Proceedings of the 1st Workshop on Digital Twin & Edge AI for Industrial IoT最新文献

Optimal graph partitioning for time-sensitive flow scheduling towards digital twin networks 面向数字孪生网络的时间敏感流调度优化图划分

Proceedings of the 1st Workshop on Digital Twin & Edge AI for Industrial IoT Pub Date : 2022-10-17 DOI: 10.1145/3566099.3569003

Shuoi Wang, Jonathan Kua, Jiong Jin, Ambarish Kulkarni, P. Jayaraman, Xianghui Cao

{"title":"Optimal graph partitioning for time-sensitive flow scheduling towards digital twin networks","authors":"Shuoi Wang, Jonathan Kua, Jiong Jin, Ambarish Kulkarni, P. Jayaraman, Xianghui Cao","doi":"10.1145/3566099.3569003","DOIUrl":"https://doi.org/10.1145/3566099.3569003","url":null,"abstract":"The growing maturity of Digital Twin (DT) technology represents a quantum leap towards the realisation of Industry 4.0 and beyond. DT refers to virtual representations (in a virtual space) of physical objects/processes/systems (in a physical space), where information is regularly exchanged between them to enable real-time remote control and monitoring. DT will significantly improve product life-cycles and will transform industries such as smart manufacturing, smart transportation, and so forth. Digital Twin Networks (DTNs) are envisaged to be the norm where multiple DTs are logically connected to their respective physical objects, forming a many-to-many communication relationship. Strict real-time communication for bi-directional data flows is required in DTNs for DTs to accurately reflect the changes in the physical objects, and vice-versa. One potential candidate to achieve real-time data transmission is Time Sensitive Networking (TSN). The IEEE 802.1Q working group has developed a set of TSN standards to facilitate data transmissions that require low-latency, high availability and reliability. In TSN, the Central Network Controller (CNC) computes the schedule of frame transmission. However, the computational time required can exponentially increase as the number of nodes and data flows increases (already typical in industrial environments and will increase exponentially with DTNs). In this paper, we propose a novel technique using multi-level graph partitioning theory with Integer Linear Programming (ILP) to facilitate TSN scheduling. Our results demonstrated significant improvements in computational time and the success rate of scheduled data flows in complex networks where there are up to 100 nodes and 350 data flows.","PeriodicalId":272675,"journal":{"name":"Proceedings of the 1st Workshop on Digital Twin & Edge AI for Industrial IoT","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126296800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Learning to optimize computation offloading performance in multi-access wireless networks 学习优化多址无线网络的计算卸载性能

Proceedings of the 1st Workshop on Digital Twin & Edge AI for Industrial IoT Pub Date : 2022-10-17 DOI: 10.1145/3566099.3569005

Lin Sun, Yangjie Cao, Rui Yin, Celimuge Wu, Yongdong Zhu, Xianfu Chen

{"title":"Learning to optimize computation offloading performance in multi-access wireless networks","authors":"Lin Sun, Yangjie Cao, Rui Yin, Celimuge Wu, Yongdong Zhu, Xianfu Chen","doi":"10.1145/3566099.3569005","DOIUrl":"https://doi.org/10.1145/3566099.3569005","url":null,"abstract":"In this paper, we investigate computation offloading in a multi-access wireless network, which supports both cellular and WiFi connectivity between a mobile user (MU) and the edge server. The MU decides to process an arrived computation task locally at the device or offload it to the edge server for remote execution. The technical challenges of designing a computation offloading policy lie in the network uncertainties due to the MU mobility, the sporadic task arrivals, the spatially distributed WiFi connectivity and the intermittent wireless charging opportunities. Accordingly, we apply a Markov decision process framework to formulate the problem of computation offloading over the infinite discrete time horizon. The objective of the MU is to find a policy to minimize the expected long-term cost. Without the knowledge of network uncertainty statistics, this paper makes the first attempt to exploit the model-free DQNReg, which is built upon a deep Q-network by adding a weighted Q-value to the squared Bellman error, to solve an optimal computation offloading policy. Experiments validate the superior performance from our approach compared to the baselines in terms of average computation offloading cost.","PeriodicalId":272675,"journal":{"name":"Proceedings of the 1st Workshop on Digital Twin & Edge AI for Industrial IoT","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133534280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Space spider: a hyper large scientific infrastructure based on digital twin for the space internet 空间蜘蛛:基于空间互联网数字孪生的超大型科学基础设施

Proceedings of the 1st Workshop on Digital Twin & Edge AI for Industrial IoT Pub Date : 2022-10-17 DOI: 10.1145/3566099.3569007

Jiaqi Li, Lvyang Zhang, Quan Hong, Yang Yu, Lidong Zhai

{"title":"Space spider: a hyper large scientific infrastructure based on digital twin for the space internet","authors":"Jiaqi Li, Lvyang Zhang, Quan Hong, Yang Yu, Lidong Zhai","doi":"10.1145/3566099.3569007","DOIUrl":"https://doi.org/10.1145/3566099.3569007","url":null,"abstract":"With its advantages of low latency and global coverage, the Low Earth Orbit (LEO) satellite constellations can form an effective complement to the terrestrial 5G/6G mobile communication system and provide infrastructure support for broadband access and various services of the Internet. However, due to the spatial particularity of this network across land, sea, air, and other levels, it faces the dilemma of being \"easy to attack\" and \"difficult to defend\". At the same time, with the acceleration of the wave of digital transformation, the space Internet is increasingly facing software supply chain security risks. Currently, there is no security simulation and verification platform for the whole life cycle of the space Internet. Therefore, in the present study, we design a digital twin-based hyper large scientific infrastructure for the space Internet named Space Spider to realize the ground simulation of all elements of the space Internet and establish an attack and defense environment for the space Internet to support core technology verification. In addition, we also proposed Spiderland, an open experimental platform for space Internet applications and security researchers, to conduct simulation and attack-defense experiments.","PeriodicalId":272675,"journal":{"name":"Proceedings of the 1st Workshop on Digital Twin & Edge AI for Industrial IoT","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115518258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Transformer-driven multi-agent deep reinforcement learning based point cloud video transmissions 基于变压器驱动的多智能体深度强化学习的点云视频传输

Proceedings of the 1st Workshop on Digital Twin & Edge AI for Industrial IoT Pub Date : 2022-10-17 DOI: 10.1145/3566099.3569006

Hai Lin, Xianfu Chen

{"title":"Transformer-driven multi-agent deep reinforcement learning based point cloud video transmissions","authors":"Hai Lin, Xianfu Chen","doi":"10.1145/3566099.3569006","DOIUrl":"https://doi.org/10.1145/3566099.3569006","url":null,"abstract":"The point cloud videos, a medium for representing natural content in AR/VR with point clouds, have attracted a wide range of attention for its characteristics and have the potential to be the next generation of video technology. Given the high data volume, the point cloud video raises the challenge of intelligent transmission and resource scheduling in multi-user scenarios under time-varying system conditions. In this paper, we propose a multi-agent deep reinforcement learning (DRL) approach to optimize the expected long-term multi-user QoE and adopt a Field of View (FoV) prediction model with Transformer for high-accuracy FoV prediction. Over the time horizon, the proposed approach learns to select the tiles of the corresponding video in accordance with a proposed well-defined QoE model capable of quantifying users' satisfaction for transmissions in an iterative way. Under various settings, extensive numerical experiments based on real throughput data traces and different computation capabilities data demonstrate that the proposed approach is effective for long-term multi-agent point cloud video transmissions.","PeriodicalId":272675,"journal":{"name":"Proceedings of the 1st Workshop on Digital Twin & Edge AI for Industrial IoT","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117278446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive mobile VR content delivery for industrial 5.0 工业5.0的自适应移动VR内容交付

Proceedings of the 1st Workshop on Digital Twin & Edge AI for Industrial IoT Pub Date : 2022-10-17 DOI: 10.1145/3566099.3569002

Mushu Li, Jie Ying Gao, Conghao Zhou, X. Shen, W. Zhuang

引用次数: 2

Proceedings of the 1st Workshop on Digital Twin & Edge AI for Industrial IoT 第一届工业物联网数字孪生与边缘人工智能研讨会论文集

Proceedings of the 1st Workshop on Digital Twin & Edge AI for Industrial IoT Pub Date : 1900-01-01 DOI: 10.1145/3566099

引用次数: 0