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.