Context-aware wireless connectivity and processing unit optimization for IoT networks

Ozturk, M., Abubakar, A. I. , Rais, R. N. B., Jaber, M., Hussain, S. and Imran, M. A. (2022) Context-aware wireless connectivity and processing unit optimization for IoT networks. IEEE Internet of Things Journal, 9(17), pp. 16028-16043. (doi: 10.1109/JIOT.2022.3152381)

[img] Text
264972.pdf - Accepted Version



A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the best connectivity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by jointly optimizing energy consumption, response-time, security, and monetary cost. The proposed scheme employs a reinforcement learning algorithm, and manages to achieve significant gains compared to deterministic solutions. In particular, the requirements of IoT devices in terms of response-time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy. The results obtained show that only our method is able to meet the holistic multi-objective optimization criteria, albeit, the benchmark approaches may achieve better results on a particular metric at the cost of failing to reach the other targets. Thus, the proposed approach is a device-centric and context-aware solution that accounts for the monetary and battery constraints.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Ozturk, Mr Metin and Abubakar, Mr Attai and Imran, Professor Muhammad and Hussain, Dr Sajjad
Authors: Ozturk, M., Abubakar, A. I., Rais, R. N. B., Jaber, M., Hussain, S., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
Journal Name:IEEE Internet of Things Journal
ISSN (Online):2327-4662
Published Online:17 February 2022
Copyright Holders:Copyright © 2021 IEEE
First Published:First published in IEEE Internet of Things Journal 9(17): 16028-16043
Publisher Policy:Reproduced in accordance with the publisher copyright policy

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300725Distributed Autonomous Resilient Emergency Management System (DARE)Muhammad ImranEngineering and Physical Sciences Research Council (EPSRC)EP/P028764/1ENG - Systems Power & Energy