Things Big Data Research Articles

Water Pollution Detection Using Video Surveillance and Deep Learning

Aiming at the problems that the traditional water quality prediction model is generally not high in prediction accuracy and robustness, a water pollution prediction using deep learning in water environment monitoring big data is proposed. Objective. To optimize and improve the prediction accuracy of the water quality prediction model. Firstly, in the water environment monitoring system, the Internet of Things big data technology is used to accurately sense and monitor the real-time data of sewage treatment equipment and sewage quality. Then, the deep belief network (DBN) is used to build the water pollution prediction model, and the collected sewage treatment data is analyzed to predict the water quality status. Finally, particle swarm optimization algorithm is used to dynamically optimize the number of hidden layer neural units and learning rate in the DBN prediction model, which makes the prediction results more scientific and accurate. Based on the sampling data of Shanghai Jinze Reservoir, the proposed model is experimentally analyzed. The results show that the probability of accurate location of the pollution source is not less than70%. And under the two indicators of chemical oxygen demand and biological oxygen demand, the root mean square error and correlation coefficient are 3.073, 0.9892 and 1.958, 0.9565, respectively, which are better than other comparison models.

Application of standardized management and effect evaluation of chronic obstructive pulmonary disease patients using the big data center of the Internet of Things.

Early detection, diagnosis, treatment and management of chronic obstructive pulmonary disease can lower morbidity and perhaps mortality. This study aimed to evaluate the effect of the application of standardized management against the background of the rapid development of the big data center of modern internet of things technology. Participants ≥40 years of age with chronic obstructive pulmonary disease presenting at Xiamen Medical College Affiliated Haicang Hospital from October 2019 to October 2020 were selected as the observation patients based on the Internet of Things big data center for chronic obstructive pulmonary disease standardized management, and control patients from the community were selected for without down to the chronic obstructive pulmonary disease standardized management. Follow-up after 2 years of patient health records and acute episodes using the World Health Organization Quality of Life Questionnaire-Brief version to evaluate the quality of life of the two groups revealed differences. The results of comparative analysis of the number of acute attacks before and after follow-up in the observation and control groups after propensity score matching showed that the decrease in acute episodes before and after in the observation group was significant compared with that in the control group (t = -3.664, P < 0.001). The quality of life of chronic obstructive pulmonary disease patients indicated that the effect in the observation group was greater than that in the control group according to the World Health Organization Quality of Life Questionnaire-Brief version. In this study, we analyzed the application of modern internet of things technology in the management of chronic obstructive pulmonary disease patients, discussed the effect of standardized management, and promoted the self-management of chronic obstructive pulmonary disease patients. The effectiveness and continuity of the standardized management model for chronic obstructive pulmonary disease implemented in Xiamen city based on the internet of things big data center were considered true and effective.

RETRACTION: Sharing Economy and New Business Model Development Based on Internet of Things Big Data

RETRACTION: Sharing Economy and New Business Model Development Based on Internet of Things Big Data

Investigation on security management risk assessment of accounting resource sharing under internet of things big data technology

Investigation on security management risk assessment of accounting resource sharing under internet of things big data technology

Multi-Criteria Feature Selection Based Intrusion Detection for Internet of Things Big Data.

The rapid growth of the Internet of Things (IoT) and big data has raised security concerns. Protecting IoT big data from attacks is crucial. Detecting real-time network attacks efficiently is challenging, especially in the resource-limited IoT setting. To enhance IoT security, intrusion detection systems using traffic features have emerged. However, these face difficulties due to varied traffic feature formats, hindering fast and accurate detection model training. To tackle accuracy issues caused by irrelevant features, a new model, LVW-MECO (LVW enhanced with multiple evaluation criteria), is introduced. It uses the LVW (Las Vegas Wrapper) algorithm with multiple evaluation criteria to identify pertinent features from IoT network data, boosting intrusion detection precision. Experimental results confirm its efficacy in addressing IoT security problems. LVW-MECO enhances intrusion detection performance and safeguards IoT data integrity, promoting a more secure IoT environment.

Unmanned Aerial Vehicle Assisted Post-Disaster Communication Coverage Optimization Based on Internet of Things Big Data Analysis.

The rapid development of Internet of Things (IoT) communication devices has brought about significant convenience. However, simultaneously, the destruction of communication infrastructure in emergency situations often leads to communication disruptions and challenges in information dissemination, severely impacting rescue operations and the safety of the affected individuals. To address this challenge, IoT big data analytics and unmanned aerial vehicle (UAV) technologies have emerged as key elements in the solution. By analyzing large-scale sensor data, user behavior, and communication traffic, IoT big data analytics can provide real-time communication demand prediction and network optimization strategies, offering decision support for post-disaster communication reconstruction. Given the unique characteristics of post-disaster scenarios, this paper proposes a UAV-assisted communication coverage strategy based on IoT big data analytics. This strategy employs UAVs in a cruising manner to assist in communication by partitioning the target area into multiple cells, each satisfying the minimum data requirements for user communication. Depending on the distribution characteristics of users, flight-communication or hover-communication protocols are selectively employed to support communication. By optimizing the UAV's flight speed and considering the coverage index, fairness index, and average energy efficiency of the mission's target area, the Inner Spiral Cruise Communication Coverage (IS-CCC) algorithm is proposed to plan the UAV's cruising trajectory and achieve UAV-based communication coverage. Simulation results demonstrate that this strategy can achieve energy-efficient cruising communication coverage in regions with complex user distributions, thereby reducing energy consumption in UAV-based communication.

Business ecosystem model innovation based on Internet of Things big data

The research paper presents a novel big data technique for conceptual modelling of Internet of Things (IoT) networks. This method enables the development of models that incorporate IoT based devices into its activities to give a business for the company while taking into account the business operational activities. This study's major goal is to assist management in making quicker effective options that depend on the structured output from their input to develop IoT networks and resources. In the big data business ecological model based on the Internet of Things, 47.5% of the integrated enterprises started to innovate the enterprise ecological model, which can better help enterprises make decisions and plan. In order to include IoT into business model innovation programs to ensure that IoT is taken into account in the overall strategy business planning and decision-making at all levels, such that the modelling and formal descriptions must be used. A number of interviews with workers from various companies and with their experts, throughout such platform were done to further illuminate this research area. Future challenges will create a constant challenge to create and adopt fresh IoT-related business models for large manufacturing organisations. The high-level strategic steps of business model innovation (BMI) are an intriguing topic for current research. These results represent a first step in the exploration of IoT adoption and serve as a springboard for promising new research directions.

Evaluation of the Service Capability of Maritime Logistics Enterprises Based on the Big Data of the Internet of Things Supply Chain System

Marine logistics is one of the main forms of transnational transportation, and the transportation of goods between multinational companies and even countries must be realized through marine logistics. Since maritime logistics involves various factors, such as overseas tariffs and transportation routes, the logistics service capabilities of maritime logistics companies cannot be evaluated by a single standard. This article aims to study the service capabilities of maritime logistics enterprises based on the supply chain system of the Internet of Things big data. In the context of the Internet of Things, the workflow of the intelligent logistics supply chain management system plays an important role, and its main line is the flow of information. Based on the evaluation of logistics service ability and related research on shipping logistics, this paper establishes the evaluation index system of logistics service ability of shipping enterprises and the analytic hierarchy process of logistics service ability evaluation by analyzing the logistics service cases of relevant shipping enterprises. The results show that the logistics capability evaluation method of this study is more targeted for the evaluation of maritime logistics services, with an evaluation accuracy of 97%.

Groundwater development and energy utilization of water environment protection based on big data and Internet of Things

The issues related to the big data and energy reports of the Internet of Things (IoT) have become a hot topic in recent years. As a typical representative of issues related to IoT big data and energy reports, groundwater development, energy utilization and water environment protection have attracted the attention of all sectors of society based on the Internet of Things big data. With the rapid development of China’s economy and society, the demand for water resources is growing day by day. As an important part of people’s living and production water, the exploitation of groundwater is also increasing year by year. However, many problems caused by groundwater exploitation and utilization, such as surface subsidence, collapse, ground fissure, seawater intrusion, water quality deterioration, etc., have seriously affected the economy, society and people’s normal life. At present, China is increasingly short of water resources. The problem of environmental pollution is increasingly serious, which seriously affects the survival of the people. Environmental protection based on big data and the Internet of Things is an effective measure to solve the current problems of groundwater development, energy utilization and water environment protection. Therefore, to ensure China’s economic development and residents’ water safety, it is not only necessary to reasonably develop and use water resources, but also to protect the water environment to avoid water shortage caused by water quality problems. On this basis, this paper analyzed and discussed the current situation of groundwater resources utilization in China, with a view to fundamentally solving the problems of groundwater energy utilization and water environment protection in China. Research showed that the energy utilization rate had been in a low state until 2017, with the highest water pollution level of 92% and poor water quality. Since 2018, due to the maturity of the big data technology of the Internet of Things, energy utilization has been generally improved in all regions. The degree of water pollution has reached a new historical low. The quality of water sources has been improved, and the residents’ satisfaction with the energy utilization of groundwater development and water environment protection has reached 96%. The changes in various signs have laid the foundation for the long-term development of the country. The research in this paper has important guiding significance for solving problems related to IoT big data and energy reports.

Emotional Linkage among Teacher-Student in English Multimedia Smart Classroom Teaching in the Internet of Things Big Data Era

Emotional Linkage among Teacher-Student in English Multimedia Smart Classroom Teaching in the Internet of Things Big Data Era

Discussion on Construction Framework and Key Content of Integrated Medical Internet of Things

Since the internet of things was listed as a national emerging strategic industry, its development in China has received unprecedented attention. This study takes the lead in proposing the concept and construction framework of the integrated medical internet of things, analyses and discusses the integrated medical internet of things from four aspects: the integrated internet of things infrastructure, the internet of things security management application construction, the internet of things big data application construction, and the integrated management platform. The integrated medical internet of things starts from medical management, based on the hospital's existing information construction foundation, and uses internet of things related technologies to build an internet of things integrated service platform for medical quality and safety.

Intelligent power outage information management system based on power internet of things platform

With the new round of distribution network construction, the task of power outage notification is heavy. The issuance of a large number of the notification leads to the high cost of power supply enterprises, and the high rate of power outage notification return also affects the service quality of the enterprises.In this paper, using the intelligent power outage information management system based on the electric power internet of things platform, in allusion to the inaccurate announcements because of the the large amount of power outage information,the untimely announcements caused by the sudden failure outage information and the pain points which affect customer satisfaction,through the power ubiquitous in the Internet of Things big data platform and 5G communication technology,improving the power failure information intelligent analysis and decision - making ability. Through intelligent analysis of power outage information, active identification, information interaction, real-time closed loop and other links, it conforms to the trend of big data in the Internet of Things, strengthens the accuracy and timeliness of power outage information announcement, and improves customer satisfaction[10].

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.

Sharing Economy and New Business Model Development Based on Internet of Things Big Data

The sharing economy and new business models have emerged as a result of the economy’s rapid growth in recent years. The sharing economy and innovative business models have enabled people to transact with one another, saving both time and money. The growth has been aided by the introduction of IoT big data. This study will look at how to use big data from the Internet of Things to examine the growth of the sharing economy and new business models. This study proposes a BP neural network algorithm based on the big data of the Internet of Things (IoT), which can not only effectively classify a large amount of data in the sharing economy but also predict the development direction of the sharing economy and new business models. The experimental results of this study show that the economic benefit of shared office in 2017 is 11 billion yuan, and the economic benefit of shared office in 2018 is 20.6 billion yuan, an increase of 87.3% in 2018 compared with 2017. The economic benefit of knowledge and skills in 2017 was 138.2 billion yuan, and the economic benefit of shared office in 2018 was 235.3 billion yuan, an increase of 70.3% in 2018 compared with 2017. It can be seen that the economic benefits brought by the sharing economy to the country are very high, and there is also a lot of room for growth. Therefore, based on the background of IoT big data, it is essential to conduct research on the development of sharing economy and new business models.

Wisdom Tourism Management Mode in the Background of Big Data of the Internet of Things

Since the rapid development of the global economy, tourism has become an indispensable entertainment and relaxation item in people’s lives. However, the traditional tourism management mode is slow in conveying tourism information, and there is no reasonable planning of tourism projects for tourists, and it is even more difficult to accurately locate the travel preferences of travelers and make specific travel plans, so that travel resources cannot be fully utilized. Therefore, the Internet of Things big data technology and travel management are combined. For this reason, a wisdom tourism management model based on the big data background of Internet of Things (IoT) is developed. In order to timely arrange and adjust work and travel plans, wisdom tourism makes use of cloud computing, IoT, and other new technologies, with the help of portable mobile terminal devices, so that the tourists can receive timely information about tourism resources, tourism activities, and tourism surroundings to achieve intelligent perception of tourism information, timely arrangement effect. Wisdom tourism can make use of undeveloped tourism resources as much as possible and drive the development of surrounding industries, and promote the economic development of tourist areas. This paper mainly compares the smart tourism management mode with the traditional tourism management mode. The experiment shows that the development degree of the wisdom tourism management mode accounts for 82.8% of the total tourism resources. The traditional tourism management model is only 70.1%, and the GDP of the economy around the tourist area in the wisdom tourism management model in one year is 1.4 times higher than that of the traditional tourism management model. In terms of tourism experience, the wisdom tourism management model is also much better than the traditional tourism management model. And the lower the average age of the experimenter, the higher the favorability of wisdom tourism, up to 98%. It highlights the advantages of the wisdom tourism management model in personalized tourism, tourism development, and surrounding economic growth.

Research on Performance Optimization Algorithm of Resource and Environment Audit Based on Computer Technology.

With the rapid development of the economy and society, the sustainable development of resources and environment has been paid more and more attention. As an important part of the national environmental supervision system, resources and environmental audits have attracted great attention from the society. In order to explore the application effect of intelligent networking technology in resource and environment audit, this study carries out monitoring sampling, information collection, and later data processing optimization in resource and environment audit projects through the reference of artificial intelligence technology and method and combined with Internet of things big data analysis, which brings new Turks to the development of natural resource and environment audit and helps to improve the efficiency of audit work. Through the application of new technology in the sampling process of environmental information, the staff use new technology to collect, analyze, and mine data and explore how the new technology can promote the efficiency of resource and environmental audit and better promote the construction of ecological civilization.

Mobile internet big data technology-based echo loss measurement method of optical communication system

The paper proposes a method to analyze the process of echo loss in an optical communication system in combination with mobile internet big data technology. The optical echo loss tester shall include the output connector, the standard reflector and the automatic unite to process and display the device to be measured. Using mode-coupling theory, we can calculate the loss when a light in an optical fiber flows through a fiber optic connector under various effect variables; then analytical formulas are developed, to calculate insertion and echo loss in a real-life situation. In this research, we propose a method for measuring echo loss in optical communication systems using mobile internet big data technology. The effect of this method is evaluated combined with the simulation test, based on the research carried out by research. The optical communication echo loss measurement based on mobile Internet of Things big data technology has some influence.

Internet of Things Big Data Security in Cloud via Stream Cipher and Clustering Model

Internet of Things Big Data Security in Cloud via Stream Cipher and Clustering Model

Design of E-commerce Data Scalable Storage System Based on Mobile Internet Communication Technology

The traditional encrypted storage system is inefficient when it encrypts the data of the Internet of Things, and there are few IOT data nodes that can be encrypted in a short time. In order to solve the above problems, a new Internet of Things data effective information encryption storage system is proposed. The hardware and software of the system are mainly designed. The chip selected for the collector is TTSAD251, which can expand the collection range. The processor is set with multiple cores to reduce the system power consumption. The memory uses SPRTAN-2 chip as the structure chip. The software work consists of three parts: collecting effective information of Internet of Things big data, establishing encrypted documents and storing effective information of big data of Internet of Things. In order to detect the working effect of the system, the experimental comparison with the traditional system shows that the proposed encryption storage system can improve the storage range of big data effective information of the Internet of Things by 20.58%, and the work efficiency by 5.64%. Compared with the traditional system, the designed system also has obvious advantages in the number of big data node secrets. In different files, the average number of big data information node encryption in this system is about 166,700. The experimental data show that the designed system has ideal application performance and provides a reliable basis for related fields.

The use of edge computing-based internet of things big data in the design of power intelligent management and control platform

The use of edge computing-based internet of things big data in the design of power intelligent management and control platform