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Collaborative learning-based industrial IoT API recommendation for software-defined devices : The implicit knowledge discovery perspective
Gao, Honghao Qin, Xi Barroso, Ramón J. Durán Hussain, Walayat Xu, Yueshen Yin, Yuyu
Gao, Honghao
Qin, Xi
Barroso, Ramón J. Durán
Hussain, Walayat
Xu, Yueshen
Yin, Yuyu
Abstract
The industrial Internet of things (IIoT), a new computing mode in Industry 4.0, is deployed to connect IoT devices and use communication technology to respond to control commands and handle industrial data. IIoT is typically employed to improve the efficiency of computing and sensing and can be used in many scenarios, such as intelligent manufacturing and video surveillance. To build an IIoT system, we need a collection of software to manage and monitor each system component when there are large-scale devices. Application programming interface (API) is an effective way to invoke public services provided by different platforms. Developers can invoke different APIs to operate IoT devices without knowing the implementation process. We can design a workflow to configure how and when to invoke target APIs. Thus, APIs are a powerful tool for rapidly developing industrial systems. However, the increasing number of APIs exacerbates the problem of finding suitable APIs. Current related recommendation methods have defects. For example, most existing methods focus on the relation between users and APIs but neglect the valuable relations among the users or APIs themselves. To address these problems, this article studies implicit knowledge in IIoT by using collaborative learning techniques. Considering the increased dimensions and dynamics of IoT devices, we explore the possible relationships between users and between APIs. We enhance the matrix factorization (MF) model with the mined implicit knowledge that are implicit relationships on both sides. We build an ensemble model by using all implicit knowledge. We conduct experiments on a collected real-world dataset and simulate industrial system scenarios. The experimental results verify the effectiveness and superiority of the proposed models.
Keywords
API recommendation, collaborative learning, implicit relationship mining, industrial internet of things, matrix factorization
Date
2022
Type
Journal article
Journal
IEEE Transactions on Emerging Topics in Computational Intelligence
Book
Volume
6
Issue
1
Page Range
66-76
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled