UAV-Served Energy Harvesting-Enabled M2M Networks for Green Industry-A Perspective of Energy Efficient Resource Management Scheme

As one of the most important metrics to sustainably provide communication services in green Industrial Internet of Things (IIoT), the problem of improving energy efficiency has constantly attracted extraordinary concerns from industry so far as to academia. In this paper, we intend to investigate th...

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Bibliographic Details
Published in:IEEE transactions on green communications and networking 2023-12, Vol.7 (4), p.1-1
Main Authors: Xu, Xiao-Ren, Xu, Yi-Han, Suo, Long, Zhou, Wen, Yu, Gang, Nallanathan, Arumugam
Format: Article
Language:English
Subjects:
Autonomous aerial vehicles
Energy efficiency
Energy harvesting
Industrial applications
Industrial Internet of Things
Internet of Things
Iterative algorithms
M2M communications
Machine-to-machine communications
Mathematical programming
Optimization
Power control
Quality of service
Resource management
UAV communications
Unmanned aerial vehicles
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Summary:As one of the most important metrics to sustainably provide communication services in green Industrial Internet of Things (IIoT), the problem of improving energy efficiency has constantly attracted extraordinary concerns from industry so far as to academia. In this paper, we intend to investigate the energy efficiency issue for an Energy Harvesting (EH)-enabled Machine-to-Machine (EH-M2M) communication underlaying Unmanned Aerial Vehicles (UAVs) networks from the perspective of resource management. Specifically, we are aiming at maximizing the average energy efficiency of EH-M2M communications by conjointly considering the EH time slot assignment, transmit power control and bandwidth allocation under the limitations of Quality of Service (QoS) and the available energy status of the EH-M2M devices. However, as the optimization problem is non-convex and NP-hard which is hard to tackle directly, we first transform the primitive objective function into a convex form equivalently by non-linear fractional programming and variable relaxation approach. After that, an iterative algorithm on the basis of Dinkelbach and Lagrangian theory is designed to optimize the resource management strategy. Finally, extensive simulation results demonstrate that the proposed scheme can establish more energy efficient communications compared to the benchmark schemes in different network settings.
ISSN:2473-2400
2473-2400
DOI:10.1109/TGCN.2023.3305562