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Customizable and Robust Internet of Robots Based on Network Slicing and Digital Twin
IEEE NETWORK ( IF 6.8 ) Pub Date : 2024-03-21 , DOI: 10.1109/mnet.2024.3375503
Kai Liang 1 , Wei Guo 1 , Zan Li 2 , Cheng Li 3 , Chunlai Ma 4 , Kai-Kit Wong 5 , Chan-Byoung Chae 6
Affiliation  

The Internet of Robots (IoR) is proficient in handling complex tasks in challenging environments, yet it encounters challenges related to service and scenario diversity, risk reduction, and ultra-low latency requirements. To address these challenges, we propose an integrated architecture that enhances the IoR’s adaptability, flexibility, robustness, and low latency. This is achieved through the introduction of network slicing, service-based architecture, and digital twin (DT). We have developed an open-source experimental platform to showcase the customizability of the proposed architecture. Slices with different requirements are set up in WiFi and cellular scenarios to demonstrate its versatility. Additionally, we present a DT-assisted deep reinforcement learning (DRL) approach for the IoR to improve DRL performance and mitigate risks associated with undesirable actions. The DT is employed to predict rewards and dynamic state transitions in the physical environment. Furthermore, we introduce a resource allocation method that combines data processing queue preemption and spectrum puncturing. This is designed to accommodate coexisting services, specifically enhanced mobile broadband (eMBB) and bursty ultra-reliable low latency communications (URLLC). Experimental and numerical results validate the effectiveness of our proposed methods, showing improvements in customizability, robustness, latency, and outage probability in IoR.

中文翻译:


基于网络切片和数字孪生的可定制、鲁棒的机器人互联网



机器人互联网(IoR)擅长在充满挑战的环境中处理复杂任务,但也遇到了与服务和场景多样性、降低风险和超低延迟要求相关的挑战。为了应对这些挑战,我们提出了一种集成架构,增强了 IoR 的适应性、灵活性、鲁棒性和低延迟。这是通过引入网络切片、基于服务的架构和数字孪生 (DT) 来实现的。我们开发了一个开源实验平台来展示所提出的架构的可定制性。在WiFi和蜂窝场景下设置不同需求的切片,展示其多功能性。此外,我们还提出了一种用于 IoR 的 DT 辅助深度强化学习 (DRL) 方法,以提高 DRL 性能并减轻与不良行为相关的风险。 DT 用于预测物理环境中的奖励和动态状态转换。此外,我们引入了一种结合数据处理队列抢占和频谱打孔的资源分配方法。其设计目的是适应共存服务,特别是增强型移动宽带 (eMBB) 和突发超可靠低延迟通信 (URLLC)。实验和数值结果验证了我们提出的方法的有效性,显示了 IoR 中可定制性、鲁棒性、延迟和中断概率的改进。
更新日期:2024-03-21
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