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Semantic data exchange between collaborative robots in fog environment: Can CoAP be a choice?

机译:雾环境下协作机器人之间的语义数据交换:CoAP是否可以选择?

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The transition of Internet from human-centric exchanges to the Internet of Things has posed several interesting challenges regarding the communication model and data exchange pattern. The emergence of edge/fog computing as a compute model has further highlighted the need of a uniform model to cater to the inherent heterogeneity of participating devices. In several domains, the devices may range from standard compute and communication devices to gateways/switches, as well as robots/drones and other types of autonomous entities. In scenarios like disaster management, warehouse automation, surveillance etc. use of robots/drones/AGVs/UAVs is steadily increasing in order to minimise potentially hazardous human intervention. Optimality of data exchange and further processing is a desired feature in such scenarios, especially in outdoor disaster situations where availability of the back-end cloud infrastructure can not be guaranteed. In this paper, we demonstrate a real life robotic data exchange model based on Resource Description Framework (RDF). We further perform a comparative analysis of network performance between TCP in Robot Operating System (ROS) and block-wise extension to Constrained Application Layer Protocol (CoAP) for bidirectional exchange of such RDF data in a disaster situation where robotic entities accomplish search and rescue missions in collaboration with fog devices. The causal analysis presented in this paper puts a stepping stone for potential future enhancement of CoAP.
机译:互联网从以人为本的交换向物联网的过渡对通信模型和数据交换模式提出了一些有趣的挑战。边缘/雾计算作为计算模型的出现进一步突出了需要统一模型来满足参与设备固有的异构性的需求。在几个领域中,设备的范围可能从标准的计算和通信设备到网关/交换机,以及机器人/无人机和其他类型的自治实体。在诸如灾难管理,仓库自动化,监视等场景中,机器人/无人机/ AGV / UAV的使用正在稳步增长,以最大程度地减少潜在的人为干预。在这种情况下,尤其是在无法保证后端云基础架构可用性的室外灾难情况下,数据交换和进一步处理的优化是理想的功能。在本文中,我们演示了一个基于资源描述框架(RDF)的现实生活中的机器人数据交换模型。我们还对机器人操作系统(ROS)中的TCP与受约束的应用层协议(CoAP)的逐块扩展之间的网络性能进行了比较分析,以便在机器人实体完成搜索和救援任务的灾难情况下双向交换此类RDF数据与雾装置合作。本文提出的因果分析为将来可能提高CoAP打下了基础。

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