Sodhro, A. H., Gurtov, A., Zahid, N., Pirbhulal, S., Wang, L., Rahman, M. M. U., Imran, M. A. and Abbasi, Q. H. (2021) Towards convergence of AI and IoT for energy efficient communication in smart homes. IEEE Internet of Things Journal, 8(12), pp. 9664-9671. (doi: 10.1109/JIOT.2020.3023667)
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222671.pdf - Accepted Version 1MB |
Abstract
The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) promotes the energy efficient communication in smart homes. Quality of Service (QoS) optimization during video streaming through wireless micro medical devices (WMMD) in smart healthcare homes is the main purpose of this research. This paper contributes in four distinct ways. First, to propose a novel Lazy Video Transmission Algorithm (LVTA). Second, a novel Video Transmission Rate Control Algorithm (VTRCA) is proposed. Third, a novel cloud-based video transmission framework is developed. Fourth, the relationship between buffer size and performance indicators i.e., peak-to-mean ratio (PMR), energy (i.e., encoding and transmission) and standard deviation is investigated while comparing the LVTA, VTRCA, and Baseline approaches. Experimental results demonstrate that the reduction in encoding (32%, 35.4%) and transmission (37%, 39%) energy drains, PMR (5, 4), and standard deviation (3dB, 4dB) for VTRCA and LVTA, respectively, is greater than that obtained by Baseline during video streaming through WMMD.
Item Type: | Articles |
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Additional Information: | This work is also supported by CENIIT project 17.01, Computer and Information Science department, Linkop- ing University, Linkoping, Sweden and in part by CAS President’s International Fellowship Initiative Project (2020VBC0002) China. |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Abbasi, Professor Qammer and Imran, Professor Muhammad |
Authors: | Sodhro, A. H., Gurtov, A., Zahid, N., Pirbhulal, S., Wang, L., Rahman, M. M. U., Imran, M. A., and Abbasi, Q. H. |
College/School: | College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering College of Science and Engineering > School of Engineering > Systems Power and Energy |
Journal Name: | IEEE Internet of Things Journal |
Publisher: | IEEE |
ISSN: | 2327-4662 |
ISSN (Online): | 2327-4662 |
Published Online: | 14 September 2020 |
Copyright Holders: | Copyright © 2020 IEEE |
First Published: | First published in IEEE Internet of Things 8(12): 9664-9671 |
Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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