Abstract

Global virtual teamwork has emerged as a cornerstone of collaborative teamwork in today’s work landscape, characterized by cross-border collaboration using digital technologies. Although research has started to investigate the mechanisms underlying effective teamwork through information and communication technologies (ICTs), an often-neglected dimension is the pivotal social context within which this collaboration unfolds. To address this research gap, this study adopts a social capital lens on teamwork in proposing social capital as a multidimensional mediator between the usage of ICTs and team effectiveness. The research model was tested using questionnaire data from 271 technologically sensitive and aware companies in the DACH region (Germany, Austria, Switzerland). The findings reveal that using multiple ICTs alone does not suffice for effective global virtual teamwork. Therefore, this study highlights the significance of social capital for the effectiveness of global virtual teamwork, which holds significant implications for theory and practice.

Introduction

Advances in information and communication technologies (ICTs), coupled with the increasing prevalence of globalization, have driven organizations to rely on digital technologies for globally distributed collaboration—so-called global virtual teamwork ( Tavoletti & Taras, 2023 ). Due to several benefits, such as time and cost savings, talent pooling, and increased team diversity, global virtual teams were already well established in today’s workplace before the COVID-19 pandemic ( Jimenez, Boehe, Taras, & Caprar, 2017 ; Kozlowski & Ilgen, 2006 ; Tavoletti & Taras, 2023 ). However, global virtual teamwork also presents significant challenges that can threaten team effectiveness. Reliance on ICTs reduces media richness and delays feedback, including an increased risk of miscommunication and misunderstanding ( Bilotta et al., 2021 ; Klitmøller & Lauring, 2013 ; Mangla, 2021 ).

Based on the premise that a single medium cannot support the two fundamental communication processes of conveyance (i.e., sharing new information) and convergence (i.e., reaching a common understanding), we introduce the concept of ICT usage, which refers to the frequent and strategic use of multiple ICTs ( Dennis, Fuller, & Valacich, 2008 ; Niinimäki, Piri, Lassenius, & Paasivaara, 2012 ). We propose that high levels of ICT usage will increase team effectiveness, which consists of performance, longevity, and satisfaction ( Dennis et al., 2008 ; Hackman, 1987 ).

Conclusion

The confluence of digitalization and globalization has had an enduring influence on utilizing virtual and global collaboration in the workplace. Due to their embeddedness in a social context, teams are a focal point of multiple levels, linking the individual team members and the organizational system holistically ( Kozlowski & Bell, 2013 ). This requires a profound comprehension of the social context, which can enhance the uptake of ICTs and the effectiveness of global virtual teamwork. To fill this research gap, we conceptualized social capital as a multidimensional mediator, encompassing structural, cognitive, and relational social capital ( Nahapiet & Ghoshal, 1998 ), between the adoption of ICTs and team effectiveness composed of performance, satisfaction, and longevity.

In summary, this study provides a solid foundation for integrating social capital in global virtual teamwork. The results of our study indicate that frequent use of multiple ICTs does not influence the effectiveness of global virtual teams. Instead, a significant positive relationship was found between ICT use and social capital. Regarding the individual dimensions of social capital, this relationship was only found for structural social capital. Similar results were obtained for the mediator hypotheses, with both total and structural social capital acting as strengthening mediators between ICT usage and team effectiveness. Conversely, cognitive and relational social capital did not show significant mediating effects. The present research context makes developing cognitive and relational social capital difficult, although their importance cannot be denied, as both social capital and its individual dimensions show a positive relationship with team effectiveness. Possible ways to build cognitive and relational social capital include allocating sufficient time for team interactions and creating shared physical spaces, such as face-to-face meetings, to balance them. Overall, our study provides insights into the extent to which the integration of social capital enhances team effectiveness in today’s digitized and globalized world.

Abstract

     One of the main challenges in the design of wireless ad-hoc and sensor networks is to reduce energy consumption and radio interference and collision, which are strictly and strongly correlated to the transmission range of the nodes. In this article, the transmission ranges of sensors in sparse Wireless Sensor Networks (WSN) is theoretically analyzed and is shown that the transmission ranges of sensors can be significantly reduced in this type of WSNs without losing any connection. Next, a method is presented to calculate reduced transmission ranges. In this method, an iterative transmission range adjustment mechanism is used to select an almost transmission range while no information is needed on the location of nodes or the distance between nodes. This method is very efficient and its communication overhead is low. It can be used as a pre-step of any routing algorithm in order to decrease energy consumption and radio interference in targeted scenarios. The simulation results show the efficiency of the proposed method. We got at least 50% improvement in energy consumption in comparison with full power network, in sparse WSNs.

Introduction

     Wireless Sensor Networks (WSNs) have become increasingly popular in recent years, as they offer a powerful and cost-effective solution for monitoring and collecting data from a variety of environments. A Wireless Sensor Network (WSN) consists of a large number of distributed sensor nodes that cooperatively monitor the physical world [1]. Each node in these networks is typically equipped with a wireless communication device, a small microcontroller, a memory unit and a power source. The nodes in a WSN are typically battery-powered and have limited processing power, memory, and communication bandwidth. Therefore, the design of a WSN requires careful consideration of these constraints, such as the choice of communication protocol, data routing algorithms, and energy management strategies [2].

     The complexity of the WSN domain as well as the presence of many sensor nodes unavoidably introduces a large amount of data in these networks that must be processed, transmitted and received. The sheer amount of data generated by WSNs can pose a significant challenge for the network design and operation. The sensors in a WSN can generate data continuously or periodically, depending on the application requirements. This data must be processed, stored, and transmitted to the base station in a timely and efficient manner. So the complexity of the WSN domain and the presence of many sensor nodes can introduce a large amount of data that must be processed, transmitted, and received [3]. Despite their profound advantages, the utilization of WSNs is often battery-powered and strictly limited due to energy constraints. In fact, most of the energy expenditure of a sensor node occurs during wireless communication, and the remaining energy is consumed during sensing and data processing. The radio transceiver of a node consumes a significant amount of energy during data transmission and reception. Therefore, the energy consumption of a node must be carefully managed to ensure the longevity of the network [4]. Transmission power adjustment for a special transmitter-receiver pair depends on several environmental conditions. The transmission power required to reach the receiver is affected by two main factors including distance and wireless connection quality. Distance affects transmission power. As the distance between the transmitter and receiver increases, the signal strength decreases, resulting in a weaker connection.

Conclusions and future works

     We studied the transmission range of sensors in wireless sensor networks in this study, and proposed a simple yet effective distributed transmission power control algorithm for sparse wireless sensor networks. We showed that this algorithm has advantages in many cases. The main idea of algorithm is simple, but performance of algorithm in targeted scenarios is excellent. We analyzed the performance of this method theoretically and show that using this method is worthwhile in some scenarios, especially in sparse wireless sensor networks with high amount of communication traffic. In these scenarios, our method can be used as a pre-step of any routing algorithm and decrease energy consumption and radio interference. As a future work, we intend to design methods to overcome the collisions problem. Designing methods for resisting the algorithm against the sensor failures is another future work. Furthermore we intend to work on using learning methods like Reinforcement learning for power control. Designing a good energy efficient reward mechanism is one of the main challenges of these methods.

Abstract

The advent of the Internet of Things (IoT) has accelerated the pace of economic development across all sectors. However, it has also brought significant challenges to traditional human resource management, revealing an increasing number of problems and making it unable to meet the needs of contemporary enterprise management. The IoT has brought numerous conveniences to human society, but it has also led to security issues in communication networks. To ensure the security of these networks, it is necessary to integrate data-driven technologies to address this issue. In response to the current state of human resource management, this paper proposes the application of IoT technology in enterprise human resource management and combines it with radial basis function neural networks to construct a model for predicting enterprise human resource needs. The model was also experimentally analyzed. The results show that under this algorithm, the average prediction accuracy for the number of employees over five years is 90.2 %, and the average prediction accuracy for sales revenue is 93.9 %. These data indicate that the prediction accuracy of the model under this study’s algorithm has significantly improved. This paper also conducted evaluation experiments on a wireless communication network security risk prediction model. The average prediction accuracy of four tests is 91.21 %, indicating that the model has high prediction accuracy. By introducing data-driven technology and IoT applications, this study provides new solutions for human resource management and communication network security, promoting technological innovation in the fields of traditional human resource management and information security management. The research not only improves the accuracy of the prediction models but also provides strong support for decision-making and risk management in related fields, demonstrating the great potential of big data and artificial intelligence technology in the future of enterprise management and security.

Introduction

At this stage, there are many problems in the traditional HRM, such as the complicated work of the HRM department, the weak teamwork of the enterprise, and the imperfect employee incentive mechanism, which restrict the overall development of the enterprise. In the new era, the traditional HRM simply cannot meet the new needs of enterprise management, so it needs to change this status quo in line with the development of the times. The arrival of the IoT era has connected the world as a whole, which not only provides many conveniences for human society, but also promotes the economic development of all walks of life. Therefore, this paper proposed to apply IoT technology to enterprise HRM, so as to promote the overall development of enterprise HRM.

This research leverages IoT technology and radial basis function neural networks to significantly enhance the predictive modeling of enterprise human resource needs, outperforming traditional algorithms in accuracy and efficiency. Our model not only predicts employee numbers with an impressive 90.2 % accuracy (compared to the traditional 83.9 %) but also excels in forecasting sales revenue, achieving a remarkable 93.9 % accuracy against the conventional 85.2 %. These advancements highlight our model’s capability to provide more reliable and actionable insights for human resource planning and sales forecasting. Further extending our research’s applicability, we delved into the realm of wireless communication network security, achieving prediction accuracies ranging from 90.29 % to 92.85 % in various tests. This high level of precision in security risk prediction underscores the potential of IoT and data-driven approaches in addressing complex challenges in both human resources and information security management. The comprehensive examination and validation of our predictive models across different domains underscore their significant contribution to enhancing strategic decision-making and risk management in an increasingly digital and interconnected business environment.

Conclusions

In the era of the IoT, there are many problems in the traditional enterprise HRM, which is difficult to meet the new needs of enterprise strategic development, and needs to follow the pace of the times to make changes. The IoT not only brings a lot of convenience to human beings, but also brings some hidden dangers to the security of communication networks. It also needs to combine data driven technology to improve the security of communication networks. In order to promote the development of enterprise HRM, this paper proposed to apply the IoT to enterprise HRM, and combined radial basis function neural network to build a prediction model of enterprise human resource demand. Finally, the model was tested. Under this algorithm, the prediction accuracy of the enterprise human resource demand prediction model for sales revenue and the number of employees was very high, and the prediction accuracy had been significantly improved. This paper also evaluated the wireless communication network security risk prediction model, and the experimental results showed that the prediction accuracy of this model was high.

Abstract

Low-carbon (LC) cities are the cities that people long for today. LC environmental protection plays a very important role in people’s health. The construction of a city in LC is a great cause that contributes to the present and benefits the future. In this study, we propose a development and sustainability evaluation system for building low-carbon cities based on the Internet of Things (IoT). The study is novel in that it considers key areas such as urban planning, environmental issues and solutions, and how the Internet of Things can optimize low-carbon logistics and smart grids, with the aim of promoting the formation of low-carbon city models. This comprehensive approach not only presents problems and solutions in low-carbon urban planning but also focuses on how the Internet of Things can be used as a key technology to promote low-carbon urban development. The urban development of LC was constructed with a sustainable evaluation system, so that people could experience the life of LC. Through the investigation of the degree of atmospheric pollution of LC cities using the Internet of Things, this article found that the highest degree of atmospheric pollution was 30. The highest degree of atmospheric pollution in cities in LC without IoT was 53. The severity of water pollution in cities in LC using IoT technology ranged from 10 to 25, while those without IoT ranged from 30 to 60. The degree of soil pollution in LC cities using IoT technology was concentrated in 10–30, while those without using IoT were concentrated in 30–50. Through these experimental data, it could be seen that IoT technology could reduce environmental pollution, thus achieving the effect of LC cities. This shows that the use of IoT technology in LC cities was highly feasible.

Introduction

LC cities are currently a prominent topic of discussion and delight the imaginations of people around the world. The concept of LC cities, short for “Low-Carbon” cities, has gradually gained traction among the populace. People harbor a deep yearning for the LC lifestyle, envisioning medium-sized cities that are environmentally friendly and pristine, capable of fostering both health and happiness. Consequently, there is an ongoing surge in scholarly endeavors aimed at comprehensively understanding LC cities.

As public interest in LC cities continues to increase, so does the volume of research dedicated to unraveling their intricacies. Liu C, for instance, espoused the view that LC policies are not merely geared towards fostering economic development, but also towards mitigating the impacts of climate change. He posited that LC policies foster a symbiotic relationship between economic industrial structures and foreign investments, thus achieving mutually beneficial growth. Despite the promotion of regional innovation by LC policies, Liu cautioned that innovation might not produce immediate results [1]. Similarly, Xin L stated that the promotion of green and LC development has become a global consensus, but lamented the dearth of studies exploring the scientific nuances and repercussions of LC development [2]. Chen L, on the other hand, emphasized that the primary environmental challenge lies in carbon emissions arising from urbanization. He advocated for stringent measures to curb the growth of urban carbon emissions as part of experimental policies aimed at achieving carbon neutrality [3]. Colenbrander S shed light on the prevalent misconception that LC development is inherently more expensive than high-carbon development, a belief that often hampers opportunities for sustainable growth, especially in resource-constrained environments [4]. It is posited that integrating LC city construction with the Internet of Things (IoT) could potentially catalyze more comprehensive LC development, thereby fostering cities’ transformation towards greater sustainability.

Conclusions

With the increasing severity of pollution, people gradually began to pay attention to the development of LC, which can reduce environmental pollution and bring health to people. As a new technology, the IoT can play a very good role in the construction of LC cities. Cities become intelligent and can reduce many unnecessary pollutants, thus achieving the effect of reducing pollution. This paper studied the development of LC cities based on IoT, in hopes of improving the construction of LC cities through IoT technology. This paper tested the air pollution level, water pollution level, and soil pollution level of LC cities using the IoT technology and without the IoT technology. From the experimental data, it could be seen that the level of air pollution level, water pollution level, and soil pollution level after using IoT technology were lower than those without using IoT technology, which proved that the IoT technology was suitable for the LC development of cities and could reduce pollution. Due to the length of this paper, the experiments and discussions on LC cities were still insufficient and would be improved in the future. Finally, it was hoped that the LC of the city could develop better and better and that people could lead a life that was environmentally friendly and LC.

Abstract

Previous studies have addressed social media adoption in business-to-business (B2B) contexts, but limited research has focused on understanding social media marketing assimilation in the B2B context. Using an integrative model, this study examines how top management participation influences the assimilation of social media in the key marketing areas of product development, pricing decision-making, channel management, and promotion. Furthermore, it examines the resulting impact of these assimilations on B2B firms’ performance, particularly in respect of sales performance and relationship development. The study also examines the moderating impact of absorptive capacity on top management participation in the assimilation processes. The findings reveal that top management participation strongly influences social media assimilation into marketing functions. However, the impact of social media assimilation on B2B firms’ performance is varied. While assimilation for channel management positively impacts both sales performance and relationship development, assimilation for product development is only positively related to relationship development. Conversely, assimilation into the functions of pricing decisions and promotion activities shows no significant impact on either sales performance or relationship development. Finally, absorptive capacity positively moderates top management participation in social media assimilation into all key marketing functions except one. The theoretical and managerial implications of these findings are discussed.

Introduction

Social media have emerged as a “game-changer” (Kumar, 2015, p. 5), playing a leading role in the revolutions taking place in business-to-consumer (B2C) and business-to-business (B2B) marketing. B2C marketing uses social media marketing widely, and B2B operations have begun embracing it to streamline their internal marketing communication, generate leads, drive sales, and strengthen customer relationships. Chaffey (2018) found that 79% of B2B marketing specialists rated social media as the most effective marketing channel, with a further 38% indicating that, if they had surplus in their budget for marketing in the following year, they would devote it to social media marketing. Similarly, Elevation Marketing (2020) noted that 90% of B2B buyers use social media to engage with industry thought leaders, with a further 72% indicating that they use social media channels to research solutions.

B2B social media research tracks the increase in social media use in this business interface. Previous studies have addressed several aspects of social media use in the context of B2B, including salespersons’ social media use in B2B relationships (Bill, Feurer, & Klarmann, 2020; Bowen, Lai-Bennejean, Haas, & Rangarajan, 2021; Fraccastoro, Gabrielsson, & Pullins, 2021), B2B social media adoption (Dwivedi, Ismagilova, Rana, & Raman, 2021; Lashgari, Sutton-Brady, Søilen, & Ulfvengren, 2018; Siamagka, Christodoulides, Michaelidou, & Valvi, 2015; Veldeman, Van Praet, & Mechant, 2017), and B2B social media marketing and branding (Brennan & Croft, 2012; Juntunen, Ismagilova, & Oikarinen, 2020). In addition, social media in B2B entails several unexplored issues worth investigating. Although previous studies have addressed social media adoption in a B2B context, the current body of research shows an observable dearth of producing a nuanced understanding of the factors underlying the assimilation of social media in firms’ marketing functions and how they improve their marketing performance. The assimilation phase occurs after the initial implementation of the innovation. It involves the ongoing integration and use of innovation in the firm’s day-to-day operations. In the context of social media marketing, this phase focuses on leveraging social media platforms to support the attainment of the marketing strategy related to product development, pricing decision-making, channel management, and promotion activities, and on realising the anticipated benefits.

Conclusion

In many instances, technological innovations are introduced into firms with great enthusiasm, which may be occasioned by widespread initial acquisition. Nevertheless, these innovations may fail to be completely deployed in the operations of the acquiring firms. Research has shown that, unless innovations are assimilated, firms do not benefit fully from them. Social media are one innovation that B2B firms are adopting for their marketing activities. While social media adoption in B2B firms has received some research attention, a limited number of studies have focused on social media marketing assimilation in B2B firms, the conditions that enhance this assimilation, and how the assimilation contributes to firms’ performance. In this study we examined how top management participation influences the assimilation of social media in the key marketing functional areas of product development, pricing decision-making, channel management, and promotion activities. We also examined how the existing absorptive capacity of B2B firms moderates the influence of top management participation on the assimilation of social media in the key marketing functional areas. Finally, we analysed how social media marketing assimilation into these marketing functional areas improves a firm’s performance outcomes of sales and relationship development. The study’s results show that top management participation in the social media marketing assimilation processes has a strong and positive impact on the assimilation of social media for key marketing functional areas, namely product development decisions, pricing decision-making, channel management, and promotion activities. Furthermore, the findings indicate that top management participation in the assimilation of social media in the functional areas, except in the case of promotion activities, is positively moderated by the existing absorptive capacity of the firm. The results also show that social media marketing assimilation for product development has a significant impact on sales performance, while its assimilation for channel management has a significant impact on both sales performance and relationship development.

Abstract

Although artificial intelligence can contribute to decision-making processes, many industry players lag behind pioneering companies in utilizing artificial intelligence-driven technologies, which is a significant problem. Explainable artificial intelligence can be a viable solution to mitigate this problem. This paper proposes a research model to address how explainable artificial intelligence can impact decision-making processes. Using an experimental design, empirical data is collected to test the research model. This paper is one of the pioneer papers providing empirical evidence about the impact of explainable artificial intelligence on supply chain decision-making processes. We propose a serial mediation path, which includes transparency and agile decision-making. Findings reveal that explainable artificial intelligence enhances transparency, thereby significantly contributing to agile decision-making for improving cyber resilience during supply chain cyberattacks. Moreover, we conduct a post hoc analysis using text analysis to explore the themes present in tweets discussing explainable artificial intelligence in decision support systems. The results indicate a predominantly positive attitude towards explainable artificial intelligence within these systems. Furthermore, the text analysis reveals two main themes that emphasize the importance of transparency, explainability, and interpretability in explainable artificial intelligence.

Introduction

Automotive industry leaders, such as Tesla, have made substantial investments in artificial intelligence (AI) to expedite the introduction of self-driving vehicles to the market, enhancing their competitive capabilities. The integration of AI in supply chain operations has played a crucial role in enabling Tesla to optimize its operational costs [64] while simultaneously facilitating the establishment of a Gigafactory in China [78]. We are witnessing a rapid digital transformation driven by the integration of AI in supply chain management. The COVID-19 pandemic forced companies and organizations to expedite the digitalization of their operations [6]. To enhance their competitive edge, prominent companies, including Amazon, Walmart, Alibaba, Siemens, and Toyota have embraced AI-based technologies to automate and digitalize their operations and supply chain activities [1,32]. Digital transformation also introduces new possibilities for potential cyberattacks. However, using AI-based technologies for decision-making during cyberattacks (e.g., American Express monitoring; [75]) offers significant advantages that outweigh the potential losses incurred.

Although AI-based technologies can contribute to decision-making processes in operations and supply chain management, many industry players are lagging behind pioneering companies in utilizing AI-driven technologies, which is a main problem. Therefore, the adoption of AI-based technologies in decision-making processes, particularly during sensitive situations such as cyberattacks, may encounter potential barriers that delay their usage. Due to its vital advantages, AI has received much attention from decision-makers to address resilient case studies [45] and other sensitive problems such as healthcare [69]. The lack of explanations of the underlying AI processes leads to the rejection of AI in decision support systems [60]. Leveraging AI-powered decision-making platforms can significantly facilitate and expedite the decision-making process, which results in improved overall performance. For instance, the Colonial Pipeline, a U.S. oil supplier, faced a cyberattack and, after a week of deliberation, opted to pay around $4.4 million to solve the issue [21]. A quick decision on the first day through agile decision-making could have saved them money and enabled the uninterrupted continuation of their operations with stockholders.

Results

We employed regression analysis to test the hypothesized relationship between research model variables. We conducted required tests relating to the experimental design along with method assumptions and bias checks.

4.1. Design check
To check the design, the scenario test, the realism test, the validity test, and the manipulation test were performed. We conducted the scenario check to measure the participants’ learning process regarding the provided information. The results showed that participants’ learning was strong enough to support the scenario design (in the first scenario, the Mean = 5.3 out of 7, with a Standard Deviation = 0.99, and in the second scenario, the Mean = 5.5 out of 7, with a Standard Deviation = 0.85). The realism check was conducted through a face validity process and tested using the quantitative approach proposed by Thomas et al. [65]. Participants supported the realism check by agreeing that the scenarios were close enough to reality (Mean = 4.08 out of 5; Standard Deviation = 0.44). Similarly, the validity check supported the scenario design (Mean = 4.07 out of 5; Standard Deviation = 0.43). The manipulation test supported the manipulation by showing a significant difference between the two manipulated levels (p-value <0.001, the first level, the low level of XAI: Mean = 2.68 out of 7; Standard Deviation = 0.12; and the second level, the high level of XAI: Mean = 5.9 out of 7; Standard Deviation = 0.10).

Abstract

Using a unique dataset comprising 6313 firm-year observations for Chinese listed firms between 2008 and 2017, we investigate the impact of CEO social capital on cost of debt. Our results show that CEO social capital is negatively related to cost of debt, and the impact of CEO social capital in environments with a low degree of marketization or social trust is more pronounced than in environments with a high degree of marketization or social trust. Moreover, our results reveal that two potential mechanisms, discretionary accruals and information disclosure quality, mediate the impact of CEO social capital on cost of debt.

Introduction

Research into social capital in the area of social economics emerged late last century (e.g., Coleman, 1988 ; Putnam, 1993 , Putnam, 1995 ; Knack and Keefer, 1997 ; Uzzi, 1997 ; Portes, 1998 ; Woolcock, 1998 ; Dasgupta, 2005 ; Fafchamps, 2006 ). Prior studies have acknowledged the significance of the personal social capital embedded in social networks for corporate commercial activities, including corporate financing ( Engelberg et al., 2012 ; Ferris et al., 2017 ; Huang and Shang, 2019 ; Jin et al., 2019 ), corporate investment ( Faleye et al., 2014 ; Fracassi, 2017 ), mergers and acquisitions ( Fracassi and Tate, 2012 ; El-Khatib et al., 2015 ), executive employment and compensation policies ( Hwang and Kim, 2009 ; Fracassi and Tate, 2012 ; Nguyen, 2012 ; Engelberg et al., 2013 ; Ferris et al., 2020 ), risk-taking and accounting practices ( Ferris et al., 2017 , Ferris et al., 2019 ; Panta, 2020 ), and litigation risk ( Zhang et al., 2023 ). Personal social capital embedded in social networks, especially the social capital of corporate executives, has a significant influence on corporate financing. Social capital enhances the trust between borrowers and lenders and increases risk-taking behavior as social capital is seen as an informal insurance. As invisible social credit and a conduit for the exchange of information, CEO social capital benefits corporate financing activities.

We are motivated to investigate the connect between CEO social capital and cost of debt due to the complex institutional background of network and the importance of debt financing in the context of China. Despite China’s remarkable economic growth in recent decades, its financial and legal environments remain underdeveloped. Allen et al. (2005) suggest that in order to sustain this growth, China requires alternative governance mechanisms and informal institutional arrangements. In the Chinese context, China is characterized as a relationship-based society, where social capital embedded in social networks, known as “Guan Xi,” holds significant prevalence and complexity. These networks provide Chinese additional social resources such as information and status ( Song et al., 2012 ; Bian, 2019 ). For example, the CEOs and chairmen of Chinese non-state-owned firms use bank loans to raise funds through their political contacts ( Yu and Pan, 2008 ). Loan approvals and covenants for Chinese corporates are influenced by CEO social capital ( Shen et al., 2009 ; Xu and Li, 2016 ). CEO social capital is negatively associated with firm leverage and short-term debt ratio, and social capital lowers the need for corporate bank borrowing ( Huang and Shang, 2019 ), and the social capital benefits improving the quality of corporate loans ( Jin et al., 2019 ). These studies only reveal the impact of managerial social capital on corporate financing, while the research into the impact of CEO social capital on the cost of debt is relatively scarce, particularly in emerging markets such as China.

Conclusion

This study investigates the relationship between CEO’s social capital and cost of debt. We posit that CEO’s social capital offers incentives for CEOs to provide enhanced information transparency and quality, which lowers the monitoring costs for the borrowers, inducing reduced cost of debt. Additionally, CEO’s social capital creates a sense of trust between the lenders and the borrowers due to the informal tie that social capital creates among the networks. Using a unique dataset consisting with 6313 firm-year observations from the firms listed on the Shanghai and Shenzhen Stock Exchange markets from 2008 to 2017, our results suggest that CEO’s social capital reduces cost of debt in the context of China, and such effect is more pronounced when there is a lack of information verification from the regulators (i.e., marketization) and societal norm (i.e., social trust). Moreover, we find that CEO’s social capital reduces cost of debt through the path of reduced discretionary accruals and information disclosure quality. Our results hold after a battery of robustness checks, including the three individual proxies (betweenness centrality, closeness centrality and degree centrality) for CEO social capital, the two alternative proxies for cost of debt, sample without political capital, excess social capital, the consideration of family business characteristics, the interactive effect of state-owned enterprises and endogeneity concerns.

Abstract

Abstract
The rapid expansion of mobile broadband networks and the proliferation of Internet of Things (IoT) applications have substantially increased data transmission and processing demands. However, the application domains of IoT-enabled models often face resource limitations, requiring rapid responses, low latency, and large bandwidth, surpassing their inherent capabilities. To address these challenges, we propose a fishnet approach-based packet scheduling and resource allocation system, termed Fishnet-6G, to optimize network resource allocation in the proposed 6G networks. Initially, we constructed a Sierpinski Triangle-based network in a 6G-IoT environment, enhancing device connectivity. We utilize the Quantum Density Peak Clustering (QDPC) algorithm to perform clustering for IoT devices, establishing Cluster Head (CH) and Substitute CH (SUB CH) based on actual metrics. Furthermore, traffic prediction is achieved through two processes, grouping, and fair queue status, using the Improved Deep Deterministic Policy Gradient (IMPDDPG) algorithm with a variable sampling rate, resulting in well-organized packet scheduling. Subsequently, we perform optimal packet scheduling by employing the Willow Catkin Optimization (WCO) algorithm, and the scheduled packets are managed within a Fishing Net Topology to reduce energy consumption and system complexity. Finally, we allocate the scheduled packets to the desired resource blocks using the Bayesian Game-Theoretic Approach (BGTA). The proposed approach is implemented using Network Simulator-3.26, and the performance of the Fishnet-6G model is evaluated based on time, transmission rate, energy efficiency, average throughput, latency, and Packet loss rate. Numerical analysis demonstrates that Fishnet-6G outperforms existing approaches across these metrics, showcasing its effectiveness in addressing the challenges of 6G-IoT networks.

Introduction

The rapid expansion of IoT devices has led to notable progress in data generation, contributing significantly to the growth of linked objects inside the IoT systems. It is essential to highlight that this development is experiencing a sustained increase as many objects and devices are interconnected in IoT applications [1,2]. As 5G networks continue to improve and expand, concerns arise about their capacity to support the constantly evolving needs of emerging IoT applications fully [3]. The impending era of sixth-generation (6G) mobile communication is marked by the widespread adoption of IoT devices and cellular networks, contributing to a substantial increase in energy consumption and network traffic [4]. The ever-growing demands of intelligent and self-sufficient systems challenge their capabilities and spark the evolution towards the 6G-IoT vision [5]. By 2025, it is projected that there will be over 25 billion IoT devices, putting immense pressure on current multiple access techniques and necessitating the development of B5G wireless systems. In light of this uncertainty, attention has turned towards the potential of 6G wireless technologies to overcome these limitations and propel the transformation of existing networks [6]. Envisioned as a vital component in the development of sustainable smart cities, the 6G-IoT vision incorporates cutting-edge elements such as intelligent edge computing, health analytics, and multidimensional design technology, with a firm emphasis on critical features such as scalability, wireless multi-access, personalized AI, machine learning, cyber security, blockchain, and augmented sensing [3,[6], [7], [8], [9]]. The strategic placement of small cells in a 6G system optimizes coverage and data transmission efficiency, while sensor nodes collect essential information communicated to IoT devices, enabling diverse applications like home automation and healthcare, showcasing the network’s sophistication [10,11]. Network architecture and administration must mitigate complexities in the 6G network to provide exemplary performance. The construction of a 6G network presents numerous advantages, encompassing enhanced capacity, extensive coverage, cost-effectiveness, load balancing, and energy efficiency [12].

Conclusion

This study proposes a fishnet technique to overcome the significant issues related to packet scheduling and resource allocation in the context of the 6G-IoT environment. The primary objective of this study is to implement the Sierpinski triangle-based network construction in a 6G-IoT environment to mitigate the issues of excessive energy consumption and communication overhead. The edge server utilizes QDPC to provide clustering for IoT devices. The suggested study aims to group IoT devices based on relevant metrics. This clustering process involves the identification of cluster heads and sub-cluster heads, which are executed by an edge server. Subsequently, the process of traffic prediction is conducted through two distinct stages, namely grouping and fair queue status assessment, employing the IMPDDPG algorithm with variable sampling rate. Packet scheduling is executed by considering the relevant metrics and employing the WCO algorithm. Following the establishment of a schedule, the management of packets is conducted inside the fishing net architecture, resulting in a reduction in energy consumption, complexity, and overall process weight. The allocation of scheduled packets to the requested resource blocks is accomplished using BGTA, while taking into consideration the operational indicators. The proposed approach was tested using Network Simulator-3.26, and its effectiveness was assessed by comparing its performance to existing methods taking into account various permanence metrics, including time, transmission rate, energy efficiency, average throughput, latency, and packet loss rate. The efficacy of our methodology is examined by quantitative analysis, which substantiates that our technique surpasses existing methodologies across all metrics. The future work will consider cases high availability in cases of Base Station failures and massive IoT V2X environment to support the next generation of wireless networks.

Abstract

The Industrial Internet of Things (IIoT) and Digital Twins (DTs) are changing how digital models and physical products interact. IIoT connects to intelligence in the physical world, and DTs virtually represent their physical counterparts. As a result, DTs will be invaluable for testing and simulating new parameters and design variants. However, Despite the undeniable potential of DTs, they still cannot differentiate themselves from simulation technologies, and their application and adoption remain limited. This study defines the concept, highlights the evolution and development of DTs, reviews its key enabling technologies, identifies IIoT’s role as the backbone of DTs, examines DTs trends, highlights the key challenges, and explores its applications in the manufacturing process and Industry 4.0.

Introduction

The industrial sector has experienced a significant transformation due to several factors, the most notable being the shift in how products are sold, from the factory floor to the point of sale [1]. Over the past 15 years, there has been a growing trend among consumers to embrace e-commerce and digital channels for their purchasing needs [2], [3] Buyers’ habits changed to home ordering–a shift that industry experts forecast to be permanent. This consumer shift brought changes to supply chain disruptions — a dramatic need for increased agility and resilience in the consumer product manufacturing industries. As a result, manufacturers incorporated robust digital platforms in a post-pandemic marketplace to maintain competitiveness, optimize their manufacturing value chain, and provide end-to-end supply chain visibility [4], [5]. According to a new survey, manufacturing companies leveraging digital technologies to transform their operations have reduced costs by 5–30 percent, increased productivity by 5–40 percent, and achieved substantial agility and sustainability improvements [6].

The second factor having a correlating impact on industrial companies’ business models is COVID-19. The pandemic has led to significant disruptions affecting nearly every aspect of global industry [4]. While supply chain disruptions have uncovered operational vulnerabilities, they have also presented transformative opportunities for manufacturing companies [7]. Manufacturers quickly adapted during disruptions by implementing new advanced technologies at the core of true fourth industrial revolution innovation [6]. The swift advancement of digital technology and designed intelligence has driven the 4th industrial revolution [8]. Similarly, Industry 4.0 helped advance the usage of technologies like the Industrial Internet of Things (IIoT) and DT and enabled progress and betterment in the industries that utilize DT  [9], [10], [11].

Summary and conclusions

In the post-pandemic, manufacturers use digital transformation to gain much-needed agility by improving supply-chain visibility, creating feasible production plans, deploying advanced production planning and scheduling, and providing visibility, transparency, and traceability to consumer demand. We analyzed the use of DT and showed that DT technology with IoT capabilities has emerged as a critical concept in the digital transformation of the industry. It is used in Industry 4.0 and connected digital factories. The construction of DTs can be tailored for various objectives, including the conceptualization, production, examination, modeling, and management of non-digital systems. This is done to comprehend, supervise, and potentially enhance the real system. This paper explored the possibilities, challenges, and limitations of DT implementation and its use in different manufacturing domains. Research concerning DT solutions in manufacturing deals with production planning and control, the primary data sync within a production system that ties everything together. Supply chain and logistics management is another area where use cases of DT are reviewed in the literature.

DTs have been effectively utilized across various application fields, positioning them as a crucial component in Industry 4.0. Combining DTs and IIoT can enable new business models, such as predictive or product-as-a-service maintenance. In addition, with the ability to monitor and analyze data from multiple sources in real-time, companies can offer new value-added services to their customers. Overall, implementing DTs and IIoT can unlock significant benefits for industries regarding operational efficiency, cost savings, and new business opportunities.

Abstract

Distributed denial-of-service (DDoS) attacks pose a significant threat to computer networks and systems by disrupting services through the saturation of targeted systems with traffic from multiple sources. Real-time detection of these attacks has become a critical cybersecurity task. However, current DDoS attack detection methods suffer from high false positive rates and limited ability to capture the complex patterns of attack traffic. This research proposes an enhanced approach for detecting DDoS attacks using a hybrid feature selection technique in combination with an ensemble-based classifiers. The ensemble-based approach aggregates many decision trees to increase classification accuracy and reduce overfitting and model robustness. The feature selection technique uses correlation analysis, mutual information, and principal component analysis to identify the most useful characteristics for attack detection. The ensemble-based Random Forest classifier from the various ensemble-based approaches with the specified relevant features produces the best detection rates. Many datasets related to identifying DDoS attacks are used to evaluate the proposed model, and experimental findings demonstrate that it surpasses existing techniques in terms of accuracy, recall, precision, f1-score, and false positive rate, with other evaluation metrics. The proposed approach achieves almost 100 % accuracy, 100 % true positive rate, and 0 % error rate making it a promising solution for DDoS attack detection.

Introduction

A distributed denial of service (DDoS) attack uses a large number of compromised devices, sometimes those that are part of a botnet, to overload a targeted system or network with traffic and render it inaccessible to authorized users [1,2]. The goal of a DDoS attack is to disrupt the normal functioning of the target system or network, denying access to its intended users [3,4]. In this DDoS attack, the attacking devices may be compromised computers, routers, or IoT devices that have been infected with malware or taken over by an attacker. These devices are then directed to send a large volume of traffic to the target system or network, making it unable to respond to legitimate requests. DDoS assaults can originate from any location in the world, and since they are widespread, it may be difficult to effectively prevent or stop them. They are frequently employed by hackers or other criminals to demand money or to obstruct the work of a company, government, or organization. DDoS attacks can cause significant harm, including financial losses, reputational harm, and even legal consequences [5,6].

DDoS attacks raise significant ethical and legal concerns due to their potential to harm sensitive data and jeopardize user information. These attacks are on the rise in terms of both frequency and sophistication, which makes their identification and mitigation increasingly challenging [7]. Attackers employ a variety of techniques and technologies, and the impact of DDoS attacks extends beyond the targeted organization. For instance, an attack on a critical infrastructure provider can have a far-reaching impact, affecting other organizations, governments, and individuals. Consequently, addressing the DDoS attack problem is not only essential for individual enterprises but also for the broader community and society at large. Thus, there is a pressing need to develop effective methods and tools to detect and minimize DDoS attacks [8,9]. As DDoS attacks continue to grow in complexity, they pose challenges for mitigation. Countermeasures are difficult to implement because these attacks can target multiple network levels and originate from diverse sources [10]. Moreover, distinguishing genuine traffic from attack traffic remains a challenge. To effectively reduce the impact of DDoS attacks, innovative and collaborative approaches are required to address these evolving challenges [11,12].

Conclusion and future direction

The advanced approach presented for DDoS attack detection, employing a hybrid feature selection method and an ensemble-based Random Forest machine learning classifier, has showcased exceptional performance compared to existing techniques. The fusion of various feature selection methods and ensemble-based classifiers has yielded remarkable results, with near-perfect accuracy and outstanding performance across a range of evaluation metrics, making it a highly promising solution for real-world DDoS attack detection. The model’s consistent excellence in handling diverse DDoS datasets, including Botnet DDoS, APA-DDoS, DDoS-SDN, and others, underscores its versatility and effectiveness. As DDoS attacks continue to evolve in complexity and frequency, the demand for innovative and efficient approaches to identify and mitigate these threats has become imperative. The proposed model represents a significant leap in this domain and holds the potential to deliver substantial benefits to cybersecurity practitioners and organizations. Its applicability in real-time scenarios and its capability to effectively mitigate DDoS attack impacts ensure the uninterrupted availability and functionality of vital systems and services.

The methodologies and principles outlined in this research can be extended to identify and mitigate other network threats beyond DDoS attacks. Investigating its applicability to various cybersecurity challenges is a promising direction. Developing a comprehensive framework that not only detects attacks but also initiates adaptive responses and countermeasures in real-time can enhance network security. This proactive approach will be pivotal in addressing evolving cyber threats.

Abstract

The popularity of cloud computing, with its incredible scalability and accessibility, has already welcomed a new era of innovation. Consumers who subscribe to a cloud-based service and use the associated pay-as-you-go features have unlimited access to the applications mentioned above and technologies. In addition to lowering prices, this notion also increased the reliability and accessibility of the offerings. One of the most crucial aspects of cloud technology is the on-demand viewing of personal services, which is also one of its most significant advantages. Apps that are cloud-based are available on demand from anywhere in the world at a reduced cost. Although it causes its users pain with safety concerns, cloud computing can thrive because of its fantastic instantaneous services. There are various violations, but they all accomplish something similar, taking the systems offline. Distributed denial of service attacks are among the most harmful forms of online assault. For fast and accurate DDoS (Distributed Denial of Service, distributed denial of service) attack detection. This research introduced the DDOS attack and a method to defend against it, making the system more resistant to such attacks. In this scenario, numerous hosts are used to carrying out a distributed denial of service assault against cloud-based web pages, sending possibly millions or even trillions of packets. It uses an OS like ParrotSec to pave the way for the attack and make it possible. In the last phase, the most effective algorithms, such as Naive Bayes and Random Forest, are used for detection and mitigation. Another major topic was studying the many cyber attacks that can be launched against cloud computing.

Introduction

DDos attack is a distributed type of attack mode in which an attacker controls a large number of attack machines and sends out DoS attack instructions to the machine. In the latest Internet security report, DDoS attacks remain one of the major cybersecurity threats. The inexpensive pricing and “pay-as-you-go” focused accessibility to computational features and amenities on demand make cloud-based services a formidable competitor to the conventional IT solutions available in prior eras. The use of cloud computing is gaining popularity rapidly. Whether entirely or largely governments and companies have moved their IT infrastructures onto the cloud. Cloud-based Infrastructure offers various advantages compared to traditional, on-site conventional infrastructures. The removal of expenses associated with operation and impairment, as well as the accessibility of materials on request, are only a few of the advantages. However, there are many concerns that cloud consumers have, and the research addresses these issues. The majority of these inquiries centre on safeguarding operational concepts and information. Many security-related attacks can be prevented in conventional IT systems that do not use cloud computing. Focused cloud-based crimes are already using their innovations. Many security vulnerabilities in cloud computing are unique compared to their predecessors in non-cloud computing environments because data and business logic are stored on an external cloud server that lacks accessible oversight. The denial-of-service (DoS) assault is one technique that has been in the spotlight recently. Denial-of-service incidents are directed at the server rather than the people it supports. DoS attackers attempt to flood live servers by masquerading genuine users to overload the service’s capacity to handle incoming inquiries [1]. Cloud computing is an Internet-based service that enables users to access configurable computing resource sharing pools (including server, storage, application software, services, networks, etc.) to achieve online access to computing resources on demand. As a mixture of emerging technologies and business models, cloud computing has developed rapidly in recent years due to its advantages of super-large scale, virtualization, high reliability, good scalability and on-demand services. To overcome this issue, multiple inquiries are sent to the server simultaneously. The term “distributed denial of service,” or DDoS, refers to a variation on the classic “denial of service” that uses numerous computers to attack and impair one service at a time simultaneously.

Conclusion

The key goals of this study are to learn how to recognize and prevent attacks involving distributed denial-of-service. The first and most crucial step is determining which ports can be exploited. Nevertheless, this approach is not risk-free because susceptible ports are more likely to be exploited. Given ParrotSec’s track record for stability and performance, we decided it would be the ideal choice for our company’s computer system. Since a DDoS attack involves sending one million separate packets toward the target, starting with an on-the-internet website would be best. The targeted website was taken offline after it became clear that an assault had happened. Machine learning is constructive in this detecting process as well. Using this data, the most popular and accessible tool, “weka,” is being trained. Employing pre-processing techniques and the “discretize” filter to achieve the desired effect. Therefore, the following phase is not only quite intriguing but also rather useful for both forecasting and detecting. We employed both methods and compared the findings on the same platform, and we found that the naive Bayes method provides the most trustworthy conclusions. PCA selected 21 features from the possible 42 features, while LVQ selected only 20 features. The results suggest that LVQ based feature selection in the DT model may be more accurate than other methods in identifying attacks. As mentioned earlier, the model also outperformed the previous models in terms of accuracy, recall, specificity, and f-score. It was shown that the naive Bayes model had significantly better predictive power than the random forest model. There is a chance that a false positive rate warning will be triggered for packet transmissions within a network. Moreover, when compared to the random forest, naive Bayes produces considerably more accurate forecasts. It was demonstrated that the Naive Bayes algorithm outperformed the random forest technique to identify the false and actual rate of transmissions. The result detection is not carried out in real time. Although attacks can be detected, real-time alarm cannot be realized in the environment of high cluster security, so the feasibility of real-time monitoring under Hadoop platform should be studied continuously.

Abstract

Deep learning techniques have shown significant impact in enhancing security across various domains by leveraging artificial neural networks models. When applied to cloud computing security, deep learning offers cost-effective solutions by automating threat detection, reducing manual monitoring, and improving overall security effectiveness. Deep learning models using neural networks play crucial role in tasks like intrusion detection, malware detection, anomaly detection, and log analysis. Integration of deep learning into cloud security requires careful evaluation of existing systems, defining objectives, dataset selection and preparation, model tuning, and eventual modifications for compatibility. Furthermore, implementing deep learning techniques in cloud security entails considering factors such as computational resources, data collection and preparation costs, model development, integration efforts, and ongoing monitoring and maintenance. This paper proposes a feed-forward propagation Artificial Neural Network (ANN) model in cloud security and investigates the key steps for integrating such models into cloud security strategies. Considering that the effectiveness of the ANN model depends on factors such as training data quality, network architecture, and weight adjustment algorithms, the study utilizes a dataset from Kaggle.com for validation and demonstrates steps involved in training and evaluation of the ANN model.

Introduction

Deep learning (DL) techniques have emerged to become effective tools for enhancing security in various domains. These techniques leverage the capabilities of artificial neural networks to learn and identify patterns from vast amounts of data, enabling more robust and efficient security solutions [1][2]. When applied to cloud computing security, deep learning techniques offer several benefits having direct implications in the cost-effectiveness of the products. A great benefit of implementation of DL in security of cloud is seen in automated threat detection. DL algorithms can analyze large volumes of data, such as network traffic logs, system logs, and user behavior, to detect anomalies and potential security threats automatically [2], [3]. This together with other utilizations as a result of DL, reduces the need for manual monitoring and analysis, what leads to faster identification of security incidents, timely response, mitigation, and reduced costs in time and resources. Considering this, through automation of various security tasks, DL minimizes the risk of human error, which can lead to security breaches and associated costs.

Automated systems powered by deep learning can perform tasks consistently and accurately, enhancing overall security effectiveness [4], [5]. DL models learn patterns and relationships in data, allowing for more accurate threat detection and classification. Through detecting patterns and anomalies which could go unnoticed by traditional rulebased systems, DL implementation makes it possible for organizations to optimize resource allocation and minimize unnecessary costs.

Conclusion

The integration of deep learning techniques into cloud computing security offers numerous benefits. By analyzing large volumes of data, deep learning algorithms can detect security threats in real-time and minimize various risks. However, the implementation of deep learning in cloud security requires careful evaluation of costs and resources. Organizations struggle when having to adjust the solution to the computational resources, data acquisition and preparation costs, personnel expertise, and ongoing monitoring and maintenance.

The integration of deep learning techniques in cloud security involves few steps for a successful outcome. These include assessing the strengths and weaknesses of the existing system, identifying specific tasks that deep learning can address, selecting suitable models and algorithms, training the models with labeled data, and ensuring compatibility and integration with existing components. Continuous monitoring and feedback are also crucial stages for maintaining the efficiency and effectiveness of the integrated system.

Abstract

The Internet of Things (IoT) connects devices, enabling real-time data acquisition, automation, and collaboration. Wireless sensor networks are one of the important components of the Internet of Things, consisting of many wireless sensor nodes distributed in space. These nodes can perceive environmental information and transmit it to other nodes through wireless communication. In wireless sensor network routing optimization methods, improved ant colony algorithm can be used to find the optimal routing scheme. Ant colony algorithm simulates the behavior of ants in the process of searching for food, and optimizes factors such as transfer probability and pheromone concentration to enable ants to find the shortest path. In wireless sensor networks, node positions can be used as reference nodes and anchor nodes, combined with the objective function of wireless sensor network routing optimization, and improved ant colony algorithm can be used to solve the optimal path, thus obtaining the optimal wireless sensor network routing optimization scheme. Through experimental results, it can be found that the proposed method performs well in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. These optimization results have positive implications for the sustainable development and practical application of the Internet of Things, which can improve the development of the digital economy and enhance the construction of smart cities.

Introduction

At present, wireless sensor networks have been widely applied in various fields such as environmental monitoring, agriculture, and the Internet of Things. In these applications [1,2], wireless communication between nodes in wireless sensor networks plays a crucial role. The Internet of Things has characteristics such as connectivity, intelligence, and real-time. It connects devices through wireless communication technology and endows them with perception, computing, and decision-making capabilities. This enables the Internet of Things to obtain and transmit data in real-time, automate collaboration and data sharing, improve production and operational efficiency, and reduce costs. At the same time, the Internet of Things utilizes big data analysis and predictive maintenance to provide insight into market trends and user needs, helping enterprises make more accurate decisions. In addition, the application of the Internet of Things in fields such as smart homes and health monitoring is improving people’s quality of life and promoting the process of sustainable development. Therefore, the Internet of Things has significant advantages in improving efficiency, reducing costs, enhancing insight, improving quality of life, and promoting sustainable development. The routing optimization of wireless sensor networks has become an important and challenging issue due to the limited energy of sensor nodes, wide and random distribution of nodes, and other characteristics. The goal of wireless sensor network routing optimization is to improve network performance and energy utilization efficiency by selecting appropriate paths, minimizing energy consumption, maintaining low latency, and maximizing network lifespan [3]. It involves techniques and mechanisms such as path selection and transmission scheduling to ensure efficient and accurate data transmission between nodes. The research on routing optimization in wireless sensor networks is of great significance for improving network performance, extending network lifespan, providing reliable data transmission, and supporting real-time applications. By adopting effective routing optimization algorithms and mechanisms, more reliable, intelligent, and efficient wireless sensor network systems can be achieved. Wireless sensor network routing optimization plays an important role in the Internet of Things, as it can improve network performance and energy utilization efficiency, extend network lifespan, provide reliable data transmission and support real-time applications for various application fields. Therefore, routing optimization in wireless sensor networks is of great significance in promoting the development and application of animal networking [4,5].

Conclusion

Wireless sensor network routing optimization is one of the key issues in the Internet of Things, which is of great significance for improving network performance, extending network lifespan, providing reliable data transmission, and supporting real-time applications. The current routing optimization methods have problems such as high energy consumption, high transmission delay, high number of dead nodes, and low network throughput. To address these issues, this article proposes a wireless sensor network routing optimization method based on improved ant colony algorithm in the Internet of Things. Through experimental testing, it has been proven that the proposed method has absolute advantages over the current method in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. This research achievement can not only improve the energy efficiency of wireless sensor networks, but also improve the quality of data transmission and provide better support for real-time applications. At the same time, this method can also optimize network capacity and meet the network performance requirements of different application scenarios. These contributions provide important support for the reliability, intelligence, and efficiency of wireless sensor networks, and promote the further development of wireless sensor network technology in various application fields. However, there are still some challenges in the research of routing optimization in wireless sensor networks. For example, when optimizing routing in a dynamic environment, it is necessary to consider issues such as changes in network topology and the balance between energy consumption and data transmission quality. These challenges require further in-depth research to propose more comprehensive and adaptable routing optimization methods for wireless sensor networks, and to promote the development and application of animal networking technology.