A Big Data Layered Architecture and Functional Units for the Multimedia Internet of Things

Kah Phooi Seng; Li-Minn Ang

doi:10.1109/TMSCS.2018.2886843

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A Big Data Layered Architecture and Functional Units for the Multimedia Internet of Things

Journal article

Peer reviewed

A Big Data Layered Architecture and Functional Units for the Multimedia Internet of Things

Kah Phooi Seng and Li-Minn Ang

IEEE Transactions on Multi-Scale Computing Systems, Vol.4(4), pp.500-512

2018

DOI: https://doi.org/10.1109/TMSCS.2018.2886843

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Abstract

Decision making

Software architecture

big data

IoT

linear discriminant analysis

multimodal

principal component analysis

unstructured data

Data mining

Discriminant analysis

Modal analysis

Multimedia services

Decision making unit

Layered archit

The escalating growth of multimedia content in Internet of Things (IoT) applications leads to a huge volume of unstructured data being generated. Unstructured Big data has no particular format or structure and can be in any form such as text, audio, images, and video. Furthermore, current IoT systems cannot successfully realize the notion of having ubiquitous connectivity of everything if they are not capable to include 'multimedia things'. In this paper, we address two issues by proposing a new architecture for the Multimedia Internet of Things (MIoT) with Big multimodal computation layer. We first introduce MIoT as a novel paradigm in which smart heterogeneous multimedia things can interact and cooperate with one another and with other things connected to the Internet to facilitate multimedia-based services and applications that are globally available to the users. The MIoT architecture consists of six layers. The computation layer is specially designed for Big multimodal analytics. This layer has four important functional units: Data Centralized Unit, Multimodal Data Aggregation Unit, Multimodal Data Divide & Conquer Computation Unit, and Fusion & Decision Making Unit. A novel and highly scalable technique called the Divide & Conquer Principal Component Analysis (DC-PCA) for feature extraction in the divide and conquer mechanism is proposed to be used together with the Divide & Conquer Linear Discriminant Analysis (DC-LDA) for multimodal Big data analytics. Experiments are conducted to confirm the good performance of these techniques in the functional units of the Divide & Conquer computational mechanisms. The final section of the paper gives application on a camera sensing IoT platform and real-world data analytics on multicore architecture implementations.

Details

Title: A Big Data Layered Architecture and Functional Units for the Multimedia Internet of Things
Authors: Kah Phooi Seng (Author) - UNSW Australia
Li-Minn Ang (Author) - Griffith University
Publication details: IEEE Transactions on Multi-Scale Computing Systems, Vol.4(4), pp.500-512
Publisher: Institute of Electrical and Electronics Engineers Inc.
Date published: 2018
DOI: 10.1109/TMSCS.2018.2886843
ISSN: 2332-7766; 2332-7766
Organisation Unit: University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
Language: English
Record Identifier: 99513905402621
Output Type: Journal article

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Collaboration types: Domestic collaboration
Web Of Science research areas: Computer Science, Information Systems

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