Development Of Internet Research Articles

Artistic Practices in the Context of the Evolution from Web 1.0 to Web 3.0

This article examines the evolution of artists’ interaction with the audience in the context of media culture, linked to the updates of communication mechanisms on the Internet, from the static Web 1.0 to the interactive Web 3.0. The aim of the article is to show the connections between the changes in authorship models and mechanisms of interaction with the audience in artistic practice and the evolutionary development of web technologies. The transformation of contemporary artistic forms is the result of the active influence of digital technologies and the associated collapse of the existing hierarchy between author, performer and audience. The study discusses the strengthening of the interactive component in artistic practices, parallel to the development of Internet technologies from Web 1.0 to the semantic Web 3.0 and analyzes the overlapping of these processes and their mutual influence in the context of post humanist perspectives. It was found that artistic collaborations through collective authorship are a characteristic model of authorship for this phase of Internet development. It was noted that the evolution from Web 2.0 to Web 3.0 opens new possibilities for the creation of multisensory experiences, thanks to the use of VR (Virtual Reality) and AR (Augmented Reality) technologies, which are actively used by artists in artistic practices and works, for example in operas or concerts. In addition, the active development and integration of artificial intelligence technologies in artistic practices has been identified. By analyzing musical projects of different genres and forms, the transformative influence of web culture on contemporary artistic practices becomes clear. Keywords: artistic practices, music, internet, technologies, interactivity, communicative models’ development

Internet development, information availability, and Chinese enterprises’ cooperative green technology innovation

Cooperative green technology innovation is potentially the answer to the lack of motivation for independent green innovation, which can effectively alleviate many difficulties faced by enterprises engaging in independent green innovation. Internet development provides new opportunities to stimulate innovative cooperation of enterprises. However, little literature has studied the impact of Internet development on enterprises' cooperative green technology innovation. Based on the data of Chinese A-share 3284 listed companies from 2010 to 2019, this paper uses a panel two-way fixed effects model to assess the effect of Internet development on enterprises' cooperative green innovation. The findings are: (1) Internet development significantly drives firms' collaborative green innovation behavior. The result remains robust even after performing a series of robust tests. (2) The Internet empowers green innovation cooperation among firms by improving information availability, including market information availability and technical information availability. (3) The heterogeneous results show enterprises use the Internet to accomplish high-quality collaborative green innovation. Internet development is more helpful in encouraging the cooperative green innovation of enterprises located in central cities and state-owned enterprises. This study provides novel and targeted policy implications to empower enterprises’ green innovation cooperation and drive sustainable economic development through Internet development.

Design and building of a room light control System based on iot arduino cloud

The development of Internet of Things (IoT) technology opens up various opportunities to improve efficiency and convenience in various aspects of life, including in the management of electrical devices in the room. One of the innovative solutions that utilizes IoT technology is the Arduino Cloud-based room light control system using the NodeMCU ESP8266 microcontroller. This system is specifically designed to improve the efficiency of using room lights in the PLC laboratory of the Electrical Engineering department. This research aims to design and develop an Arduino Cloud-based IoT room light control system that can be controlled remotely using an Android device, PC, or laptop. NodeMCU ESP8266, as the core of this system, is a microcontroller that can connect with IoT, allowing users to control room lights via WiFi network. With this system, it is expected that users can control the PLC laboratory room lights in the Electrical Engineering department more easily, practically, and efficiently. In addition, this system is also expected to help save energy usage in the room. This research uses the method of designing and making a room light control system based on IoT Arduino Cloud. The research stages include system design, hardware and software development, and system testing. The test results show that the developed room light control system is able to turn on and off four lights in the PLC laboratory room via WiFi network using an Android device. In addition, this system is also equipped with an On/Off switch that can be used in the event of internet communication disruption. Thus, the IoT Arduino Cloud-based room light control system using the NodeMCU ESP8266 microcontroller is proven to provide convenience and efficiency for users in managing lighting in the PLC laboratory of the Electrical Engineering department, while helping to save energy.

System Design, Automatic Data Collection Framework and Embedded Software Development of Internet of Things (IoT) for Air Pollution Monitoring of Nagpur Metropolis

System Design, Automatic Data Collection Framework and Embedded Software Development of Internet of Things (IoT) for Air Pollution Monitoring of Nagpur Metropolis

Can internet development improve carbon emission efficiency for manufacturing? The role of market integration

Improving carbon emission efficiency of manufacturing is important for promoting low-carbon development and achieving harmonious coexistence between humans and nature. In this study, we use the Global-MinDS model to measure the carbon emission efficiency of China's provincial manufacturing from 2011 to 2020. To do so, we construct a mathematical model to analyze the mechanism of the interaction among internet development, market integration, and carbon emission efficiency of manufacturing. Then, we empirically examine the carbon emission reduction effect of internet development using the Bartik instrument variable method and verify the significance of market integration. Our results show that internet development can significantly improve the carbon emission efficiency of manufacturing, promoting manufacturing low-carbon transformation. Carbon emission reduction effect of internet development is characterized by heterogeneity in market integration, environmental regulation, and policy intensity. This means that the impact of internet development is more pronounced in lower integration regions, higher environmental regulation intensity and in big data comprehensive experimental zone. Intermediate mechanism tests show that internet development can promote market integration by reducing external transaction costs, so as to play a carbon emission reduction effect in the market mechanism. Our study provides empirical evidence for the carbon emission reduction effect of the digital economy and offers valuable references for effectively promoting the low-carbon transformation of manufacturing in China from the perspective of unified market.

Collaborative Planning of Distribution Network, Data Centres and Renewable Energy in the Power Distribution IoT via Interval Optimization

With the development of the power distribution Internet of Things (IoT), the escalating power demand of data centers (DCs) poses a formidable challenge to the operation of distribution networks (DNs). To address this, the present study considers the operational flexibility of DCs and its impact on DNs and constructs a collaborative planning framework of DCs, renewable energy sources (RESs), and DNs. This framework employs the interval optimization method to mitigate uncertainties associated with RES output, wholesale market prices, carbon emission factors, power demand, and workloads, and the collaborative planning model is transformed into an interval optimization problem (IOP). On this basis, a novel hybrid solution method is developed to solve the IOP, where an interval order relation and interval possibility method are employed to transform the IOP into a deterministic optimization problem, and an improved integrated particle swarm optimization algorithm and gravitational search algorithm (IIPSOA-GSA) is presented to solve it. Finally, the proposed planning framework and solution algorithm are directly integrated into an actual integrated system with a distribution network and DC to verify the effectiveness of the proposed method.

Landscape design of urban culture transmission based on the regional information security of Internet of Things

In the context of the rapid development of Internet of Things technology, urban cultural communication and information security have become a new focus in the field of landscape design. This paper innovatively discusses the landscape design of urban cultural communication based on Internet of Things regional information security, aiming at building a safe and culturally rich urban landscape environment. Taking the unique regional culture of Zhangjiajie as an example, this study evaluated the cultural communication effect of landscape design under information security guarantee through in-depth case analysis and field investigation, combined with Internet of Things information security technology. The results show that the cluster head node strategy has significant advantages in resisting physical capture attacks, especially when the number of sensor nodes captured is less than 2000, the information loss rate is less than 0.1. This discovery not only improves the level of information security in the Internet of Things environment, but also provides technical support for the effective dissemination of urban culture. In addition, through the detailed analysis and evaluation of landscape, this study further reveals the important role of landscape design in regional cultural inheritance. To sum up, this study not only provides a new perspective for urban landscape design, but also provides practical guidance for the protection and dissemination of urban culture in the era of Internet of Things.

Environmental Health Crises and Public Health Outcomes: Using China’s Empirical Data to Verify the Joint Role of Environmental Regulation and Internet Development

Environmental Health Crises and Public Health Outcomes: Using China’s Empirical Data to Verify the Joint Role of Environmental Regulation and Internet Development

Optimization configuration strategy for regional energy systems based on multiple uncertainties and demand response

With the opening of the power market and the development of the energy Internet, the optimal allocation of regional energy systems has become the key to achieving energy efficiency and economic balance. The article studies how to achieve this balance by optimizing the allocation of regional energy systems under the influence of price fluctuations and load demand uncertainty in the electricity market. This study introduces a real-time electricity price adjustment mechanism to stimulate user participation in energy adjustments and improve energy utilization efficiency. By constructing an optimization model based on multiple uncertainties and comprehensive demand response, uncertainty factors such as energy market price fluctuations and climate change were considered, and user demand response was integrated. The research results indicate that electricity price fluctuations have a significant impact on system operation, while CSP power plant thermal storage fluctuations have a relatively small impact. After the introduction of demand response, the electricity load can be reduced to zero during specific periods, and the adjustment of electricity prices stimulates user participation and improves the consumption rate of renewable energy. The total revenue of the system increased by 54.147 million yuan, demonstrating the potential of optimized configuration in reducing costs and improving efficiency. This study provides important references for building more efficient and sustainable energy systems.

A GSO‐based multi‐objective technique for performance optimization of blockchain‐based industrial Internet of things

SummaryThe latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain‐based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain‐based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain‐based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision‐making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain‐based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations.

Inner External DQN LoRa SF Allocation Scheme for Complex Environments

In recent years, with the development of Internet of Things technology, the demand for low-power wireless communication technology has been growing, giving rise to LoRa technology. A LoRa network mainly consists of terminal nodes, gateways, and LoRa network servers. As LoRa networks often deploy many terminal node devices for environmental sensing, the limited resources of LoRa technology, the explosive growth in the number of nodes, and the ever-changing complex environment pose unprecedented challenges for the performance of the LoRa network. Although some research has already addressed the challenges by allocating channels to the LoRa network, the impact of complex and changing environmental factors on the LoRa network has yet to be considered. Reasonable channel allocation should be tailored to the situation and should face different environments and network distribution conditions through continuous adaptive learning to obtain the corresponding allocation strategy. Secondly, most of the current research only focuses on the channel adjustment of the LoRa node itself. Still, it does not consider the indirect impact of the node’s allocation on the entire network. The Inner External DQN SF allocation method (IEDQN) proposed in this paper improves the packet reception rate of the whole system by using reinforcement learning methods for adaptive learning of the environment. It considers the impact on the entire network of the current node parameter configuration through nested reinforcement learning for further optimization to optimize the whole network’s performance. Finally, this paper evaluates the performance of IEDQN through simulation. The experimental results show that the IEDQN method optimizes network performance.

Stochastic optimization for capacity configuration of data center microgrid thermal energy management equipment considering flexible resources

The rapid development of Internet and cloud computing technologies has led to the expansion of the scale of data centers, resulting in a continuous increase in the demand for data processing and storage. This not only results in higher energy consumption but also makes thermal management a key factor of energy management in data centers. A capacity configuration method is proposed for the core thermal management equipment of data center microgrids, electric boilers, cooling systems, and heat pumps. This method not only considers the uncertainty factors of the source and load but also effectively utilizes the characteristics of various flexible resources in data centers, such as the adjustable ability of different types of batch processing loads, air thermal inertia, and waste heat recovery, significantly improving the energy utilization efficiency and enhancing the system’s economy and flexibility. In addition, a wind power scenario generation method based on conditional least squares generative adversarial networks was proposed to improve the generation quality of wind power scenarios, and a random optimization model was constructed. Considering a certain province’s data center microgrid as a case study, the effectiveness of the model was verified, and a sensitivity analysis of the relevant parameters provided important references for microgrid planning. This study provides a new perspective and tool for the thermal management of data center microgrids and comprehensive utilization of renewable energy, which is of great significance for promoting the improvement of data center energy efficiency and sustainable development.

A Conditional Privacy-Preserving Identity-Authentication Scheme for Federated Learning in the Internet of Vehicles.

With the rapid development of artificial intelligence and Internet of Things (IoT) technologies, automotive companies are integrating federated learning into connected vehicles to provide users with smarter services. Federated learning enables vehicles to collaboratively train a global model without sharing sensitive local data, thereby mitigating privacy risks. However, the dynamic and open nature of the Internet of Vehicles (IoV) makes it vulnerable to potential attacks, where attackers may intercept or tamper with transmitted local model parameters, compromising their integrity and exposing user privacy. Although existing solutions like differential privacy and encryption can address these issues, they may reduce data usability or increase computational complexity. To tackle these challenges, we propose a conditional privacy-preserving identity-authentication scheme, CPPA-SM2, to provide privacy protection for federated learning. Unlike existing methods, CPPA-SM2 allows vehicles to participate in training anonymously, thereby achieving efficient privacy protection. Performance evaluations and experimental results demonstrate that, compared to state-of-the-art schemes, CPPA-SM2 significantly reduces the overhead of signing, verification and communication while achieving more security features.

Pengaruh Konten Pemasaran dan Kualitas Produk di Media Sosial Instagram Erigo Terhadap Keputusan Pembelian Pelanggan

In an increasingly developing era, the use of the internet in marketing in Indonesia is the right choice for business people, because all circles use the internet. One of the internet developments in terms of improving business is using social media. This requires companies to be able to compete with other companies in expanding and accelerating their marketing activities, one of which is through creating content that is put into social media. In their marketing activities, many companies use Instagram social media, one of which is Erigo, because Instagram is one of the phenomenal social media among the public. Instagram social media in Indonesia is ranked as the second most users in 2022 with 84.8% of users. This study aims to see whether there is an influence between marketing content and product quality on Instagram social media on customer purchasing decisions in DKI Jakarta. This research uses a quantitative analysis method approach. This study applied purposive sampling technique with a sample size of 90 respondents. Data processing in this study used the SPSS (Statistical Program For Social Science) version 27.0 program. The results of this study indicate that marketing content has a positive effect on purchasing decisions, product quality has a positive effect on purchasing decisions, and marketing content and product quality have a positive effect on purchasing decisions.

Computation Offloading with Privacy-Preserving in Multi-Access Edge Computing: A Multi-Agent Deep Reinforcement Learning Approach

The rapid development of mobile communication technologies and Internet of Things (IoT) devices has introduced new challenges for multi-access edge computing (MEC). A key issue is how to efficiently manage MEC resources and determine the optimal offloading strategy between edge servers and user devices, while also protecting user privacy and thereby improving the Quality of Service (QoS). To address this issue, this paper investigates a privacy-preserving computation offloading scheme, designed to maximize QoS by comprehensively considering privacy protection, delay, energy consumption, and the task discard rate of user devices. We first formalize the privacy issue by introducing the concept of privacy entropy. Then, based on quantified indicators, a multi-objective optimization problem is established. To find an optimal solution to this problem, this paper proposes a computation offloading algorithm based on the Twin delayed deep deterministic policy gradient (TD3-SN-PER), which integrates clipped double-Q learning, prioritized experience replay, and state normalization techniques. Finally, the proposed method is evaluated through simulation analysis. The experimental results demonstrate that our approach can effectively balance multiple performance metrics to achieve optimal QoS.

A Secure Protocol Authentication Method Based on the Strand Space Model for Blockchain-Based Industrial Internet of Things

The rapid development of the Industrial Internet of Things (IIoT) and its application across various sectors has led to increased interconnectivity and data sharing between devices and sensors. While this has brought convenience to users, it has also raised concerns about information security, including data security and identity authentication. IIoT devices are particularly vulnerable to attacks due to their lack of robust key management systems, efficient authentication processes, high fault tolerance, and other issues. To address these challenges, technologies such as blockchain and the formal analysis of security protocols can be utilized. And blockchain-based Industrial Internet of Things (BIIoT) is the new direction. These technologies leverage the strengths of cryptography and logical reasoning to provide secure data communication and ensure reliable identity authentication and verification, thereby becoming a crucial support for maintaining the security of the Industrial Internet. In this paper, based on the theory of the strand space attack model, we improved the Fiber Channel Password Authentication Protocol (FACP) security protocol in the network environment based on symmetric cryptography and asymmetric cryptography. Specifically, in view of the problem that the challenge value cannot reach a consensus under the symmetric cryptography system, and the subject identity cannot reach a consensus under the asymmetric cryptography system, an improved protocol is designed and implemented to meet the authentication requirements, and the corresponding attack examples are shown. Finally, the effectiveness and security of the protocol were verified by simulating different networking environments. The improved protocol has shown an increase in efficiency compared with the original protocol across three different network configurations. There was a 6.43% increase in efficiency when centralized devices were connected to centralized devices, a 5.81% increase in efficiency when centralized devices were connected to distributed devices, and a 6.32% increase in efficiency when distributed devices were connected to distributed devices. Experimental results show that this protocol can enhance the security and efficiency of communication between devices and between devices and nodes (servers, disks) in commonly used Ethernet passive optical network (EPON) environments without affecting the identity authentication function.

Pola Konsumsi Berita dalam Era Digital: Perbandingan Preferensi Generasi Z Terhadap Sumber Berita Tradisional dan Modern

People interaction has changed along with internet development. Digital news (internet-based) is beginning to alter classical ones. The development of social media and other digital platforms is a result of the Internet development. Reading comprehension and audience skills are undoubtedly impacted by this, particularly for Generation Z because of shifting preferences for news-based-content consumption. By comparing Generation Z's preferences for classical and digital news sources, this study aims to comprehend the trends of news consumption in the digital age. This study used a quantitative approach using a descriptive methodology by employing a valid and reliable questionnaire to collect data that were then randomly distributed to 85 communication science students in semesters 2, 4, 6, and 8. The study's findings explain why communication science students do not vary significantly from other Generation Z students in terms of their preferences for news sources or how they consume them. However, since they study communication science, they are more astute when consuming news from contemporary sources that often include false information or aren't grounded in reality.

A Multi-Hop End-Edge Cooperative Computing Scheme for Power IoT

With the continuous development of the power Internet of Things (PIoT), smart devices (SDs) have been widely used in electric power inspections. Due to the limited resources of intelligent inspection SDs and the distance of overhead transmission lines, many inspection tasks cannot be processed promptly. This paper proposes a multi-hop-based end-edge cooperative computing (MHCC) scheme to address inspection task processing in power IoT. We formulate a multi-hop task offloading problem that minimizes the energy consumption of inspection SDs with delay constraints. We develop a JDPSO algorithm to solve the multi-hop task offloading problem and evaluate the algorithm’s performance based on numerous simulation experiments. The experimental results show that JDPSO reduces the system’s energy consumption by 56.30%, 48.56%, 78.87%, 50.89%, 30.85%, and 68.31%, and also reduces delay by 50.69%, 42.78%, 58.67%, 44.84%, 10.22%, and 62.88% compared to GA, RSA, SSA, MFO, DOA, and ALC schemes.

Dual step hybrid routing protocol for network lifetime enhancement in WSN-IoT environment

Recent development in internet of things (IoT) has been generating huge data due to the large number of nodes deployed and utilized for different applications. In addition, these applications utilize big data and require a more efficient mechanism for data sensing and data transmission. This research work proposes dual step hybrid routing (DSHR) protocol for efficient cluster-based routing. It comprises a two-phase algorithm, which aims at finding the optimal path considering clustering. It further comprises several processes such as cluster head selection, optimal path construction, integrating of nodes to cluster head and sensing range optimization. DSHR is evaluated considering the network lifetime; thereafter model is compared with the existing low energy adaptive clustering hierarchy (LEACH) protocol to prove the efficiency.

Interface Design and Functional Optimization of Chinese Learning Apps Based on User Experience

This research paper investigates the interface design and functional optimization of Chinese learning apps through the lens of user experience. With the increasing popularity of Chinese language learning apps in the era of rapid mobile internet development, users' demands for enhanced interface design and interaction experience have grown significantly. The study aims to explore the influence of user feedback on the design and functionality of Chinese learning apps, proposing optimization strategies to improve user experience and learning outcomes. By conducting a comprehensive literature review, utilizing methods such as surveys and user interviews for data collection, and analyzing user feedback, this research identifies existing issues in the interface design and interaction experience of Chinese learning apps. The results present user opinions, feedback analysis, identified problems, improvement directions, and specific optimization strategies. The study discusses the potential impact of these optimization strategies on enhancing user experience and learning outcomes, compares findings with previous research, addresses limitations, and suggests future research directions. In conclusion, this research contributes to enriching the design theory of Chinese learning apps, offering practical optimization recommendations for developers, and supporting the continuous advancement of Chinese language learning apps.