Enabling data processing at the network edge, as close to the actual source of data as possible, is a challenging, yet realistic goal to be achieved by the Internet of Things (IoT), which still primarily relies on the Cloud for data processing. By further extending the Fog and Edge computing principles, recent research advancements enabled aggregation of computing resources from multiple edge devices to support data-intensive task processing using Big Data clustering middleware. The use of these existing solutions, however, is hindered by the heterogeneous, dynamic, mobile, resource-constrained, and time-critical nature of IoT ecosystems. More specifically, a particularly challenging goal is to discover, select, and cluster suitable edge devices – on the one hand, and decompose and allocate data-intensive tasks with respect to discovered resources – on the other. To address this challenge, this paper introduces a novel decentralized architecture for clustering heterogeneous edge devices and executing data-intensive IoT workflows. The proposed approach first breaks down a complex workflow into simpler tasks, then discovers and selects suitable edge devices, and finally allocates the tasks to the selected nodes, connecting them to recompose the original workflow. The proposed approach benefits from an intelligent mapping algorithm that takes into account available cluster resources and processing demands to efficiently allocate fine-grained tasks to selected nodes. To support the clusterisation process, the proposed solution relies on a unified semantic knowledge base that provides a common vocabulary of terms for modelling task requirements and edge device properties, as well as enables automated task grouping and match-making for device discovery and selection, using built-in reasoning capabilities.

中文翻译：

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在集群边缘计算中自动分配物联网数据密集型应用程序

在网络边缘启用数据处理，尽可能接近实际数据源，是物联网 (IoT) 需要实现的具有挑战性但现实的目标，物联网仍然主要依赖云进行数据处理。通过进一步扩展雾和边缘计算原理，最近的研究进展使来自多个边缘设备的计算资源能够聚合，以支持使用大数据集群中间件的数据密集型任务处理。然而，这些现有解决方案的使用受到物联网生态系统异构、动态、移动、资源受限和时间紧迫的性质的阻碍。更具体地说，一个特别具有挑战性的目标是发现、选择和集群合适的边缘设备——一方面，另一方面，分解和分配与已发现资源相关的数据密集型任务。为了应对这一挑战，本文介绍了一种新颖的分散式架构，用于集群异构边缘设备和执行数据密集型物联网工作流。所提出的方法首先将复杂的工作流分解为更简单的任务，然后发现并选择合适的边缘设备，最后将任务分配给选定的节点，将它们连接起来重组原始工作流。所提出的方法受益于智能映射算法，该算法考虑了可用的集群资源和处理需求，以将细粒度的任务有效地分配给选定的节点。为了支持聚类过程，