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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MDPI AG
2021-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/13/4392 |
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| author | Emmanouel T. Michailidis Nikolaos I. Miridakis Angelos Michalas Emmanouil Skondras Dimitrios J. Vergados |
| author_facet | Emmanouel T. Michailidis Nikolaos I. Miridakis Angelos Michalas Emmanouil Skondras Dimitrios J. Vergados |
| author_sort | Emmanouel T. Michailidis |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T04:51:17Z |
| format | Article |
| id | doaj.art-ba96972381a64cc7bf90ed4f397ca361 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:51:17Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ba96972381a64cc7bf90ed4f397ca3612023-12-03T13:10:49ZengMDPI AGSensors1424-82202021-06-012113439210.3390/s21134392Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of VehiclesEmmanouel T. Michailidis0Nikolaos I. Miridakis1Angelos Michalas2Emmanouil Skondras3Dimitrios J. Vergados4Department of Electrical and Electronics Engineering, University of West Attica, Ancient Olive Grove Campus, 250 Thivon & P. Ralli Str, 12241 Egaleo, GreeceDepartment of Informatics and Computer Engineering, University of West Attica, Egaleo Park Campus, Ag. Spyridonos Str, 12243 Egaleo, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, Karamanli & Ligeris, 50131 Kozani, GreeceDepartment of Informatics, University of Piraeus, 80 Karaoli & Dimitriou St., 18534 Piraeus, GreeceDepartment of Informatics, University of Western Macedonia, Fourka Area, 52100 Kastoria, GreeceMobile 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.https://www.mdpi.com/1424-8220/21/13/4392computation offloadingenergy efficiencyInternet of Vehicles (IoV)mobile edge computing (MEC)reconfigurable intelligent surface (RIS)unmanned aerial vehicle (UAV) |
| spellingShingle | Emmanouel T. Michailidis Nikolaos I. Miridakis Angelos Michalas Emmanouil Skondras Dimitrios J. Vergados Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles Sensors computation offloading energy efficiency Internet of Vehicles (IoV) mobile edge computing (MEC) reconfigurable intelligent surface (RIS) unmanned aerial vehicle (UAV) |
| 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 | computation offloading energy efficiency Internet of Vehicles (IoV) mobile edge computing (MEC) reconfigurable intelligent surface (RIS) unmanned aerial vehicle (UAV) |
| url | https://www.mdpi.com/1424-8220/21/13/4392 |
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