In the wake of the acceleration of China’s industrialization and rapid economic growth, environmental pollution has also attracted great attention. The technological innovation of heavily polluting enterprises is conducive to reducing pollution emissions and promoting environmental health. The financial investment tendency and behavior of real enterprises have a significant impact on the technological innovation decision-making of enterprises. A panel model is used in this paper in order to empirically test the impact of financialization of Chinese heavily polluting enterprises on technological innovation based on the data of Listed Companies in Chinese heavily polluting industries from 2008 to 2019. The + results show that the financialization of heavily polluting enterprises has a significant crowding out effect on technological innovation. After introducing arbitrage motivation as the regulating variable, further research finds that arbitrage motivation weakens the inhibitory effect of enterprise financialization on technological innovation, that is, the stronger the arbitrage motivation, the smaller the negative effect of financialization on enterprise technological innovation, which weakens this crowding out effect. Finally, the listed enterprises in heavily polluting industries are divided into state-owned enterprises and non-state-owned enterprises according to their corporate attributes. Compared with state-owned enterprises, the financialization of non-state-owned enterprises has a greater squeeze out of technological innovation; and arbitrage motivation has a more significant regulatory effect on the impact of enterprise financialization on technological innovation. With the further development of ecological civilization, the 19th National Congress of the Communist Party of China put forward a new concept on environmental issues: “Optimize the ecological security barrier system, build ecological corridors and biodiversity protection networks, improve the quality and stability of the ecosystem, firmly establish the concept of socialist ecological civilization, and promote the formation of a new pattern of harmonious development of man and nature.” However, along with China’s accelerated industrialization process and rapid economic growth, environmental pollution has also received great attention. Currently, China’s economy is in a critical period of transformation, and is facing new challenges and opportunities. As the foundation of economic development, the real economy should be led by science and technology innovation to realize the transformation and upgrading of the real economy. As the financialization of China’s real economy has deepened in recent years, real enterprises, as the main force of technological innovation, tend to be detached from reality. At present, the financialization at the enterprise level is mainly reflected in the fact that most entity enterprises put a large amount of capital into financial activities such as stock investment and bank wealth management. In addition, a large amount of industrial capital flows to the field of investment real estate, and the phenomenon of “idling”, in which funds are away from the real economy in the virtual economy, is widespread. However, while there are risks in the financialization of enterprises, there are also certain opportunities. Due to the excessive profits in the financial market, corporate financial investment activities can increase the financing capacity of enterprises, and financial investment activities can also create sources of funds for technological innovation projects, and can also control the leverage ratio and the scale of financial investment within a reasonable range to achieve a reasonable and effective operation.From the source of pollution, heavily polluting enterprises are the main body of responsibility for ecological environment pollution. In today’s world, ecological environment problems are mainly complicated and diversified, and there is a difficult situation of point-surface compounding, multi-source coexistence and multi-type overlay. Environmental pollution has become one of the problems that must be solved in the process of economic development in all countries of the world, and it is beneficial to promote the sustainable development of the global economy to deal with the relationship between environment and economy. Among them, technological innovation has become an indispensable and important link in the process of combating environmental pollution and improving environmental quality. In fact, in the initial stage of technological innovation, the use of resources and energy consumption caused by the expansion of economic scale and the development of industrial technology are the keys to environmental problems. However, Han Jian and Sheng Peihong , Huang JAs China’s economy enters a new era, innovation is the key to achieving stable transformation. The Outline of the 14th Five-Year Plan for National Economic and Social Development of the People’s Republic of China emphasizes the need to adhere to innovation-driven development and to comprehensively shape new advantages in development. Facing the challenges of the financial environment, the ability of enterprises to maintain sustained innovation is an important issue related to whether China’s economy can cross the middle-income trap. By combing through the relevant literature, we can find that studies on the impact of financialization of non-financial firms on firms’ technological innovation have not reached a uniform conclusion. According to their research findings, they can be broadly classified into three categories. First, the financialization of enterprises has played a “reservoir” effect on enterprise technology innovation. The second is the crowding-out effect of enterprise financialization on enterprise technology innovation. Third, the impact of enterprise financialization on enterprise technology innovation is not a simple promotion or inhibition relationship, but shows different characteristics under the influence of many factors.Some researchers argue that the financialization of firms facilitates the alleviation of financing constraints, which improves their technological innovation, research and development capabilities. Kim et al.  and AlmeSome researchers believe that enterprise financialization will have a crowding-out effect on enterprise technological innovation. Hu Yiming, Wang Xueting et al. , Wang HoSome scholars argue that the impact of firm financialization on technological innovation is not a simple facilitative or inhibitory relationship. Guo Liting  studied Most previous studies focused on the impact of enterprise financialization on enterprise technological innovation, and did not conduct separate studies based on the nature of the enterprise. Additionally, this article intends to expand the research from the following aspects based on the previous research experience: (1) When conducting empirical research on the impact of enterprise financialization on enterprise technological innovation, add arbitrage motivation as a moderating variable. (2) As the “Outline of the Fourteenth Five-Year Plan for the National Economic and Social Development of the People’s Republic of China” emphasizes the need to enhance the awareness of ecological and environmental protection in the whole society, and in-depth fight for pollution prevention and control. Therefore, heavily polluting enterprises will also become the focal point of attention of the whole society. This article sets the scope of enterprises in the heavily polluting industries, focusing on the impact of the financialization of heavily polluting enterprises on the technological innovation of enterprises. (3) Divide the sample into state-owned enterprises and non-state-owned enterprises to study separately, focusing on their differences.Milberg and Winkler  defined China’s Decisions on Strengthening Technological Innovation, Developing HighTechnology, and Realizing Industrialization, technological innovation is defined as: technological innovation is a creative activity for enterprises to realize market value. Enterprises improve their existing products, production methods and operating modes by adopting new processes and new concepts, thereby improving the quality of their products and services, and ultimately expanding their market share by virtue of their advantages of product and service. However, uncertain sources of financing and high costs restrict the technological innovation activities of enterprises. In addition, the input cost for technological innovation is higher than the social cost, and the benefits obtained are lower than the social benefits. Therefore, many companies prefer to invest a higher rate of return on financial items in the case of limited funds.In Through combing the relevant literature, it can be found that the research on the impact of non-financial enterprise financialization on technological innovation of enterprises has not reached a unified conclusion. According to the results of the research, it can be broadly divided into three categories. First, the financialization of enterprises has played a “water reservoir” effect on enterprise technological innovation. Second, the crowding-out effect of enterprise financialization on enterprise technology innovation. Third, the impact of enterprise financialization on enterprise technology innovation is not a simple promotion or inhibition, but shows different characteristics under the influence of a variety of factors. Xu Shan, Liu Dechi  and Cui Hypothesis 1.The financialization of heavily polluting enterprises exerts a “crowding effect” on technological innovation of enterprises, and there is a negative correlation between the two.Market arbitrage is an activity in which entities put funds into financial markets to obtain excess returns. Some scholars have pointed out that due to limited enterprise capital, enterprise managers who invest funds into the financial market for market arbitrage motivations will inevitably crowd out the investment in enterprise technological innovation, thereby weakening the level of technological innovation . TherefoHypothesis 2.When the market arbitrage motivation of heavily polluting enterprises is stronger, the negative correlation between the degree of financialization and technological innovation capability is more significant.Since endogenous financing has excellent properties such as lowing financing costs, will not dilute the original shareholders’ equity, and reduce shareholders’ tax costs, enterprises will give priority to endogenous financing when considering financing channels. The net cash flow from operating activities is an important source of endogenous financing. When an enterprise faces a serious shortage of cash flow, it is more likely to invest the limited resources of its internal balance into financial investment with short payback period, rather than into technological innovation with a long cycle and uncertain returns. In this way, the normal production and operation activities are guaranteed. Therefore, the crowding out effect is more significant at this time . When faHypothesis 3.Compared with heavily polluting enterprises with abundant cash flow, the more serious the cash flow shortage faced by the enterprise, the higher the possibility of arbitrage-driven financial assets, which has a more significant negative effect on technological innovation.There are significant differences in the degree of financialization between state-owned enterprises and non-state-owned enterprises in China ,18. TherHypothesis 4.The financialization of heavily polluting enterprises presents differences in enterprise attributes for technological innovation.Hypothesis 5.Arbitrage motivations show differences in enterprise attributes to the extent that the financialization of heavily polluting enterprises affects technological innovation of enterprise.This paper mainly studies the impact of the financialization of heavily polluting enterprises on technological innovation, in the empirical analysis, that the years and industries will affect the level of technological innovation is taken into account. For example, first, the technological innovation ability and level changes over time, the technological innovation development in the past is slow, but is faster now; second, companies in different industries also have great differences in their technological innovation ability. Therefore, in this paper, a two-way fixed effect model that controls both year effect and industry effect is chosen, rather than a random effect model.Learn from the previous research literature on financialization and technological innovation. This chapter establishes a quantitative model, selects listed companies in heavily polluting enterprises as research samples, and uses a two-way fixed effect model to empirically study the impact of financialization of listed companies in heavily polluting industries on technological innovation.(1) Quantification of enterprise technological innovation (ETI)Xie Jiazhi , Zhao Li(2) Quantification of enterprise financialization (EF)For heavily polluting entities, the proportion of financial assets in all assets can better reflect the behavior of heavily polluting enterprises in financial investment outside of normal business activities. Drawing on the domestic research of Song Jun , financi(3) Moderating variableIntroduce arbitrage motivation (AM) as a moderating variable. This article uses investment income/net profit to quantify enterprise market arbitrage motivation.(4) Control variableCash flow (CF). This article uses the percentage of net cash flow from operating activities in total assets to quantify enterprise cash flow.Debt-to-asset ratio (DAR). The level of technological innovation of enterprises is related to financing channels. The debt-to-asset ratio is the ratio of enterprise liabilities to assets. It reflects the external financing channels of enterprises. This article uses total liabilities/total assets to quantify.Enterprise size (size). The size of the enterprise affects the financing constraints of the enterprise, and enterprise scale is represented by Ln .Ownership concentration (OC). According to the theory of enterprise governance, the capital structure of an enterprise will have an impact on enterprise financialization or technological innovation. The degree of ownership concentration indicates the degree of dispersion or concentration of the enterprise’s equity, and the size of the shareholder’s right to speak in the enterprise. This indicator is also used to measure the internal stability of the enterprise. This article uses the proportion of the top ten shareholders to measure the degree of ownership concentration.Growth rate of operating revenue (grow), Return On Total Assets (ROA), Capital Intensity (CI). The operating conditions and profitability of the enterprise will also have an impact on the technological innovation of the enterprise. The improvement in operating conditions and the increase in profitability will increase the enterprise’s disposable funds, which will have a certain impact on R&D expenditures. This article uses the ratio of fixed assets to total assets to measure capital intensity.Time effect (ξ), industry effect (μ). With changes in the macro environment and policies, the level of technological innovation may vary greatly from year to year, so the time effect is introduced. If the enterprise is in the year, the value is 1; otherwise, it is 0. According to the “Guidelines for Environmental Information Disclosure of Listed Companies” (draft for comments) published by the Ministry of Environmental Protection on 14 September 2010, the heavily polluting industries are divided into 14 sub-sectors to set dummy variables. If the enterprise belongs to this industry, the value is 1, otherwise it is 0. See Cash flow will directly affect the enterprise’s ability to invest funds in technological innovation research and development. The debt-to-asset ratio can indicate the enterprise’s external financing channels. The scale of the enterprise affects the extent to which the enterprise is constrained in financing. Ownership concentration can measure the degree of stability within the enterprise, and the stability within the enterprise will also affect the enterprise’s willingness to invest in technology research and development. Growth rate of operating revenue, total asset net interest rate, and capital intensity all affect the enterprise’s disposable funds, thereby affecting the enterprise’s willingness to invest in research and development. In order to verify the hypothesis, this article adds these variables to the model as control variables. Construct the following empirical models, Equations (1) and (2), in order to verify the impact of enterprise financialization on technological innovation.According to the “Guidelines for Environmental Information Disclosure of Listed Companies” (Draft for Comment) published by the Ministry of Environmental Protection on 14 September 2010, 196 listed companies in heavily polluting industries were selected by using wind database. The time period is 2008–2019. After selecting the data, the data was processed as follows: One is to eliminate ST companies; the second is to eliminate companies with incomplete, missing or abnormal financial data; the third is to exclude companies that have no financial assets or have not disclosed R&D expenditures; and the fourth is to standardize data for other continuous variables except dummy variables. In the end, 2352 observations were obtained. This article uses stata15 as the data analysis software.As can be seen from This article uses variable tolerance (Tolerance) and variance expansion factor (VIF) to accurately judge the multicollinearity between independent variables. In univariate regression and multiple regression, the regression coefficients of enterprise financialization (EF) to enterprise technological innovation (ETI) are −0.0839 and −0.1009, respectively, and both results show a significant negative correlation at the 1% level. This result clearly shows that, for heavily polluting enterprises, enterprise financialization inhibits enterprise technological innovation, that is, to verify that Hypothesis 1 is true. This is in line with the conclusion of Hu Yiming and other scholars ,20 that From the perspective of enterprise financing sources, the coefficient of enterprise net operating cash flow (CF) to enterprise technological innovation (ETI) is 0.0514, and shows a significant positive correlation at the 5% level. From the coefficient, it can be seen that the net operating cash flow of an enterprise has played a significant role in promoting the technological innovation of the enterprise. That is, Hypothesis 3 holds. The net operating cash flow of an enterprise is an important part of the enterprise’s internal financing. When enterprise managers have sufficient internal funds, companies will consider investing funds in technological innovation for obtain longer-term profits, instead of just pursuing immediate benefits. The debt-to-asset ratio (DAR) has a coefficient of 0.0056, but its impact on enterprise technological innovation is not robust. However, it may be possible to conclude from the symbol that enterprise debt is conducive to technological innovation. It shows that when listed companies in heavily polluting industries receive external financing, they will spend more on technological innovation.From the perspective of ownership concentration, the coefficient of ownership concentration (OC) for enterprise technological innovation is −0.0345, showing a significant negative correlation at the 1% level. This is the same as the conclusion drawn by a scholar named Guo Liting . This reFrom the perspective of enterprise size, the coefficient of enterprise size (size) to enterprise technological innovation is −0.0407, and shows a significant negative correlation at the 10% level. It shows that the scale of the enterprise has restrained the investment in technological innovation. This seems to be completely contrary to the conclusions reached by scholars such as Ni Zhiliang and Zhang Kaizhi . This alFrom the perspective of enterprise operating conditions, return on total assets (ROA) has a negative coefficient for enterprise technological innovation and is significant at the level of 1%. This shows that the stronger the profitability of an enterprise, the more restrained the technological innovation ability of the enterprise. This result shows that most of the managers of heavily polluting enterprises will choose financial investments with fast returns and high returns because they care about the profits on the current income statement, rather than investing in technological innovation. The better the profitability, the more likely it is to attract managers to invest funds in financial assets, thereby obtaining more excess returns. The growth rate of operating income (grow) is not significant for the coefficient of enterprise technological innovation. This shows that the increase in business income of an enterprise is not necessarily related to the ability of the enterprise’s innovative activities, and the increase in total revenue does not mean an increase in enterprise profits. The coefficient of capital intensity (CI) for enterprise technological innovation is negative, and is significantly negatively correlated at the 10% confidence level. This result shows that the higher the capital intensity of an enterprise, the lower the level of technological innovation of the enterprise. The high capital intensity of an enterprise indicates that the enterprise has more fixed assets and relatively less current assets. When an enterprise faces limited liquidity assets, the managers of heavily polluting enterprises will choose to invest funds in financial assets that can quickly return the enterprise, thereby creating a crowding-out effect on the enterprise’s technological innovation.The motives of high-level managers of heavily polluting enterprises for investment decisions will largely affect the flow of investment funds. The moderating effects of arbitrage motivations on the financialization of heavily polluting enterprises and their technological innovation show that: Firstly, the three groups of companies with different arbitrage motivations are not parallel, indicating that the arbitrage motivations of heavily polluting enterprises have a moderating effect on enterprise financialization and enterprise technological innovation. Secondly, the group with stronger arbitrage motivations is weaker than the group with weaker arbitrage motivations in negatively regulating the effect of enterprise financialization on enterprise technological innovation. In general, the arbitrage motivation of enterprise investment decision-making has a moderating effect on the impact of enterprise financialization on enterprise technological innovation. In order to verify Hypotheses 2 and 3, the variable AM, which measures enterprise arbitrage motivation, is introduced in Model 2; and the interaction term EF*AM, which is the interaction term between financialization and market arbitrage motivation, is introduced to perform regression. State-owned enterprises and non-state-owned enterprises are quite different in terms of organizational structure, social resources, and development goals. Therefore, heavily polluting enterprises are divided into state-owned holding enterprises and non-state-holding enterprises according to their enterprise attributes. After being divided into two categories, regression analysis was performed, respectively. The regression results are presented in In order to verify Hypothesis 5, the interaction term EF*AM of financialization and market arbitrage motivation are introduced at the same time, and the regression is carried out. (2c) and (2d), respectively, present the test results of the moderating effect of the arbitrage motivations of state-owned enterprises and non-state-owned enterprises. According to the results, after adding the interaction term (EF*AM), the coefficient of financialization of non-state-owned heavily polluting enterprises for enterprise technological innovation is −0.2614, which is significant at the level of 1%. This result is consistent with the previous empirical results, indicating that the financialization of non-state-owned heavily polluting enterprises is still a significant crowding-out effect on enterprise technological innovation. The coefficient of the interaction term between arbitrage motivation and enterprise financialization (EF*AM) is 0.2337, which is significant at the level of 10%. This result shows that the arbitrage motivation has a significant moderating effect on the financialization of non-state-owned heavily polluting enterprises. However, the coefficient of enterprise financialization of state-owned heavily polluting enterprises on enterprise technological innovation is not significant at the 10% confidence level. This shows that the moderating effect of arbitrage motivation does not exist. The above results verify the establishment of Hypothesis 5, that is, the degree of arbitrage motivations that affects the financialization of heavily polluting enterprises and the extent to which they affect technological innovations shows differences in enterprise attributes.The above results show that the financialization of heavily polluting enterprises has a greater impact on non-state-owned enterprises’ technological innovation than that of state-controlled enterprises. The first reason is that compared with state-owned enterprises, non-state-owned enterprises have fewer social resources and their financing channels are relatively limited. In the case of limited enterprise funds, financial investment by non-state-owned enterprises has a more obvious effect of crowding-out R&D expenditure for technological innovation. The second is that senior managers of non-state-owned enterprises will pay more attention to individual final evaluation indicators. For their personal performance, they may choose short-term benefits with quick and high returns, thus giving up investment in technological innovation with a long payback period.In order to further verify the reliability of the empirical results of this paper, this paper will conduct the following reliability tests. In the above empirical research, this article mainly uses the fixed effects model, and then adopts the FGLS method to revalidate the empirical results. The regression results are presented in China, as a responsible developing country, is facing the pressure of environmental pollution, and heavily polluting enterprises need to achieve high-quality development in a more energy-saving and environmentally friendly way. It is crucial for China to find the path and countermeasures to achieve high-quality economic development to deal with environmental problems. Under the general trend of China’s economic financialization, heavily polluting enterprises have joined the trend of enterprise financialization in succession. We can find and solve the problem of low average level of technological innovation of heavily polluting enterprises by studying the impact of financialization of heavily polluting enterprises on technological innovation, which is conducive to further exploring the field of technological innovation to reduce environmental pollution. With the continuous improvement of the technical level of heavily polluting enterprises, the utilization of resources will be more reasonable and efficient. This will play an effective role in promoting heavily polluting enterprises to further reduce environmental pollution and reduce resource waste while maintaining economic benefits.Technological innovation is one of the core drivers of enterprise development at present. With the rapid development of the Chinese market, enterprises need to constantly update equipment, technology, and research new products to improve their core competitiveness. Technological innovation investment needs to occupy a large amount of liquidity of enterprises themselves, and most enterprises have a long technology renewal cycle and a high risk of failure, so enterprise managers are often extra cautious in making technological innovation decisions. Therefore, business managers are often extra cautious when making technological innovation decisions. The financial market in our country has shown a trend of rapid development in recent years, and new financial derivatives are constantly appearing. This allows non-financial companies that are pursuing higher and faster investment returns to participate, and the trend of financialization of entity companies has emerged. In the context of China’s advocacy of green development, it is particularly meaningful to study the impact of the financialization of heavily polluting enterprises on technological innovation. This paper takes 196 heavily polluting enterprises listed on China’s A-shares as a research sample, and explores the impact of the financialization of China’s heavily polluting enterprises on technological innovation through theoretical analysis and empirical research. The specific conclusions are as follows:The financialization of heavily polluting enterprises has inhibited the technological innovation of enterprises, and has a significant crowding-out effect on technological innovation investment. This finding is consistent with the majority of previous scholars ,29,30,31The net cash flow of an enterprise’s operating activities is an important channel for the enterprise’s endogenous financing, which has a significant positive correlation with technological innovation. It shows that the net cash flow of the business activities of the enterprise has played a significant role in promoting the technological innovation of the enterprise. Increasing operating net cash flow is beneficial for companies. When the enterprise’s own circulating funds are sufficient, senior managers are still willing to invest funds in the research and development of technological innovation for the long-term development and competitiveness of the enterprise.Arbitrage motivations have weakened the restraining effect of financialization on technological innovation. Introduce the interaction term of financialization and arbitrage motivation in the empirical model. The coefficient of enterprise financialization on enterprise technological innovation is still significantly negative, but the coefficient of the interaction term between financialization and arbitrage motivation is significantly positive. This shows that the stronger the arbitrage motivation is, the less negative effect of financialization on enterprise technological innovation will be, which weakens the crowding-out effect. The reason for this result is that based on arbitrage motivations, the income from financial investment can flow into technological innovation research and development activities, so as to alleviate the pressure caused by the shortage of research and development funds.Compared with state-owned enterprises, the financialization of non-state-owned enterprises has a greater degree of crowding-out technological innovation; arbitrage motivations have a more significant moderating effect on enterprise financialization that affects enterprise technological innovation. Compared with non-state-owned enterprises, state-controlled enterprises are more deeply affected by national policies. The state currently strongly supports scientific and technological innovation and offers many preferential treatments including taxation in terms of technological research and development. Therefore, from the perspective of capital constraints, under the circumstance of limited enterprise funds, financial investment by non-state-owned holding companies has a more obvious crowding-out effect on technological innovation R&D expenditure. From the perspective of business managers, senior managers of non-state-owned enterprises will pay more attention to personal final evaluation indicators. For the sake of personal performance, they will choose financial investments with fast returns and high yields, thus giving up investment in technological innovation with a long payback period.


By leveraging the development of mobile communication technologies and due to the increased capabilities of mobile devices, mobile multimedia services have gained prominence for supporting high-quality video streaming services. In vehicular ad-hoc networks (VANETs), high-quality video streaming services are focused on providing safety and infotainment applications to vehicles on the roads. Video streaming data require elastic and continuous video packet distributions to vehicles to present interactive real-time views of meaningful scenarios on the road. However, the high mobility of vehicles is one of the fundamental and important challenging issues for video streaming services in VANETs. Nevertheless, previous studies neither dealt with suitable data caching for supporting the mobility of vehicles nor provided appropriate seamless packet forwarding for ensuring the quality of service (QoS) and quality of experience (QoE) of real-time video streaming services. To address this problem, this paper proposes a video packet distribution scheme named Clone, which integrates vehicle-to-vehicle and vehicle-to-infrastructure communications to disseminate video packets for video streaming services in VANETs. First, an indicator called current network quality information (CNQI) is defined to measure the feature of data forwarding of each node to its neighbor nodes in terms of data delivery ratio and delay. Based on the CNQI value of each node and the trajectory of the destination vehicle, access points called clones are selected to cache video data packets from data sources. Subsequently, packet distribution optimization is conducted to determine the number of video packets to cache in each clone. Finally, data delivery synchronization is established to support seamless streaming data delivery from a clone to the destination vehicle. The experimental results show that the proposed scheme achieves high-quality video streaming services in terms of QoS and QoE compared with existing schemes. With the rapid development of ad-hoc wireless communications and vehicular technologies, vehicular ad hoc networks (VANETs) have enabled the delivery of data between vehicles on roads . In VANEAmong the applications of VANETs, video streaming services are currently receiving much attention for supporting applications such as video warning for safe driving and video chats for entertainment. A vehicle can send a video warning file about an accident on its forward path to the vehicles behind it. It can also share a video clip file advertising nearby stores with its neighboring vehicles through a video chat application. Generally, video files contain large amounts of data depending on their related applications and have stringent quality of service (QoS) and quality of experience (QoE) limitations. VANETs have dynamic topology features owing to the intermittent connections and frequent link breakages due to the high mobility of vehicles on roads. Accordingly, three challenging issues should be addressed to achieve the QoS and QoE requirements to support video streaming services in VANETs with dynamic topology features and intermittent connections. First, large video streaming data should be divided into many data chunks, which should be sequentially delivered along the travel route of the destination vehicle. Second, the video data chunks should be forwarded via paths with good link quality to ensure their QoS and QoE requirements. Third, data caching should be efficiently conducted to store the video data chunks on the travel route until the destination vehicle arrives.Until now, numerous studies have proposed protocols to deliver data to a vehicle in VANETs . They arFor supporting multimedia streaming services in VANETs, many protocols ,26,27 haTherefore, we propose a video packet distribution scheme named Clone, which supports video streaming services with QoS and QoE from a source node to a destination vehicle in VANETs. To achieve this, the proposed scheme sequentially and seamlessly distributes video streaming packets to APs called clones on the trajectory of the destination vehicle by integrating high quality V2V and V2I communications in order to deliver video streaming data from the source to the destination vehicle. To realize the proposed scheme, we first define an indicator called current network quality information (CNQI). It measures the feature of data forwarding of each node to its neighboring nodes in terms of both reliability and throughput, which impact the data delivery ratio and the delay related to the QoS factors for delivering video streaming data, respectively. The proposed scheme chooses vehicles with high CNQI values as the relay nodes for delivering the video streaming packets. Next, we decide clones among nodes on the trajectory of the destination vehicle to cache the video streaming packets from the source for the destination vehicle. The clones are selected based on the CNQI values and the data delivery ability of nodes to improve the QoS. The number of clones is determined based on the size of the video streaming data and the data storage size of nodes. Next, we provide a packet distribution optimization to determine the maximum number of video packets to cache for the destination vehicle in each clone and to allow sequential video packet delivery to achieve better QoE. The determination is based on the resource ability parameters related to data caching and forwarding in each clone. Finally, we provide data delivery synchronization, enabling each clone to provide seamless forwarding of its video streaming packets to the destination vehicle in its communication coverage in order to achieve high QoE without any buffering effects on the video streaming. The synchronization ensures packet calibration for chronological order forwarding based on the delay calculation for packet loss reduction and the mobility estimation for the destination vehicle. To evaluate the performance of the proposed scheme, we compare it with four existing schemes by simulations: TSF, which is the basic trajectory based single packet transmission scheme; BAHG, which is the enhanced version of the TSF that considers vehicle’s trajectory to predict the intersections passing by; DQLTV, which is the video streaming service that supports QoE only; and GeoQoE, which is the video streaming services that supports QoE and QoS with geographical scheme. All schemes are implemented using the NS-3  simulatoThe remainder of this paper is organized as follows. First, we present the related work of our Clone scheme in The objective of this study is to route video streaming data to a vehicle moving toward its destination according to its trajectory in a VANET. In this section, we review the studies related to our study aim. First, we first examine the studies on data delivery to a destination in VANETs. Numerous routing protocols have been proposed to support data delivery to a destination vehicle in VANETs. Generally, they are categorized into two routing approaches in VANETs: topology- and position-based routing. Topology-based routing protocols, such as AODV , DSDV 1, and dynThe second category is the position-based routing approach. It exploits only the pure position information of nodes, instead of the topology information, to route data to destinations. Thus, it is considered as a promising routing approach in the dynamic environments of VANETs. In position-based routing, a source node encapsulates the position of the destination node in each data packet and subsequently forwards the packet to one of the one-hop neighbor nodes toward the position. The neighbor node receiving the data packet is geographically closest to the position of the destination node. This process is called greedy forwarding in geographic routing. Early position-based routing protocols only used greedy forwarding, which cannot prevent frequent occurrence of local maximum traps. A local maximum trap is a state in which a vehicle cannot find any neighboring vehicle closer to the destination than itself. GPSR and GPCR are two well-known protocols of this category, which both move out of local traps by complex perimeter routing. In ,15, it wBecause a vehicle generally moves according to its trajectory toward its destination, routing protocols ,23,24 exFor satisfying the demands of multimedia streaming services and applications in VANETs, many routing schemes ,27,37,41Thus, many routing protocols have been proposed to support the QoS and QoE of video streaming services in VANETs. For supporting the QoS requirements of multimedia services, Lakas et al.  proposedAs we have examined before, in In this section, we present the proposed scheme for supporting video streaming services in VANETs. First, we explain the network model and the overview of our Clone scheme. Subsequently, we describe in detail the four phases composing our Clone scheme. VANETs pose a demanding challenge for the fulfillment of the stringent requirements for video streaming using the mentioned communication technologies. The main challenges are a result of the high dynamic topology, extensive coverage problem, and intensely varying density of VANETs. In addition to these challenges, any video streaming solution must comply with some QoS requirements. CISCO has defined some requirements for general video streaming. The delay should not be longer than 4–5 s, and the loss should not exceed 5%. Bandwidth requirements depend on applications, and jitter imposes no significant requirements. The transmission of a video demands the use of significant network resources; however, it cannot be excessive. Therefore, video streaming solutions for VANETs have to fulfill all these requirements limited to a reasonable occupation of the wireless medium.The proposed scheme aims to minimize the communication delay between the user vehicles and the APs, having the best quality based on the CNQI metrics. Furthermore, the proposed scheme has a process to select the best nodes in the topology and uses a video packet distribution algorithm for a seamless streaming service to the user vehicles. Distributed nodes in the topology are fixed with a random position and the nodes density maintains the specific rate. Additionally, the clone node located at the end of the path to a user vehicle has a link stickiness process to forward the distributed video packets. Before the scenario, numerical integration of all communication nodes is inevitable to reduce the overall delay. Moreover, the integration can be a part of the proposed scheme. Each node maintains updated CNQI values based on a connection performance test along with the nodes of the neighbors in the one-hop range. The connection performance test focuses on enhancing the QoS values, such as bandwidth, bottleneck inflation in devices, and error rates.The proposed scheme can be divided into CNQI measurement, clone node selection, packet distribution optimization, and synchronization phases. In the CNQI measurement phase, the CNQI value is calculated, which unifies the resource state and the availability of the nodes in the network into a single numerical value by a mathematical formula. The clone node selection phase selects relay nodes as the clone nodes for the proposed scheme. In this phase, the number of nodes and the locations of the nodes are determined based on the calculated CNQI information. Using the decided number of clone nodes, the proposed scheme initiates the packet distribution optimization phase. In this phase, the packets for transmission to a destination are distributed to all clone nodes for real-time seamless high packet forwarding. In the synchronization phase, a one-hop link is connected along with the selected clone nodes and the user vehicle. It utilizes the user trajectory information for a seamless link connection between the last and following link connection times of the clone nodes. Thus, the proposed scheme conducts the phases mentioned above in sequence to provide a high-packet data content service through communication between the selected clone nodes and the user vehicle.In previous studies, many search techniques have been proposed for a single packet or small data transmission. However, the existing node information measurements are unsuitable for live video streaming services, owing to the low reflect rate of real-time node scenarios. Real-time resource availability information is crucial for live streaming support for calculating the optimal forwarding path to a service user with high-quality load balancing. The CNQI is an indicator of calculating the network efficiency of the overall nodes, which affects the user QoS and QoE of the overall topology. Several factors affect the video streaming service performance of the protocol. The geographical factor affects the error rate of a node, and it varies with moving obstacles. It is difficult to calculate and satisfy user QoS if the real-time topology is not considered. The video streaming data that a user receives have a quality status. If the transmitted video data have noise and delay when a user plays them, the user QoS will be decreased. Therefore, analyzing the overall topology by the CNQI technique is inevitable for user QoS and QoE. Using the CNQI scores of the nodes, we can determine which nodes cannot forward the multimedia data with a high MOS and SSIM score owing to obstacles and overload. The CNQI provides the information from reliable data, such as bandwidth of a node, current usage of a node, available resource by hardware score, and neighbor distribution rate in the range. Each node broadcasts a packet to estimate the quality of the video transmission in the one-hop range. The packet header includes the MOS and SSIM quality test data about the QoS and the QoE; then they compute their CNQI score and share each other. Finally, all nodes in the topology share the CNQI scores for multimedia transmission.H represents the performance of a node in case of hardware, and it ranges from 1 to 10 based on the response time and the hardware score. A single packet transmission test can calculate the hardware score in the communication range of a AP to the destination vehicle without any obstacles. B is the bandwidth of the AP, and the bandwidth usage is calculated by the number E represents the error rates due to the geographic feature and network topology of the node. We modify the factors in the CNQI equation and describe them in detail below. Equation .(1)UNetA high CNQI value shows the efficient network performance of a node when the node forwards packets in the networks. Furthermore, using the CNQI value, the proposed scheme has a traffic distribution effect on the overall usage of the nodes. In addition, the proposed scheme chooses relay nodes using the CNQI values in the forwarding phase, and because a CNQI value shifts with the allocated user number in the nodes, node accessibility can be improved. It is also possible to prevent a scenario where a sizeable geographic advantage causes numerous users to collect much traffic on one node, thereby degrading the efficiency of the node. In the clone node selection phase, all nodes keep updating the data table, which contains real-time information based on the assumption that all nodes keep updating their CNQI information. Because the table contains the information on variable factors, we can determine the status of the nodes in real time for realizing live streaming service. When the nodes have received the neighbor node searching for packets, the proposed scheme identifies the potential relay nodes because the nodes reflect the CNQI information.In the clone node selection phase, the best quality nodes located on the trajectory of the user vehicle are chosen. A clone node is a relay node that maintains synchronization with the user vehicle toward the destination. In the topology, many nodes participate in multimedia transmission. All relay nodes have a different capability and availability for communication owing to various reasons, such as hardware, network status, and topology status of the neighbor nodes. A clone node is selected for its high QoS and QoE for the user vehicle. It is an end node through which the multimedia packet passes to reach the user vehicle. Therefore, choosing the best clone node for the proposed scheme is the best approach to improve transmission efficiency. The CNQI score of a clone node must be higher than those of any nearby relay nodes because significant tasks are assigned to a clone node. A clone nodes has two primary roles, which are explained below: caching the multimedia contents for the user vehicle and maintaining synchronization with the user vehicle.Clone node selection is the process of determining the number of relay nodes that a user will use for receiving the data. The first relay node forwards simultaneously content searching messages and one-hop neighbors CNQI searching messages. After the first relay node selection, the N-th node receives the user request content messages, including the content information. Subsequently, the proposed scheme determines the number of N-th nodes that will be used based on the trajectory of the user, CNQI value, content information, and topology status. If the size of the content is sufficiently large to be completed by one relay node, this process is omitted.Seamless connection of the clone nodes with a user vehicle that has high mobility is a challenging issue. For this, the link connection of the previous clone node with the user vehicle must be connected until the next clone node connects with the user vehicle. The proposed scheme estimates overlapping geographical areas among the clone nodes located on the user vehicle trajectory. The overlapping geographical areas are estimated by the communication range of the clone nodes. If the overlapping area is insufficient to provide the transmission time, link disconnection may occur. However, if the overlapping area is extremely large, the number of clone nodes in the proposed scheme will be increased, leading to bandwidth dissipation. Thus, the overlapping area has to be optimized by the escape time of the user vehicle. Euclidean distance calculation applied for the optimization uses the entry and escape points of the overlapping area. From the optimization area, the guard band for distributed packet forwarding can be calculated. The guard band prevents QoS degradation caused by packet corruption and unstable transmission service owing to vehicle mobility.The minimum guard band Equation , the appConsidering the QoS and QoE effectiveness of the proposed scheme, a clone node should be located on the trajectory of the user vehicle with a wide-open area that leads to minimum obstacles for communication.Based on the guard band equations and the CNQI for improving the QoS and QoE, all nodes should have the possibility of being relay and clone nodes, and a clone node must have extensive content storage for link connection and caching the algorithm.We designed a packet distribution algorithm and a synchronization algorithm for link connection based on the mobility of the vehicle and the content playtime. For the algorithms, a clone node must have an AP to receive the computed data.At the end of the relay nodes, the last node is called a clone node, which replicates the network information of user vehicle for link connection. The clone node has a replicated identity (ID) from the user vehicle. The content data of the source identify the clone node as a user vehicle for convenience data forwarding in turn. A clone node must meet certain requirements, which are summarized as follows:In large content data transmission cases, one replicated clone node with a few relay nodes cannot forward all content data to a user vehicle owing to the size of the content compared to the storage in the nodes and the mobility of the user vehicle. Consequently, the user vehicle does not have sufficient time to receive the content data. We proposed a clone node selection algorithm to solve this problem by reasonably selecting the relay nodes as clone nodes based on the CNQI. Furthermore, the content must be distributed over the relay and clone nodes owing to extensiveness of the content data in video streaming transmission. Therefore, we suggested packet distribution optimization of the received data. The CNQI information from the user vehicle, such as trajectory, speed, and request content size, is used to select the clone nodes. In addition, the information of the relay node is crucial for choosing the clone nodes. The information obtained from the localization devices added to the user vehicle is variable, owing to the geographic distribution of the nodes.After the clone nodes are selected based on the mobility of the user vehicle, node distribution, bandwidth usage of the nodes, and other information, the source node, which contains the contents, distributes the content data and subsequently forwards them to each clone node. All clone nodes receive the distributed packets respectively in order by time, which depends on the synchronization time of the user vehicle with each clone node and the time computed when the source node requests neighbor nodes on the user vehicle trajectory. To process the packet distribution optimization, we denote Using the factors we mentioned above, we can calculate the packet amount for the transmission to the user vehicle from each clone node. The content data are distributed and forwarded to each node based on the equation. The amount, Equation .(4)ICloBased on Equation , the amoThe distributed content packets are forwarded to the selected clone node through the calculated path, which is composed of the selected relay nodes based on the CNQI. Each selected relay node has a time table for forwarding the distributed content packet, in order for the the packets to get distributed. Even if the user vehicle has high mobility, the selected relay nodes and the clone nodes can form a link connection based on the time table of the nodes and the trajectory of the vehicle. The method to obtain a seamless link connection between the user vehicle and the clone nodes is explained in the next After the content packets are forwarded to each clone node, the user vehicle moving toward its destination based on its trajectory receives the content packets from the clone nodes when it passes through the connection area of the clone nodes. In urban road-map distinct features, vehicles naturally experience traffic interruption due to signal lights, traffic, and other factors.The message exchange task is the first step of the synchronization phase. In the topology, there are various vehicles on the road within the communication range of a clone node. We have to identify the right vehicle to transmit the streaming data to the user. To satisfy the conditions, when a random vehicle enters the clone node communication range, the clone node exchanges an ID qualifying message with the vehicle to determine if the vehicle is the target. The basis is the data packet header with the ID of the data-requested vehicle.After the massage exchange, we calculate the data forwarding delay in the user vehicle to support the mobility of the vehicle based on the query and reply packets, which is one signal time. The existing protocols do not support the mobility of a vehicle in live streaming services ,30. FinaPacket calibration is conducted for a seamless link connection with the user vehicle after the next step of the data forwarding delay calculation. Packet calibration considers two scenarios: when the user vehicle is slower and faster than the states based on the estimated time owing to the urban map features, respectively. The allocated content data, Equation  expresseWe consider two conditions for seamless transmission with high QoS and QoE. Condition In this section, we present the evaluation of the performance of the proposed scheme called Clone by comparing with the existing protocols. For this, we implemented both the existing protocols and Clone in the NS-3 simulator. In the next subsections, we first explain the simple basic concepts of the existing protocols for implementing them in the simulator, and subsequently describe the simulation model and scenario. Finally, we compare the performance of Clone with those of the existing protocols in diverse aspects based on the simulation results.For evaluating the performance of Clone, we use four existing protocols as comparison protocols. The first one is BAHG , which pThe Clone scheme is compared to four other schemes: a basic non-streaming service for the TSF scheme, mobility prediction scheme for the BAHG scheme, streaming service for the DQLTV scheme, and QoE-based streaming service scheme. The comparisons are conducted to show the advantages of our clone-based proposed scheme in high-packet live video streaming service with mobility support as well as the performance improvements over the other categorized schemes. Video streaming through VANETs is challenging, primarily owing to the amount of data forwarding. A high-definition quality video streaming service under the Internet scheme has a required maximum flow of 6 Mbps. Thus, with the WLAN network using the 802.11p standard, the binary throughput is approximately 54 Mbps for providing QoS and QoE over VANETs. However, using an ideal environment for obtaining the best QoS and QoE performance in real VANETs is unrealistic. Consequently, we focus on real map data for location setting of the RSUs and relay nodes for the simulation.Regarding basic non-streaming service, TSF is the representative scheme using vehicle trajectory for comparison to the proposed scheme. In TSF, a localization system is installed in vehicles to provide the movement path of a vehicle moving toward the destination. Using the mobility information from the localization system in the TSF scheme, TSF calculates the expected delay of a road segment for video packet forwarding. For basic streaming service, DQLTV is the representative protocol for comparison to the proposed scheme. DQLTV utilizes multi-hop level forwarding and delay, quality of the link, and a link lifetime aware routing protocol suitable for video streaming service. DQLTV adopts the role of maximizing the link quality and the link lifetime as well as minimizing the distance between the hops and the overall delay for reducing the frame loss. However, there are unconsidered factors that affect the overall performance of the video transmission, such as QoS and QoE. The Geo-QoE scheme is an improved scheme using QoS and QoE factors to enhance the QoE of a user vehicle. For the mobility support scheme, BAHG is the representative protocol for comparison of the mobility support part for video streaming services. BAHG derives the trajectory of a vehicle for minimizing the number of intermediate intersection nodes while considering connectivity. Moreover, backbone nodes track the movement of a source and a destination vehicle for providing the connectivity status around an intersection. We consider three of the challenges in a video streaming service in the experiments. Our proposed scheme is compared against multiple QoS and QoE factors, respectively.In VANETs with video streaming services, wireless communication systems encounter challenges due to the characteristic of the wireless environment. First, a wireless communication cannot provide the same satisfaction level to all users in terms of the QoS and the QoE, and it cannot cover extensive content in a vehicle, such as infotainment, which is composed of massive data content. Moreover, the massive data content is associated with a risk of packet duplication, forwarding error, and other problems that decline the user satisfaction. Second, mobility changes of the vehicles in VANETs are inevitable, and a wireless communication channel can change rapidly owing to the variation in the distance between the source and the user vehicle, which have high mobility. Interference is caused by mobility changes, and urban map characteristics are distinct consequences of the mobility of a vehicle. Third, the video streaming service can be conducted on a support, such as 802.11p. Furthermore, delivering each chunk to the relay and clone nodes in real time is susceptible if the protocol is derived from other standards.However, the 802.11p norm is the third standard using waveband 5.835 GHz to 5.923 GHz frequency band with orthogonal frequency division multiplexing (OFDM) signaling. The communication protocol used in the existing ITS environment is DSRC, mainly used for short-distance communication. However, with the ever-changing Internet of Things environment and the development of smart cars, it was necessary to develop a communication technology that can recognize traffic conditions on the road in real-time to prevent traffic accidents in advance and smoothly adjust the traffic flow. Wireless Access in Vehicular Environment (WAVE) is a standard developed to meet these requirements. WAVE is a wireless communication technology that delivers dangerous situations on roads or vehicles to vehicles in a vehicle network with high-speed mobility (up to 200 km/h) and short communication exchange time. V2X  communication is possible in real-time, even in high-speed driving conditions. It is a source technology that prevents accidents by processing real-time information such as road conditions and vehicle accidents.The video content structure used for the experiments, H264 AVC flux, is composed of image frames. H264 AVC is a global video structure also known as MPGE-4 part 10, and it is a successor of earlier standards MPEG-2 and MPEG-4. The simulation model used H264 AVC for video streaming scenarios to forward the content packets. H264 AVC has variable bit rates depending on the color of the composed image in the video. It is suitable for comparing with various color bind videos based on QoS and QoE factors.QoS is a video content quality measurement based on user experiences. QoS parameters can be categorized as opportunities to provide high-performance video streaming services. To maximize the QoS that a user experiences, several parameters—bandwidth media, control of jitter and controlled period, and decreased packet loss—must be considered and tuned. In our simulations, end-to-end delay and packet delivery ratios based on the jitter as well as the packet loss that results when forwarding the video content data were considered as QoS parameters. As already mentioned about the QoE, if video streaming does not consider the QoS in packet forwarding, the protocol will encounter crucial problems in the transfer and routing process of voice and video quality.The perceived video quality is measured by the two most commonly used QoE parameters: MOS and peak signal-to-noise ratio (PSNR). The PSNR is the standard metric for measuring the objective video quality. This parameter is expressed as a function of the mean standard error between the original and received video frames. If a video frame experiences either transmission or overdue loss, it is considered to be dropped and may be concealed by copying the payload from the last received frame before it. Metric SSIM used as an image/video metric to measure the received frame quality based on its structural, luminance, and contrast similarity. The SSIM, measured using MSU tools, improves the MOS and PSNR metrics by revealing the perceived quality of the received video sequences. The SSIM differs from the PSNR because it approximates structural distortion, instead of the pixel-by-pixel errors, to evaluate useful information to human eyes.In our experiments, the 802.11p standard for ad-hoc network QoS is derived. Three classes of video files with variable bit rates of 30 fps are considered. Each video file is composed of night view scenes (which combine multiple colors), ocean scenes (combining blue colors), and sunset scenes (combining red colors). The resolutions of the three videos are 720P high definition (HD), 1280 × 720 progressive scan, and 923,600 pixels (0.92 MP) per frame. The routing protocol for the packet transmission to the relay and clone nodes uses GPCR and AODV. We consider two binary flows: 11 Mbps as the minimum speed and 54 Mbps as the maximum speed. The detail of the parameters are in We use a network simulator (NS-3) to simulate the video streaming service with high packet data on the Seattle city map. The vehicles requesting messages and receiving the video content are placed on the Seattle city map of the network. A set of 50 requesting vehicles is placed in an area of 3000 m × 3000 m, and the 300 nodes that take the roles of relay and clone nodes located in the network area follow the features of the urban map. The mobility model of the vehicles on the map follow random mobility with the Manhattan mobility model. The CNQI information is already computed and shared with the neighbor nodes when a scenario is initiated, such as a real-map scenario, for the proposed scheme experiments. Therefore, we ignore the delay of the topology setting time for the overall delay estimation time.However, Using the same experimental conditions in In this article, we have proposed live video streaming services that support the mobility of the vehicle by the Clone scheme. We have formulated CNQI to figure out the topology and status of the nodes in video transmission to select the best relay nodes, a packet distribution optimization method with guard band for mobility support, and a link connection phase for a seamless video streaming service that reacts to various unforeseeable circumstances. Based on the CNQI algorithm, we developed high QoS and QoE video streaming services with high mobility situations in real-time.The proposed protocol, called Clone, derives a conclusion from the various processes in this paper to solve the three challenging issues in the video streaming service of VANETs. In large video streaming circumstances with high mobility, such as in moving vehicles, the video data should be divided into many chunks. The divided chunks have to be forwarded to one of the nodes on the requested vehicle trajectory. The Clone protocol has a process to distribute the requested video content data forward to the clone APs. The data distribution algorithm from the proposed protocol gives enormous advantages in load balance in the topology and the seamless data transmission. The proposed protocol considers the overall topology information to deliver the distributed video content data to Clone APs. It prevents intermittent data loss while the relay node is delivering the requested video data. This leads to a stable transmission of the proposed protocol.Moreover, the proposed protocol calculates the topology QoS and QoE information by CNQI measurement. The load balancing of the topology in the video streaming service shows better performance from the proposed protocol than other topology measurement protocols in the simulation results. Various factors that do not get the eye in the compared protocols, such as the energy of the APs, the number of subscribers, the bandwidth of the AP, and others from the CNQI measurement factors, accepted the proposed protocol to analyze the topology information. To improve the QoS and QoE in a video streaming service with a high mobility situation, the CNQI measurement in the proposed protocol gives better results than other compared protocols in QoS and QoE. As the proposed protocol distributes the contents data to forward the video packets, the data caching process should be stored until the requested vehicle receives the data. The compared protocols do not have an effective caching mechanism for QoS and QoE. Hence, the simulation results show that the proposed protocol is adaptable in various topology changes such as density, mobility, and energy of APs.The experiments have been tested under various conditions such as communication range changes, video file sizes, hop counts, speed of the vehicles, and AP density in the topology. Thus, we draw simulation results about the end-to-end delay, PDR, PSNR, MOS, and SSIM. The results show that the Clone scheme gives 10% to 20% better results than other video streaming services in overall performance with QoS and QoE and 60% to 80% better results than other comparison packet delivery routings. The simulation results have verified better QoS and QoE in the massive mobility changes of urban map features. Besides, the results have demonstrated that our proposed scheme could perform much better than other schemes in QoS and QoE with load balancing.


Network interaction has evolved into a grouping paradigm as civilization has progressed and artificial intelligence technology has advanced. This network group model has quickly extended communication space, improved communication content, and tailored to the demands of netizens. The fast growth of the network community on campus can assist students in meeting a variety of communication needs and serve as a vital platform for their studies and daily lives. It is investigated how to extract opinion material from comment text. A strategy for extracting opinion attitude words and network opinion characteristic words from a single comment text is offered at a finer level. The development of a semiautonomous domain emotion dictionary generating technique improves the accuracy of opinion and attitude word extraction. This paper proposes a window-constrained Latent Dirichlet Allocation (LDA) topic model that improves the accuracy of extracting network opinion feature words and ensures that network opinion feature words and opinion attitude words are synchronized by using the location information of opinion attitude words. The two-stage opinion leader mining approach and the linear threshold model based on user roles are the subjects of model simulation tests in this study. It is demonstrated that the two-stage opinion leader mining method suggested in this study can greatly reduce the running time while properly finding opinion leaders with stronger leadership by comparing the results with existing models. It also shows that the linear threshold model based on user roles proposed in this paper can effectively limit the total number of active users who are activated multiple times during the information diffusion process by distinguishing the effects of different user roles on the information diffusion process. With the continuous advancement of Internet technology, people's communication methods on the Internet have also undergone significant changes, from searching for information, reading news, and watching videos to gathering and discussing in groups such as Weibo, QQ, and WeChat . This tyIn order to gain a better understanding of social network public opinion, it is necessary to grasp the development status and characteristics of social network public opinion, systematically summarize the value form of social network public opinion, and analyze the reasons for the formation of social network public opinion . The useThe processing process is investigated for text preprocessing, and NLPIR-based comment text segmentation and part-of-speech tagging algorithms are primarily presented. In terms of sentiment dictionary-based opinion and attitude word extraction, the extraction of opinion and attitude words is accomplished by establishing a domain sentiment dictionary. A window-constrained LDA network opinion feature word extraction technique is suggested for extracting feature words of network opinions. The algorithm's efficacy and accuracy are confirmed by several experimental comparisons. The TSRank technique introduced in this work is enhanced by adding clustering to find candidate opinion leaders using the UserRank algorithm, considerably reducing the program's execution time. While the TRank system performs somewhat worse when identifying people with more theoretical user influence capabilities, it performs much better when identifying persons with greater actual user influence capabilities. Simultaneously, when the total number of opinion leaders to be found is modest, the TRank method performs better. As a result, the TSRank method suggested in this work is both valid and accurate. In comparison to the LT, the K-LT model introduced in this article, the linear threshold model URI-LT based on user roles may provide a more accurate interpretation of genuine social networks when modelling the process of information diffusion. As a result, the linear threshold model described in this research is both rational and effective.There are few foreign literatures on college students' online community and the expression of college students' online opinions . They maConsidering the dissemination of two distinct types of information with distinct release times and assuming that the content of each message may be unrelated to the content of the other messages, related scholars proposed an information diffusion model in which dual information is simultaneously and dynamically propagated in a complex network, as well as a model of dual information infection propagation . The resRelevant researchers have presented a microscopic risk diffusion model that forecasts the dynamic spread of network risks and threats from a microprobability viewpoint and gathers the most likely infected border nodes at the moment to withstand network risks and threats . AssuminRelated academics use the knowledge map approach to separate the subject attributes of the map's entities, extract the topic and time attributes of the map's entities from the Neo4j graph database, and trace the development of public opinion themes using multidimensional feature fusion analysis . RelevanCorpus labelingBecause network public opinion event propagation and diffusion analysis entails opinion leader mining, knowledge graph visualization, and communication and diffusion analysis, corpus labeling, character encoding, and time slicing are necessary for the network public opinion event corpus. (2) Character encodingOpinion leaders play a pivotal role in the spread of online public opinion. Therefore, for the analysis of the spread and spread of online public opinion events, it is necessary to dig out opinion leaders. In order to verify the effectiveness of opinion leader mining, it is necessary to label the opinion leaders in the real data set. Combining the corpus of online public opinion events and the development law of online public opinion, this article will mark Sina Weibo users who meet the following four aspects as opinion leaders: (1) users who are parties to the event; (2) Sina Weibo is marked as “Big V”; (3) users who forwarded more than 300 events; and (4) official Weibo users such as government, schools, and news media. (3) Time sliceThis article uses the Neo4j graph database as the platform for storing knowledge graphs. The Neo4j database only supports files in UTF-8 encoding format, so the encoding format of the network public opinion event corpus needs to be uniformly adjusted to UTF-8 encoding. The division of the life cycle of online public opinion is very important for the analysis of the spread and spread of online public opinion. According to the online public opinion life cycle theory, the online public opinion event corpus data is divided by time.The network public opinion event knowledge graph belongs to the domain knowledge graph, and the construction of the domain knowledge graph usually adopts top-down construction technology. Firstly, the network public opinion event is modelled on the ontology, and the concepts, entities, attributes, and relationships of the network public opinion event corpus are sorted out. In order to ensure the reliability of the graph, the ontology layer is manually verified; then, knowledge acquisition, entity linking, and elimination are completed in sequence. The quality of network public opinion event knowledge is evaluated. The purpose of knowledge representation and reasoning is to transform the triples of the knowledge graph into computable discrete vectors through the representation of knowledge in the knowledge graph, and then, the completion of the knowledge graph and relational reasoning can be performed through calculations between vectors. This paper proposes an attenuation attention mechanism and embeds it in the graph attention network to form a graph attenuation attention network. Knowledge representation and reasoning are carried out by using a graph attenuation attention network.Opinion content extraction differs from traditional text content extraction in that it requires the extraction of network opinion feature words, opinion attitude words, and the collocation connection between the two from the comment text, and the quantity of data collected is rather significant. There are several studies now being conducted on the extraction of views and attitudes, most of which are accomplished with the use of emotion dictionaries, and public dictionaries are accessible. This paper proposes to extract the network opinion feature words and their positions using the emotion dictionary method first and then extract the network opinion attitude words using the LDA topic model based on the position of the opinion words, in order to achieve simultaneous extraction of the network opinion feature words and the opinion attitude words to ensure Internet opinion character. The working flow chart of the opinion content extraction algorithm described in this article is shown in It can be seen from tk, as shown in the following formula:Information gain (IG) represents the difference between the quantity of information available when a certain feature item is present and the amount of information available when it is absent. Accordingly, information gain may be viewed as the criticality of the feature item and can be utilized as a criterion for whether or not a feature is kept. The entropy determines how much information is available for categorization. The information gain is equal to the difference between the original entropy of the system and the conditional entropy of the feature item M categories of documents is defined as follows, where P(Cj) represents the probability of documents of category Cj appearing in the training set. The entropy of P(tk) represents the probability that documents with the feature tk appearing in the training sample. The conditional entropy of the feature item is defined as follows, where The final expression of information gain isThe language style of comments written by users varies from person to person and does not completely conform to the grammatical rules. In order to ensure the accuracy of the comment content extraction model, preprocessing is required.Different applications have different processing requirements, and the preprocessing process will be adjusted accordingly. For example, this article preprocesses comment texts, mainly to filter invalid comments and filter out words that do not contribute to the expression of opinions.Word segmentation and part-of-speech tagging are to find out the Chinese words and their parts of speech  in the text. Word segmentation and part-of-speech tagging technologies are quite mature, like NLPIR, which is a widely used word segmentation system. This article is based on the NLPIR word segmentation system and adds it to the NLPIR user dictionary by constructing a dedicated vocabulary word segmentation dictionary in the e-commerce field. Since the priority of the user dictionary is higher than that of the word segmentation system, it can ensure that the proprietary vocabulary is correctly recognized. For example, the high-end and high-grade emotional word “Gao Dashang” that describes things should be recognized as one word instead of multiple words.The process of creating the emotional vocabulary is separated into two phases: the first phase involves the construction of the basic dictionary, and the second phase involves the expansion of the basic lexicon. The extension of the sentiment dictionary is classified as domain and synthetic sentiment word expansion. The emotion dictionary's building process is shown in The template of the basic dictionary in this article comes from the emotional vocabulary ontology database. By extracting the positive and negative words, the rare and uncommon words are removed. The expansion of the domain words is realized by the operation based on semantic similarity. When preprocessing the review corpus, if a new word that is not included in the basic dictionary appears, the semantic similarity between it and the reference word is calculated, the qualified words are put into the candidate dictionary, and the candidate new words are finally evaluated. The semantic similarity is obtained through the point mutual information operation, which can be used to measure the similarity of two words. The calculation formula is as follows:P represents the probability that the words x and y appear together in a certain type of document, P(x) represents the probability that the word x exists alone in a certain type of document, and P(y) represents the word y in a certain type of document probability of unique existence in the document.Among them, Unsupervised learning is achieved using the Latent Dirichlet Allocation (LDA) model. It is mostly employed in the area of text analysis for calculating text data set similarity, prospective topic mining, and document summary production. It is effectively a three-layer Bayesian network, with one-layer networks for documents, subjects, and words. A bag-of-words model is used to turn text input into computer-calculable word frequency information.In the LDA model, the known data is the words in the text, each subject is a random combination of words in the vocabulary, and each text is a random variable composed of various subjects, and all of them satisfy multiple items. dj, the probability of the word wj isUnder the condition of the document LDA is an unsupervised learning model. You only need to set and optimize the model parameters. There is no need to manually mark the text of the training set, which saves manpower and time and is suitable for large-scale corpus processing. Compared with other topic methods, LDA has stronger generalization ability, is not prone to overfitting problems, and performs well in text information dimensionality reduction and topic information clustering. These advantages make the LDA model very suitable for network opinion feature word extraction. Therefore, this paper uses LDA to extract network opinion feature words.K cores, and the two-degree neighbour aggregation coefficient for each node in the graph. After performing maximum and minimum normalisation on the three attribute values, respectively, the zero value of each attribute is substituted with a nonzero minimum value to enable computation of the fourth attribute. We begin the clustering procedure by selecting the optimal cluster number K using the elbow approach. Set 2k20 which is shown in k = 10.To begin, this article develops a social network graph model in order to determine the number of two-degree neighbours, the number of As seen in N increases, the performance of the LDA topic model is still optimal. This is because the LDA theme model calculates user influence and activity based on user previous behavior data, while the UI-LR algorithm generates initial user influence based on user past behavior data. Therefore, the Top-N opinion leaders identified by the LDA topic model are closer to the opinion leaders in real social networks. The overall stability of the TSRank algorithm is better.It can be seen from C-value counts how many different users can be affected by the microblogs published or reposted by Top-N opinion leaders of each algorithm in the historical behavior of users. It can be seen from C-value, the performance of the LDA topic model is better than that of the other four algorithms, indicating that the LDA topic model is more accurate in identifying users with high influence in the history.θ = 0.5 for each user. Considering that when the attenuation factor б is too large, the attenuation effect on the node's influence weight is too large, the influence cannot be effectively accumulated. Therefore, we set the value of б to . With 0.05 as the increment, we study the influence of different б values on the diffusion effect.On the basis of the LT algorithm, first considering that the user influence weight is affected by the time decay factor, the T-LT model is proposed. We set the initial influence weight of the T-LT algorithm. In order to make the diffusion effect only affected by the attenuation factor and the seed node, this paper uniformly sets the threshold As seen in θ = 0.5 for each user. According to the impact analysis results of different б values, the attenuation factor б = 0.3 is selected to make the impact of the attenuation factor on information diffusion more obvious. Based on the analysis of the influence of different proportions of opinion leaders, the total number of opinion leaders N = 60 is selected. After separately analyzing the influence of time attenuation factor and user influence weight on information diffusion, this paper proposes the URI-LT algorithm based on LT, considering the time attenuation factor and user influence weight at the same time. We analyze the diffusion effect of the same seed node set on different information diffusion models. Here, we set the threshold According to the records in This paper proposes a method of synchronously extracting the opinion attitude words of a single comment text and the network opinion characteristic words at a fine-grained level. The accuracy of opinion and attitude word extraction is increased by developing a semiautonomous domain emotion dictionary generation approach. A window-constrained LDA topic model is developed, which employs the position information of opinion attitude words to increase the accuracy of network opinion feature word extraction and guarantees that network opinion feature words and opinion attitude words are synchronized. Finally, the substance of comments is extracted from the comment text. For the same seed node, the total number of users that the URI-LT model can affect is smaller than the total number of users that the LT model can affect but substantially more than the total number of users that the K-LT model can affect. It demonstrates that the URI-LT model is very accurate when user roles and temporal attenuation effects are taken into account. Additionally, by comparing the results of R-LT, T-LT, and URI-LT information diffusion, this paper discovers that when the weight of the user's influence is distributed according to the user's role concurrently, the weight of the user's influence is dynamically accumulated according to the time decay factor. This is because, when determining the relative significance of users across user roles, this article restricts the relative relevance of users to the range , 2. Howe