Neural Architecture Search for Robust Networks in 6G-enabled Massive IoT Domain

Ke Wang, Peng Xu, Chien Ming Chen, Saru Kumari, Mohammad Shojafar, Mamoun Alazab

    Research output: Contribution to journalArticlepeer-review


    6G technology enables AI-based massive IoT to manage network resources and data with ultra high speed, responsive network and wide coverage. However, many artificial intelligence-enabled internet of things (AIoT) systems are vulnerable to adversarial example attacks. Therefore, designing robust deep learning models that can be deployed on resource-constrained devices has become an important research topic in the field of 6G-enabled AIoT. In this paper, we propose a method for automatically searching for robust and efficient neural network structures for AIoT systems. By introducing a skip connection structure, a feature map with reduced front-end influence can be used for calculations during the classification process. Additionally, a novel type of of dense connected search space is proposed. By relaxing this space, it is possible to search for network structures efficiently. In addition, combined with adversarial training and model delay constraints, we propose a multi-objective gradient optimization method to realize the automatic searching of network structures. Experimental results demonstrate that our method is effective for AIoT systems and superior to state-of-the-art neural architecture search algorithms.

    Original languageEnglish
    Pages (from-to)5332-5339
    Number of pages8
    JournalIEEE Internet of Things Journal
    Issue number7
    Early online dateNov 2020
    Publication statusPublished - 1 Apr 2021


    Dive into the research topics of 'Neural Architecture Search for Robust Networks in 6G-enabled Massive IoT Domain'. Together they form a unique fingerprint.

    Cite this