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<p>With the rapid development of wireless sensor networks and IoT technologies, metaverse services are gradually extending from high-level applications to lower-level sensing and network infrastructure. The large-scale access to IoT sensing data and heterogeneous network environments place higher demands on network latency and transmission stability on the metaverse platform. However, existing research largely focuses on application-layer experience optimization, and systematic methods for dynamic control of transmission latency under complex network conditions remain insufficient. Therefore, in this study, a utilization method from a technical perspective using a metaverse platform is performed. Several research methods are used to conduct this study. The first research method conducts literature research related to using the metaverse platform. The second research method conducts case studies based on these literature studies. The third research method conducts in-depth interviews with experts in these fields. The primary technical challenge of metaverse services identified through this research methodology is unstable network transmission. This phenomenon negatively impacts the practical utilization of the metaverse. To address this issue, this study constructed a mathematical model for an adaptive transmission rate control system that dynamically adjusts data. Furthermore, the performance of the proposed system is verified using the NS-3 simulation tool. Simulation results demonstrate that the proposed algorithm achieved an average delay of 108.8&#xa0;ms, a delay excess rate of 0.8%, and an average transmission rate of 46.8 Mbps, thereby verifying its superior stability and efficiency compared to other comparative models. In addition to the 50-user scenario, this study further supplements high-density concurrent user simulations with 200 and 500 users. The results demonstrate that the proposed algorithm can stably maintain latency and transmission performance in Metaverse environments, thereby supporting large-scale interactive scenarios effectively. The research results verify the applicability of the proposed system in the scenario of metaverse and IoT converged services, providing theoretical support and technical reference for the optimization of metaverse systems for sensing networks.</p>

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A cyclic adaptive algorithm for delay control in metaverse IoT networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月22日

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  • Hyunjin Chun

摘要

With the rapid development of wireless sensor networks and IoT technologies, metaverse services are gradually extending from high-level applications to lower-level sensing and network infrastructure. The large-scale access to IoT sensing data and heterogeneous network environments place higher demands on network latency and transmission stability on the metaverse platform. However, existing research largely focuses on application-layer experience optimization, and systematic methods for dynamic control of transmission latency under complex network conditions remain insufficient. Therefore, in this study, a utilization method from a technical perspective using a metaverse platform is performed. Several research methods are used to conduct this study. The first research method conducts literature research related to using the metaverse platform. The second research method conducts case studies based on these literature studies. The third research method conducts in-depth interviews with experts in these fields. The primary technical challenge of metaverse services identified through this research methodology is unstable network transmission. This phenomenon negatively impacts the practical utilization of the metaverse. To address this issue, this study constructed a mathematical model for an adaptive transmission rate control system that dynamically adjusts data. Furthermore, the performance of the proposed system is verified using the NS-3 simulation tool. Simulation results demonstrate that the proposed algorithm achieved an average delay of 108.8 ms, a delay excess rate of 0.8%, and an average transmission rate of 46.8 Mbps, thereby verifying its superior stability and efficiency compared to other comparative models. In addition to the 50-user scenario, this study further supplements high-density concurrent user simulations with 200 and 500 users. The results demonstrate that the proposed algorithm can stably maintain latency and transmission performance in Metaverse environments, thereby supporting large-scale interactive scenarios effectively. The research results verify the applicability of the proposed system in the scenario of metaverse and IoT converged services, providing theoretical support and technical reference for the optimization of metaverse systems for sensing networks.