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Towards Large-scale Material-integrated Computing: Self-Adaptive Materials and Agents

机译:走向大型材料集成计算:自适应材料和代理

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In the past decades there was an exponential growth of computer networks and computing devices, connecting computers with a size in the m~3 range. The Intemet-of-Things (IoT) emerges connecting everything, demanding for new distributed computing and communication approaches. Currently, the IoT connects devices with a size in the cm~3 range. But new technologies enable the integration of computing in materials and technical structures with sensor and actor networks connecting devices in the mm~3 range. This work investigates issues in large-scale computer networks related to the deployment of low- and very-low resource miniaturized nodes integrated within materials. These networks operate under harsh conditions with possibility of technical failures requiring robustness. Despite sensor networks used for structural monitoring, self-adaptive materials can profit from selforganizing and autonomous distributed data processing using Multi-agent systems, demonstrated in this paper. Self-adaptive materials are able to adapt the material or mechanical structure properties based on their environmental interaction (load/stress) to minimize the risk of overloading. A structure that could change its local properties in service based on the identified loading situation could thus potentially raise additional weight saving potentials and thus supporting lightweight design, and in consequence, sustainability.
机译:在过去的几十年中,计算机网络和计算设备的指数增长,将具有尺寸的计算机连接在M〜3范围内。事情的互动(物联网)出现了连接所有内容,要求新的分布式计算和通信方法。目前,IOT将具有大小的设备连接在CM〜3范围内。但是,新技术使得在材料和技术结构中的集成与传感器和演员网络连接在MM〜3范围内的actor网络。这项工作调查了与部署集成在材料内的低音和非常低的资源小型化节点相关的大型计算机网络中的问题。这些网络在恶劣的条件下运行,具有需要稳健性的技术失败的可能性。尽管用于结构监测的传感器网络,但自适应材料可以利用使用多种子体系统的自动化和自主分布式数据处理,本文证明。自适应材料能够基于它们的环境相互作用(负载/应力)来调节材料或机械结构性能,以最大限度地降低过载的风险。因此,可以基于所识别的装载情况改变其本地性质的结构可能会潜在地提高额外的重量节省电位,从而支持轻质设计,并因此支持可持续性。

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