The heterogeneity of the Internet-of-things (IoT) network can be exploited as a dynamic computational resource environment for many devices lacking computational capabilities. A smart mechanism for allocating edge and mobile computers to match the need of devices requesting external computational resources is developed. In this paper, we employ the concept of Social IoT and machine learning to downgrade the complexity of allocating appropriate edge computers. We propose a framework that detects different communities of devices in SIoT enclosing trustworthy peers having strong social relations. Afterwards, we train a machine learning algorithm, considering multiple computational and non-computational features of the requester as well as the edge computers, to predict the total time needed to process the required task by the potential candidates belonging to the same community of the requester. By applying it to a real-world data set, we observe that the proposed framework provides encouraging results for mobile computer allocation.

中文翻译：

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社交物联网边缘计算的计算资源分配

物联网 (IoT) 网络的异构性可以被用作许多缺乏计算能力的设备的动态计算资源环境。开发了一种智能机制，用于分配边缘计算机和移动计算机以满足请求外部计算资源的设备的需求。在本文中，我们采用社交物联网和机器学习的概念来降低分配适当边缘计算机的复杂性。我们提出了一个框架，用于检测 SIoT 中的不同设备社区，其中包含具有强大社会关系的可信赖对等方。之后，我们训练机器学习算法，考虑请求者和边缘计算机的多个计算和非计算特征，预测属于请求者同一社区的潜在候选人处理所需任务所需的总时间。通过将其应用于现实世界的数据集，我们观察到所提出的框架为移动计算机分配提供了令人鼓舞的结果。