The combination of the infrastructure provided by the Internet of Things (IoT) with numerous processing nodes present at the Edge Computing (EC) ecosystem opens up new pathways to support intelligent applications. Such applications can be provided upon humongous volumes of data collected by IoT devices being transferred to the edge nodes through the network. Various processing activities can be performed on the discussed data and multiple collaborative opportunities between EC nodes can facilitate the execution of the desired tasks. In order to support an effective interaction between edge nodes, the knowledge about the geographically distributed data should be shared. Obviously, the migration of large amounts of data will harm the stability of the network stability and its performance. In this paper, we recommend the exchange of data synopses than real data between EC nodes to provide them with the necessary knowledge about peer nodes owning similar data. This knowledge can be valuable when considering decisions such as data/service migration and tasks offloading. We describe an continuous reasoning model that builds a temporal similarity map of the available datasets to get nodes understanding the evolution of data in their peers. We support the proposed decision making mechanism through an intelligent similarity extraction scheme based on an unsupervised machine learning model, and, at the same time, combine it with a statistical measure that represents the trend of the so-called discrepancy quantum. Our model can reveal the differences in the exchanged synopses and provide a datasets similarity map which becomes the appropriate knowledge base to support the desired processing activities. We present the problem under consideration and suggest a solution for that, while, at the same time, we reveal its advantages and disadvantages through a large number of experiments.

中文翻译：

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边缘数据提要的主动管理方案

物联网 (IoT) 提供的基础设施与边缘计算 (EC) 生态系统中的众多处理节点相结合，开辟了支持智能应用程序的新途径。此类应用程序可以在 IoT 设备收集的大量数据通过网络传输到边缘节点时提供。可以对讨论的数据执行各种处理活动，并且 EC 节点之间的多个协作机会可以促进所需任务的执行。为了支持边缘节点之间的有效交互，应该共享有关地理分布数据的知识。显然，大量数据的迁移会损​​害网络稳定性及其性能的稳定性。在本文中，我们建议在 EC 节点之间交换数据概要而不是真实数据，以便为它们提供有关拥有相似数据的对等节点的必要知识。在考虑数据/服务迁移和任务卸载等决策时，这些知识可能很有价值。我们描述了一个连续推理模型，该模型构建可用数据集的时间相似性图，以使节点了解其对等数据的演变。我们通过基于无监督机器学习模型的智能相似性提取方案支持所提出的决策机制，同时将其与代表所谓差异量子趋势的统计度量相结合。我们的模型可以揭示交换概要中的差异，并提供数据集相似性图，该图成为支持所需处理活动的适当知识库。我们提出了正在考虑的问题并提出了解决方案，同时我们通过大量实验揭示了其优缺点。