, and TPR by 11% in comparison to state of art techniques.

Application Analysis of Overlapping Community Detection Algorithms for Multidimensional Network Big Data and IoT

Capture the design and elements of these layers. Each layer corresponds to an alternative connection type between hubs in the normal world and requires tracking down communities in multidimensional networks. Most community disclosure approaches for multidimensional networks, then again, may ignore the transaction between layers or a layer’s unmistakable topological construction. Moreover, most of them are just equipped for distinguishing nonoverlapping communities. In this exploration, we offer another multidimensional network community disclosure strategy that exploits the connection among layers and the extraordinary geography of each layer to track down overlapping communities. First, use an overall assessment of edge behavior within and between layers to calculate the similarity of edges from similar layers and cross layers. You can then use these similarities to build a dendrogram of a multidimensional network that takes into account both characteristic topology structures and basic transactions. Finally, you can remove the overlapping communities in these layers by splitting the dendrogram and adding another community thickness metric for the multidimensional network. We show that our strategy is precise in recognizing overlapping communities in multidimensional networks by applying it to both manufactured and genuine world datasets. In chart and enormous information examination, community detection is a commonplace issue. It involves finding groups of firmly associated hubs with little associations with hubs outside the bunch. Distinguishing communities in huge scope networks, specifically, is a basic errand in numerous logical fields. In the writing, community detection techniques have been demonstrated to be wasteful, bringing about the improvement of communities with uproarious communications. To defeat this requirement, a framework that decides the best community among multifaceted networks in light of important determination standards and substance dimensionality should be created, eliminating loud communications in a continuous setting. Our outcomes likewise show that it is vital to utilize integral measurements to assess the exhibition of overlapping community detection calculations. Performance metrics, such as the NMI or the Omega Index, only measure the overall quality of a detected cover, whereas complementary metrics give us more information about the behavior of each algorithm in detecting overlapping communities. Finally, while some algorithms perform well on synthetic networks, none of the algorithms can detect the community structure in real networks. This is due to the detected communities of the algorithms being substantially different from the communities defined by the meta-data.

스마트 글래스의 LED 배치 및 회로 생성을 위한 CAD시스템

Industrial companies aim to improve their productivity by using the latest technologies such as Big Data, artificial intelligence, and IoT. However, small and medium-sized companies (SMEs) often lag behind with the introduction of new technologies and refrain from automating their production processes. In this regard, SMEs that produce smart glass, the production of which requires advanced technology, often continue to depend on manual production processes. We analyzed the existing production process to determine whether the integration of CAD technologies could be used to improve the productivity. This study was limited specifically to the development of algorithms for placing the LEDs in the glass in correspondence with the designed shape and to automatically generate a circuit for delivering current to the LEDs. The arrangement of the LEDs is designed to minimize the discrete Fréchet distance in consideration of the “local maximum point of curvature” and “kink point,” and the circuit is automatically organized by grouping a certain number of LEDs to supply similar current to the LEDs. Finally, the time required for LED placement and circuit preparation was reduced from 2 hours to 10 minutes.

Innovation of Ideological and Political Education Management of College Students Based on IOT Big Data Technology in a Wireless Network Environment.

Universities are not only the front line of national ideological and political works but also the gathering place of national talent training, which is of far-reaching significance to the national strategic development, and it is vital to do a good job of ideological and political education in universities for the construction and development of the country. “The university has become a place where wireless network technology can be used. The paper examines the current state of mobile learning in the United States and abroad, explains the principles for developing a mobile ideological and political learning system, designs and studies the system's overall structure and functional modules, and shows how the system's application can replace traditional ideological and political learning methods, allowing students to improve their ideological and political abilities using mobile devices.

XSS Armor: Constructing XSS Defensive Framework for Preserving Big Data Privacy in Internet-of-Things (IoT) Networks

Big data characterizes superfluity of voluminous data that may be in unstructured, structured and/or semi-structured format. Internet of Things networks emerged as one of the biggest sources of big data, stored at the cloud servers. Thereby, it inflates the threats of security attacks such as cross-site scripting (XSS) for stealing the sensitive information and violates the user’s privacy. It leverages an adversary to intervene into user’s individual space. Thus, in this paper, we propose an XSS defensive framework, named as Big IoT Data XSS Armor, to protect user’s privacy in IoT networks. It is a server-side framework that mitigates nonpersistent and persistent XSS attack. Former attack is detected by measuring the similarities in request and response URL with the XSS attack strings. To identify persistent XSS attack, the proposed framework operates by unveiling the irregularities between the genuine features and generated features. The experimental outcomes yield that this framework performs efficiently in shielding against XSS attack and surpasses the detection rate of other existing XSS thwarting techniques because it attains an accuracy of 98.9% and F-measure of 98.4%.

Data anonymization evaluation for big data and IoT environment

The growth of big data can increase risks of re-identification in complex IoT environment. Data anonymization is widely used to prevent shared data from being re-identified private or sensitive information from anonymized data with other available data. It is very important to understand the risks of re-identification of results and process of commonly used anonymization techniques before properly mitigating. This work proposes a data anonymisation evaluation framework that can evaluate commonly used data anonymization algorithms in terms of privacy preserving level, data utility, and performance. Specifically, data utility, privacy metrics, and information loss were used to evaluate data anonymization schemes that can further optimise the data anonymization process to keep risks of re-identification low. Experiment results demonstrate that the proposed solution can effectively evaluate the performance and de-identification features that can help to prevent inappropriate usage of anonymized data.

Online Shopping Brand Sales Based on IoT Big Data Processing.

As a pair of closely related technical fields, “Internet of Things” and “big data” are gradually applied to the analysis of online shopping, which is very beneficial to promote the healthy and orderly development of the online shopping industry. This article uses the big data processing technology of the Internet of Things to analyze the influencing factors of online shopping brand sales based on the data collected by online questionnaires. It draws certain rules from it, and provides relevant marketing strategies for online shopping brand sales to assist its market positioning. According to the survey, the five aspects of online evaluation, brand awareness, online shopping community, online public opinion influence of related brands, and other brand information acquisition channels all have an impact on online brand sales. Especially women aged 25–35 will pay attention to online information, and 195 people will not choose to buy from brands with negative news. And during the purchase process, 186 people will check the online reviews of related brand products. Among them, there are 183 men aged 25–35 who will pay attention to online reviews.

Information security and privacy in hospitals: a literature mapping and review of research gaps

ABSTRACT Information security and privacy are matters of concern in every industry. The healthcare sector has lagged in terms of implementing cybersecurity measures. Therefore, hospitals are more exposed to cyber events due to the criticality of patient data. Currently, little is known about state-of-the-art research on information security and privacy in hospitals. The purpose of this study is to report the outcome of a systematic literature review on research about the application of information security and privacy in hospitals. A systematic literature review following the PRISMA methodology was conducted. To reference our sample according to cybersecurity domains, we benchmarked each article against two cybersecurity frameworks: ISO 27001 Annex A and the NIST framework core. Limited articles in our papers referred to the policies and compliance sections of ISO 27001. In addition, most of our sample is classified by the NIST function “Protect,” meaning activities related to identity management, access control and data security. Furthermore, we have identified key domains where research in security and privacy are critical, such as big data, IOT, cloud computing, standards and regulations. The results indicate that although cybersecurity is a growing concern in hospitals, research is still weak in some areas. Considering the recrudescence of cyber-attacks in the healthcare sector, we call for more research in hospitals in managerial and non-technical domains of information security and privacy that are uncovered by our analysis.

Upgrading of IOT Big Data Governance Scheme in Microenterprise Governance

The accelerated development of the tertiary industry is the inevitable result of productivity improvement and social progress. Vigorously developing the tertiary industry is conducive to enhancing the stamina of agricultural production, promoting the socialization and professionalization of industrial and agricultural production, optimizing the production structure, promoting the full development of the market, and alleviating employment pressure so as to promote the sustained, rapid, and healthy development of the entire economy. Microenterprises are one of the carriers for the development of the tertiary industry. Microenterprises can effectively alleviate the country’s increasingly serious employment problems. However, with the saturated operation of microenterprises, the competition of microenterprises is becoming more and more intense. Small- and medium-sized enterprises are under the pressure of competition in terms of manpower, location, consumption level, and supply of goods. At the same time, small- and medium-sized enterprises are also facing problems such as high operating pressure, rising costs, financing difficulties, and heavy taxes and fees. In this environment, if small enterprises want to maintain long-term development, they must adapt to new forms and take active measures. Small- and medium-sized enterprises must use flexible and diverse forms of employment, actively adjust the industrial structure, vigorously develop the tertiary industry, and absorb more employed labor. The purpose of this paper is to study the use of IOT big data to optimize and upgrade the governance scheme of microenterprises. The purpose is to improve the governance effect of microenterprises and promote the development of microenterprises. This paper proposes optimal governance based on the collaborative algorithm and Pareto algorithm and conducts governance evaluation in multiple aspects of microenterprises, respectively. After obtaining the relationship with profit maximization, this paper finally analyzes and summarizes, obtains two optimal schemes, and evaluates and compares these two schemes with the traditional scheme. The results show that the profit maximization degree of the two schemes can reach 92%. Compared with the traditional scheme, the maximum profit of small enterprises after the implementation of governance has increased by about 30%.

Research on Badminton Player’s Step Training Model Based on Big Data and IoT Networks

In view of the poor effect of badminton players’ physical fitness and response training, this paper puts forward the construction method of badminton players’ pace training model based on big data, constructs badminton players’ pace training index by combining big data technology, optimizes the pace training evaluation algorithm, and puts forward the corresponding Badminton Players’ pace training method to achieve the model design goal. Finally, experiments show that the badminton athlete’s pace training model based on big data has high practicability in the process of practical application and fully meets the research requirements.

What's driving the diffusion of next-generation digital technologies?

The recent development and diffusion of next-generation digital technologies (NGDTs) such as artificial intelligence, the Internet of Things, big data, 3D printing, and so on are expected to have an immense impact on businesses, innovation, and society. While we know from extant research that a firm's R&D investment, intangible assets, and productivity are factors that influence technology use more generally, to date there is little known about the factors that determine how these emerging tools are used, and by who. Using Probit and OLS modeling on a survey of 12,579 South Korean firms in 2017, we conduct one of the first comprehensive examinations highlighting various firm characteristics that drive NGDT implementation. While much of the literature assesses the use of individual technologies, our research attempts to unveil the extent to which firms implement NGDTs in bundles. Our investigation shows that more than half of the firms that use NGDTs deployed multiple technologies simultaneously. One of the insightful complementarities identified in this research exists amongst technologies that generate, facilitate and demand large sums of data, including big data, IoT, cloud computing and AI. Such technologies also appear important for innovative tools such as 3D printing and robotics.

JUpdate: A JSON Update Language

Although JSON documents are being used in several emerging applications (e.g., Big Data applications, IoT, mobile computing, smart cities, and online social networks), there is no consensual or standard language for updating JSON documents (i.e., creating, deleting or changing such documents, where changing means inserting, deleting, replacing, copying, moving, etc., portions of data in such documents). To fill this gap, we propose in this paper an SQL-like language, named JUpdate, for updating JSON documents. JUpdate is based on a set of six primitive update operations, which is proven complete and minimal, and it provides a set of fourteen user-friendly high-level operations with a well-founded semantics defined on the basis of the primitive update operations.