arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2206.01239cs.NIcs.DC
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在机会网络中传播语义知识和内容的认知方法的设计和评估

Design and evaluation of a cognitive approach for disseminating semantic knowledge and content in opportunistic networks

Matteo Mordacchini, Lorenzo Valerio, Marco Conti, Andrea Passarella

摘要:

在网络-物理融合场景中,信息在物理世界和网络世界之间无缝流动。在这里,用户的移动设备表示用户处理所获取的信息并执行动作的自然桥梁。在这种情况下,大量的数据需要新的,自主的和轻量级的数据过滤解决方案,只有相关的信息最终呈现给用户。此外,在许多现实世界的场景中,数据没有被分类在预定义的主题中,但它通常伴随着可能描述用户兴趣的语义描述。在这些复杂的条件下,用户设备应该自主地变得不仅知道网络中数据的存在,而且还知道它们的语义描述和它们之间的相关性。为了解决这些问题,我们提出了一套算法的知识和数据传播的机会主义网络,基于简单而非常有效的模型(称为认知的认知)来自认知科学。我们将展示如何利用它们来传播语义数据和相应的数据项。我们提供了一个彻底的性能分析,在各种不同的条件下比较我们的结果与非认知解决方案。仿真结果表明,我们的解决方案对一个更有效的语义知识获取和表示,更适合的内容获取的优越性能。

英文摘要:

In cyber-physical convergence scenarios information flows seamlessly between the physical and the cyber worlds. Here, users' mobile devices represent a natural bridge through which users process acquired information and perform actions. The sheer amount of data available in this context calls for novel, autonomous and lightweight data-filtering solutions, where only relevant information is finally presented to users. Moreover, in many real-world scenarios data is not categorised in predefined topics, but it is generally accompanied by semantic descriptions possibly describing users' interests. In these complex conditions, user devices should autonomously become aware not only of the existence of data in the network, but also of their semantic descriptions and correlations between them. To tackle these issues, we present a set of algorithms for knowledge and data dissemination in opportunistic networks, based on simple and very effective models (called cognitive heuristics) coming from cognitive sciences. We show how to exploit them to disseminate both semantic data and the corresponding data items. We provide a thorough performance analysis, under various different conditions comparing our results against non-cognitive solutions. Simulation results demonstrate the superior performance of our solution towards a more effective semantic knowledge acquisition and representation, and a more tailored content acquisition.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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