Deep Learning Algorithms for RF Energy Harvesting Cognitive IoT Devices: Applications, Challenges and Opportunities

Obumneme Obiajulu Umeonwuka; Babatunde Segun Adejumobi; Thokozani Shongwe

doi:10.1109/ICECET55527.2022.9872992

Deep Learning Algorithms for RF Energy Harvesting Cognitive IoT Devices: Applications, Challenges and Opportunities

Obumneme Obiajulu Umeonwuka
, Babatunde Segun Adejumobi
, Thokozani Shongwe

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

The growing number of IoT devices have brought up the opportunity for large data utilization and the challenge of energy availability due to the large number of devices that need energy to function reliably. One such source of energy is battery, especially for mobile IoT devices. Battery technology has lagged behind IoT device miniaturization in terms of size and weight, rendering them unsuitable or extremely challenging to replace for the majority of IoT device applications. A method for enhancing energy availability in mobile devices' batteries or getting rid of them entirely is radio frequency energy harvesting. These make Deep Learning attractive for resource-constrained devices such as Energy Harvesting Cognitive Internet of Things (EH CIoT) devices. This work discusses Deep Learning schemes for EH CIoT devices paying particular attention to the energy efficiency, speed of execution and complexity of the deployed models.

Original language	English
Title of host publication	International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665470872
DOIs	https://doi.org/10.1109/ICECET55527.2022.9872992
Publication status	Published - 2022
Externally published	Yes
Event	2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022 - Prague, Czech Republic Duration: 20 Jul 2022 → 22 Jul 2022

Publication series

Name	International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022

Conference

Conference	2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022
Country/Territory	Czech Republic
City	Prague
Period	20/07/22 → 22/07/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Cognitive IoT
Deep Learning
IoT
energy efficiency
energy harvesting

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Media Technology
Instrumentation

Access to Document

10.1109/ICECET55527.2022.9872992

Cite this

Umeonwuka, O. O., Adejumobi, B. S., & Shongwe, T. (2022). Deep Learning Algorithms for RF Energy Harvesting Cognitive IoT Devices: Applications, Challenges and Opportunities. In International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022 (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECET55527.2022.9872992

Umeonwuka, Obumneme Obiajulu ; Adejumobi, Babatunde Segun ; Shongwe, Thokozani. / Deep Learning Algorithms for RF Energy Harvesting Cognitive IoT Devices : Applications, Challenges and Opportunities. International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022).

@inproceedings{5dad010d60f44d9a9811e2abcf228172,

title = "Deep Learning Algorithms for RF Energy Harvesting Cognitive IoT Devices: Applications, Challenges and Opportunities",

abstract = "The growing number of IoT devices have brought up the opportunity for large data utilization and the challenge of energy availability due to the large number of devices that need energy to function reliably. One such source of energy is battery, especially for mobile IoT devices. Battery technology has lagged behind IoT device miniaturization in terms of size and weight, rendering them unsuitable or extremely challenging to replace for the majority of IoT device applications. A method for enhancing energy availability in mobile devices' batteries or getting rid of them entirely is radio frequency energy harvesting. These make Deep Learning attractive for resource-constrained devices such as Energy Harvesting Cognitive Internet of Things (EH CIoT) devices. This work discusses Deep Learning schemes for EH CIoT devices paying particular attention to the energy efficiency, speed of execution and complexity of the deployed models.",

keywords = "Cognitive IoT, Deep Learning, IoT, energy efficiency, energy harvesting",

author = "Umeonwuka, \{Obumneme Obiajulu\} and Adejumobi, \{Babatunde Segun\} and Thokozani Shongwe",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022 ; Conference date: 20-07-2022 Through 22-07-2022",

year = "2022",

doi = "10.1109/ICECET55527.2022.9872992",

language = "English",

series = "International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022",

address = "United States",

}

Umeonwuka, OO, Adejumobi, BS & Shongwe, T 2022, Deep Learning Algorithms for RF Energy Harvesting Cognitive IoT Devices: Applications, Challenges and Opportunities. in International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022. International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022, Prague, Czech Republic, 20/07/22. https://doi.org/10.1109/ICECET55527.2022.9872992

Deep Learning Algorithms for RF Energy Harvesting Cognitive IoT Devices: Applications, Challenges and Opportunities. / Umeonwuka, Obumneme Obiajulu; Adejumobi, Babatunde Segun; Shongwe, Thokozani.
International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep Learning Algorithms for RF Energy Harvesting Cognitive IoT Devices

T2 - 2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022

AU - Umeonwuka, Obumneme Obiajulu

AU - Adejumobi, Babatunde Segun

AU - Shongwe, Thokozani

PY - 2022

Y1 - 2022

N2 - The growing number of IoT devices have brought up the opportunity for large data utilization and the challenge of energy availability due to the large number of devices that need energy to function reliably. One such source of energy is battery, especially for mobile IoT devices. Battery technology has lagged behind IoT device miniaturization in terms of size and weight, rendering them unsuitable or extremely challenging to replace for the majority of IoT device applications. A method for enhancing energy availability in mobile devices' batteries or getting rid of them entirely is radio frequency energy harvesting. These make Deep Learning attractive for resource-constrained devices such as Energy Harvesting Cognitive Internet of Things (EH CIoT) devices. This work discusses Deep Learning schemes for EH CIoT devices paying particular attention to the energy efficiency, speed of execution and complexity of the deployed models.

AB - The growing number of IoT devices have brought up the opportunity for large data utilization and the challenge of energy availability due to the large number of devices that need energy to function reliably. One such source of energy is battery, especially for mobile IoT devices. Battery technology has lagged behind IoT device miniaturization in terms of size and weight, rendering them unsuitable or extremely challenging to replace for the majority of IoT device applications. A method for enhancing energy availability in mobile devices' batteries or getting rid of them entirely is radio frequency energy harvesting. These make Deep Learning attractive for resource-constrained devices such as Energy Harvesting Cognitive Internet of Things (EH CIoT) devices. This work discusses Deep Learning schemes for EH CIoT devices paying particular attention to the energy efficiency, speed of execution and complexity of the deployed models.

KW - Cognitive IoT

KW - Deep Learning

KW - IoT

KW - energy efficiency

KW - energy harvesting

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U2 - 10.1109/ICECET55527.2022.9872992

DO - 10.1109/ICECET55527.2022.9872992

M3 - Conference contribution

AN - SCOPUS:85138871583

T3 - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022

BT - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 20 July 2022 through 22 July 2022

ER -

Umeonwuka OO, Adejumobi BS, Shongwe T. Deep Learning Algorithms for RF Energy Harvesting Cognitive IoT Devices: Applications, Challenges and Opportunities. In International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022). doi: 10.1109/ICECET55527.2022.9872992

Deep Learning Algorithms for RF Energy Harvesting Cognitive IoT Devices: Applications, Challenges and Opportunities

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this