Artificial intelligence (AI), owing to recent breakthroughs in deep learning, has revolutionized applications and services in almost all technology domains including aerospace. AI and deep learning rely on huge amounts of training data that are mostly generated at the network edge by Internet of Things (IoT) devices and sensors. Bringing the sensed data from the edge of a distributed network to a centralized cloud is often infeasible because of the massive data volume, limited network bandwidth, and real-time application constraints. Consequently, there is a desire to push AI frontiers to the network edge toward utilizing the enormous amount of data generated by IoT devices near the data source. The merger of edge computing and AI has engendered a new discipline, that is, AI at the edge or edge intelligence. To help AI make sense of gigantic data at the network edge, data fusion is of paramount significance and goes hand in hand with AI. This article focuses on data fusion and AI at the edge. In this article, we propose a framework for data fusion and AI processing at the edge. We then provide a comparative discussion of different data fusion and AI models and architectures. We discuss multiple levels of fusion and different types of AI, and how different types of AI align with different levels of fusion. We then highlight the benefits of combining data fusion with AI at the edge. The methods of AI and data fusion at the edge detailed in this article are applicable to many application domains including aerospace systems. We evaluate the effectiveness of combined data fusion and AI at the edge using convolutional neural network models and multiple hardware platforms suitable for edge computing. Experimental results reveal that combining AI with data fusion can impart a speedup of 9.8× while reducing energy consumption up to 88.5% over AI without data fusion. Furthermore, results demonstrate that data fusion either maintains or improves the accuracy of AI in most cases. For our experiments, data fusion imparts a maximum improvement of 15.8% in accuracy to AI.

中文翻译：

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边缘的人工智能和数据融合


由于深度学习最近取得的突破，人工智能（AI）已经彻底改变了包括航空航天在内的几乎所有技术领域的应用和服务。人工智能和深度学习依赖于大量训练数据，这些数据主要由物联网 (IoT) 设备和传感器在网络边缘生成。由于海量的数据量、有限的网络带宽和实时应用程序的限制，将感知的数据从分布式网络边缘带到集中式云端通常是不可行的。因此，人们希望将人工智能前沿推向网络边缘，以利用数据源附近物联网设备生成的大量数据。边缘计算与人工智能的融合催生了一门新学科，即边缘人工智能或边缘智能。为了帮助人工智能理解网络边缘的海量数据，数据融合至关重要，并且与人工智能齐头并进。本文重点讨论边缘数据融合和人工智能。在本文中，我们提出了一个边缘数据融合和人工智能处理的框架。然后，我们对不同的数据融合和人工智能模型和架构进行比较讨论。我们讨论多层次的融合和不同类型的人工智能，以及不同类型的人工智能如何与不同层次的融合相结合。然后，我们强调将数据融合与边缘人工智能相结合的好处。本文详细介绍的人工智能和边缘数据融合方法适用于包括航空航天系统在内的许多应用领域。我们使用卷积神经网络模型和适合边缘计算的多个硬件平台来评估边缘数据融合和人工智能相结合的有效性。 实验结果表明，与不使用数据融合的人工智能相比，将人工智能与数据融合相结合可实现 9.8 倍的加速，同时降低高达 88.5% 的能耗。此外，结果表明，在大多数情况下，数据融合可以维持或提高人工智能的准确性。在我们的实验中，数据融合使 AI 的准确率最大提高了 15.8%。