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Published VersionCC BY V4.0, Open Access
Journal article
Biometrics-based Internet of Things and Big data design framework
Mathematical Biosciences and Engineering, Vol.18(4), pp.4461-4476
2021
Abstract
Application Specific Internet of Things (ASIoTs) has recently been proposed to address specific requirements for IoT. The objective of this paper is to serve as a framework for the design of ASIoTs using biometrics as the application. This paper provides comprehensive discussions for an ASIoT architecture considering the requirements for biometrics-based security, multimedia content and Big data applications. A comprehensive architecture for Biometrics-based IoT (BiometricIoT) and Big data applications needs to address three challenges: 1) IoT devices are hardware-constrained and cannot afford resource-demanding cryptographic protocols; 2) Biometrics devices introduce multimedia data content due to different biometric traits; and 3) The rapid growth of biometrics-based IoT devices and content creates large amounts of data for computational processing. The proposed BiometricIoT architecture consists of seven layers which have been designed to handle the challenges for biometrics applications and decision making. The latter part of the paper gives discussions for design factors for the BiometricIoT from four perspectives: 1) parallel divide-and-conquer (D&C) computation; 2) computational complexity; 3) device security; and 4) algorithm efficacies. Experimental results are given to validate the effectiveness of the D&C approach. The paper motivates the further research towards the research and development of ASIoTs for biometrics applications.
Details
- Title
- Biometrics-based Internet of Things and Big data design framework
- Authors
- Kenneth Li-minn Ang (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringKah Phooi Seng (Author) - UNSW Australia
- Publication details
- Mathematical Biosciences and Engineering, Vol.18(4), pp.4461-4476
- Publisher
- AIMS Press
- Date published
- 2021
- DOI
- 10.3934/mbe.2021226
- ISSN
- 1551-0018
- Copyright note
- © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 99548408202621
- Output Type
- Journal article
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