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Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles

Mobile edge computing (MEC) represents an enabling technology for prospective Internet of Vehicles (IoV) networks. However, the complex vehicular propagation environment may hinder computation offloading. To this end, this paper proposes a novel computation offloading framework for IoV and presents...

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Autores principales: Michailidis, Emmanouel T., Miridakis, Nikolaos I., Michalas, Angelos, Skondras, Emmanouil, Vergados, Dimitrios J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271975/
https://www.ncbi.nlm.nih.gov/pubmed/34198977
http://dx.doi.org/10.3390/s21134392
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author Michailidis, Emmanouel T.
Miridakis, Nikolaos I.
Michalas, Angelos
Skondras, Emmanouil
Vergados, Dimitrios J.
author_facet Michailidis, Emmanouel T.
Miridakis, Nikolaos I.
Michalas, Angelos
Skondras, Emmanouil
Vergados, Dimitrios J.
author_sort Michailidis, Emmanouel T.
collection PubMed
description Mobile edge computing (MEC) represents an enabling technology for prospective Internet of Vehicles (IoV) networks. However, the complex vehicular propagation environment may hinder computation offloading. To this end, this paper proposes a novel computation offloading framework for IoV and presents an unmanned aerial vehicle (UAV)-aided network architecture. It is considered that the connected vehicles in a IoV ecosystem should fully offload latency-critical computation-intensive tasks to road side units (RSUs) that integrate MEC functionalities. In this regard, a UAV is deployed to serve as an aerial RSU (ARSU) and also operate as an aerial relay to offload part of the tasks to a ground RSU (GRSU). In order to further enhance the end-to-end communication during data offloading, the proposed architecture relies on reconfigurable intelligent surface (RIS) units consisting of arrays of reflecting elements. In particular, a dual-RIS configuration is presented, where each RIS unit serves its nearby network nodes. Since perfect phase estimation or high-precision configuration of the reflection phases is impractical in highly mobile IoV environments, data offloading via RIS units with phase errors is considered. As the efficient energy management of resource-constrained electric vehicles and battery-enabled RSUs is of outmost importance, this paper proposes an optimization approach that intends to minimize the weighted total energy consumption (WTEC) of the vehicles and ARSU subject to transmit power constraints, timeslot scheduling, and task allocation. Extensive numerical calculations are carried out to verify the efficacy of the optimized dual-RIS-assisted wireless transmission.
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spelling pubmed-82719752021-07-11 Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles Michailidis, Emmanouel T. Miridakis, Nikolaos I. Michalas, Angelos Skondras, Emmanouil Vergados, Dimitrios J. Sensors (Basel) Article Mobile edge computing (MEC) represents an enabling technology for prospective Internet of Vehicles (IoV) networks. However, the complex vehicular propagation environment may hinder computation offloading. To this end, this paper proposes a novel computation offloading framework for IoV and presents an unmanned aerial vehicle (UAV)-aided network architecture. It is considered that the connected vehicles in a IoV ecosystem should fully offload latency-critical computation-intensive tasks to road side units (RSUs) that integrate MEC functionalities. In this regard, a UAV is deployed to serve as an aerial RSU (ARSU) and also operate as an aerial relay to offload part of the tasks to a ground RSU (GRSU). In order to further enhance the end-to-end communication during data offloading, the proposed architecture relies on reconfigurable intelligent surface (RIS) units consisting of arrays of reflecting elements. In particular, a dual-RIS configuration is presented, where each RIS unit serves its nearby network nodes. Since perfect phase estimation or high-precision configuration of the reflection phases is impractical in highly mobile IoV environments, data offloading via RIS units with phase errors is considered. As the efficient energy management of resource-constrained electric vehicles and battery-enabled RSUs is of outmost importance, this paper proposes an optimization approach that intends to minimize the weighted total energy consumption (WTEC) of the vehicles and ARSU subject to transmit power constraints, timeslot scheduling, and task allocation. Extensive numerical calculations are carried out to verify the efficacy of the optimized dual-RIS-assisted wireless transmission. MDPI 2021-06-27 /pmc/articles/PMC8271975/ /pubmed/34198977 http://dx.doi.org/10.3390/s21134392 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Michailidis, Emmanouel T.
Miridakis, Nikolaos I.
Michalas, Angelos
Skondras, Emmanouil
Vergados, Dimitrios J.
Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles
title Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles
title_full Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles
title_fullStr Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles
title_full_unstemmed Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles
title_short Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles
title_sort energy optimization in dual-ris uav-aided mec-enabled internet of vehicles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271975/
https://www.ncbi.nlm.nih.gov/pubmed/34198977
http://dx.doi.org/10.3390/s21134392
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