On-Demand Sensing and Wireless Power Transfer for Self-Sustainable Industrial Internet of Things Networks

Waleed Ejaz; Muhammad Naeem; Sherali Zeadally

doi:10.1109/TII.2020.3025510

On-Demand Sensing and Wireless Power Transfer for Self-Sustainable Industrial Internet of Things Networks

Waleed Ejaz, Muhammad Naeem, Sherali Zeadally

School of Information Science

Research output: Contribution to journal › Article › peer-review

28 Scopus citations

Abstract

On-demand data sensing and wireless power transfer (WPT) can provide sustainability and robust operations in large-scale industrial Internet of Things (IoT) networks. The efficiency of on-demand data collection and WPT can be increased by efficient scheduling of IoT nodes and dedicated energy transmitters respectively. In this article, we propose an energy-aware mode switching strategy to enable IoT nodes to perform either on-demand sensing or dedicated WPT. For on-demand sensing, we propose an IoT node scheduling scheme to maximize the utility of the IoT nodes comprising residual energy and energy required for sensing operation while considering the reliability of sensing tasks. For WPT, we propose an energy transmitter scheduling scheme for IoT nodes to minimize the cost of charging while keeping IoT nodes sufficiently charged. The simulation results for IoT node scheduling demonstrate that less than 50% IoT nodes need to be activated in all scenarios to complete the tasks. The proposed energy transmitter scheduling scheme shows that less than 60% energy transmitters should be scheduled in all the scenarios which results in significant energy reduction in the overall system.

Original language	English
Article number	9204414
Pages (from-to)	7075-7084
Number of pages	10
Journal	IEEE Transactions on Industrial Informatics
Volume	17
Issue number	10
DOIs	https://doi.org/10.1109/TII.2020.3025510
State	Published - Oct 2021

Bibliographical note

Publisher Copyright:
© 2005-2012 IEEE.

Funding

Manuscript received June 18, 2020; revised September 3, 2020; accepted September 15, 2020. Date of publication September 22, 2020; date of current version June 30, 2021. This work was supported by the Natural Sciences, and Engineering Research Council of Canada (NSERC). Paper no. TII-20-2948. (Corresponding author: Waleed Ejaz.) Waleed Ejaz is with the Department of Electrical Engineering, Lakehead University, Barrie, ON P7B 5E1, Canada (e-mail: [email protected]).

Funders	Funder number
Natural Sciences and Engineering Research Council of Canada	TII-20-2948

Keywords

Internet of Things (IoT)
mobile edge computing (MEC)
on-demand sensing
wireless power transfer (WPT)

ASJC Scopus subject areas

Control and Systems Engineering
Information Systems
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TII.2020.3025510

Cite this

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title = "On-Demand Sensing and Wireless Power Transfer for Self-Sustainable Industrial Internet of Things Networks",

abstract = "On-demand data sensing and wireless power transfer (WPT) can provide sustainability and robust operations in large-scale industrial Internet of Things (IoT) networks. The efficiency of on-demand data collection and WPT can be increased by efficient scheduling of IoT nodes and dedicated energy transmitters respectively. In this article, we propose an energy-aware mode switching strategy to enable IoT nodes to perform either on-demand sensing or dedicated WPT. For on-demand sensing, we propose an IoT node scheduling scheme to maximize the utility of the IoT nodes comprising residual energy and energy required for sensing operation while considering the reliability of sensing tasks. For WPT, we propose an energy transmitter scheduling scheme for IoT nodes to minimize the cost of charging while keeping IoT nodes sufficiently charged. The simulation results for IoT node scheduling demonstrate that less than 50\% IoT nodes need to be activated in all scenarios to complete the tasks. The proposed energy transmitter scheduling scheme shows that less than 60\% energy transmitters should be scheduled in all the scenarios which results in significant energy reduction in the overall system.",

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AB - On-demand data sensing and wireless power transfer (WPT) can provide sustainability and robust operations in large-scale industrial Internet of Things (IoT) networks. The efficiency of on-demand data collection and WPT can be increased by efficient scheduling of IoT nodes and dedicated energy transmitters respectively. In this article, we propose an energy-aware mode switching strategy to enable IoT nodes to perform either on-demand sensing or dedicated WPT. For on-demand sensing, we propose an IoT node scheduling scheme to maximize the utility of the IoT nodes comprising residual energy and energy required for sensing operation while considering the reliability of sensing tasks. For WPT, we propose an energy transmitter scheduling scheme for IoT nodes to minimize the cost of charging while keeping IoT nodes sufficiently charged. The simulation results for IoT node scheduling demonstrate that less than 50% IoT nodes need to be activated in all scenarios to complete the tasks. The proposed energy transmitter scheduling scheme shows that less than 60% energy transmitters should be scheduled in all the scenarios which results in significant energy reduction in the overall system.

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On-Demand Sensing and Wireless Power Transfer for Self-Sustainable Industrial Internet of Things Networks

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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