Research community has already revealed the challenges of data processing when performed at the Cloud that may affect the performance of any desired application. The main challenge is the increased latency observed when the data should ‘travel’ to the Cloud from the location they are collected and the waiting time for getting the final response. In an Internet of Things (IoT) scenario, this time could be critical for supporting real time applications. A solution to the discussed problem is the adoption of an Edge Computing (EC) approach where data can be processed close to their collection point. IoT devices could report data to a number of edge nodes that behave as distributed data repositories having the capability of processing them and producing analytics. Analytics should match the requirements of queries defined by end users or applications with the collected data and the characteristics of every edge node. However, when a query is defined, we should identify the appropriate edge node(s) to process it. In this paper, we propose an uncertainty management model to efficiently allocate every incoming query to the available edge nodes. Our scheme adopts the principles of the Fuzzy Logic (FL) theory and provides a decision making mechanism for the entity having the responsibility of the envisioned allocations. We combine the proposed uncertainty management scheme with a machine learning model based on a Support Vector Machine (SVM) to enhance the FL reasoning. Our aim is to manage all the hidden aspects of the problem combining two different technologies with different orientations. We also propose a methodology for the automated generation of the Footprint of Uncertainty (FoU) of membership functions involved in our interval Type-2 FL model. Our experimental evaluation aims at revealing the pros and cons of our mechanism presenting the results of extensive simulations adopting datasets found in the literature and a comparative analysis with other efforts in the domain.

研究社区已经揭示了在云中执行数据处理时遇到的挑战，这些挑战可能会影响任何所需应用程序的性能。主要挑战是，当数据应从收集位置“移动”到云中时，观察到的延迟增加以及获得最终响应的等待时间。在物联网（IoT）场景中，这段时间对于支持实时应用至关重要。解决此问题的一种方法是采用边缘计算（EC）方法，其中可以在数据的收集点附近对其进行处理。物联网设备可以将数据报告给许多边缘节点，这些边缘节点充当分布式数据存储库，具有处理它们和生成分析的能力。分析应将最终用户或应用程序定义的查询需求与收集的数据和每个边缘节点的特征相匹配。但是，定义查询时，我们应该确定适当的边缘节点来处理它。在本文中，我们提出了一种不确定性管理模型，可以有效地将每个传入查询分配给可用的边缘节点。我们的方案采用了模糊逻辑（FL）理论的原理，并为负责预期分配的实体提供了一种决策机制。我们将提出的不确定性管理方案与基于支持向量机（SVM）的机器学习模型相结合，以增强FL推理能力。我们的目标是结合具有不同方向的两种不同技术来管理问题的所有隐藏方面。我们还提出了一种方法，用于自动生成区间2型FL模型所涉及的隶属函数的不确定性足迹（FoU）。我们的实验评估旨在揭示我们机制的利弊，提出采用文献中发现的数据集进行广泛模拟的结果，并在该领域的其他工作中进行比较分析。