The combination of Internet of Things (IoT) and Edge Computing (EC) can assist in the delivery of novel applications that will facilitate end-users’ activities. Data collected by numerous devices present in the IoT infrastructure can be hosted into a set of EC nodes becoming the subject of processing tasks for the provision of analytics. Analytics are derived as the result of various queries defined by end-users or applications. Such queries can be executed in the available EC nodes to limit the latency in the provision of responses. In this article, we propose a meta-ensemble learning scheme that supports the decision making for the allocation of queries to the appropriate EC nodes. Our learning model decides over queries’ and nodes’ characteristics. We provide the description of a matching process between queries and nodes after concluding the contextual information for each envisioned characteristic adopted in our meta-ensemble scheme. We rely on widely known ensemble models, combine them, and offer an additional processing layer to increase the performance. The aim is to result a subset of EC nodes that will host each incoming query. Apart from the description of the proposed model, we report on its evaluation and the corresponding results. Through a large set of experiments and a numerical analysis, we aim at revealing the pros and cons of the proposed scheme.

中文翻译：

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基于集成模型的以边缘为中心的智能查询分配方案

物联网 (IoT) 和边缘计算 (EC) 的结合可以帮助交付新的应用程序，从而促进最终用户的活动。物联网基础设施中存在的众多设备收集的数据可以托管到一组 EC 节点中，成为提供分析的处理任务的主题。分析是最终用户或应用程序定义的各种查询的结果。此类查询可以在可用的 EC 节点中执行，以限制提供响应的延迟。在本文中，我们提出了一种元集成学习方案，该方案支持将查询分配到适当的 EC 节点的决策。我们的学习模型决定了查询和节点的特征。在为我们的元集成方案中采用的每个设想特征得出上下文信息之后，我们提供了查询和节点之间匹配过程的描述。我们依赖广为人知的集成模型，将它们组合起来，并提供一个额外的处理层来提高性能。目的是生成一个 EC 节点子集，用于托管每个传入查询。除了对所提出模型的描述外，我们还报告了它的评估和相应的结果。通过大量实验和数值分析，我们旨在揭示所提出方案的优缺点。目的是生成一个 EC 节点子集，用于托管每个传入查询。除了对所提出模型的描述外，我们还报告了它的评估和相应的结果。通过大量实验和数值分析，我们旨在揭示所提出方案的优缺点。目的是生成一个 EC 节点子集，用于托管每个传入查询。除了对所提出模型的描述外，我们还报告了它的评估和相应的结果。通过大量实验和数值分析，我们旨在揭示所提出方案的优缺点。