Artificial Intelligence and Data Fusion at the Edge

Arslan Munir, Erik Blasch, Jisu Kwon, Joonho Kong, Alexander Aved

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number9475883
Pages (from-to)62-78
Number of pages17
JournalIEEE Aerospace and Electronic Systems Magazine
Issue number7
StatePublished - Jul 1 2021

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science
  • Electrical and Electronic Engineering


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