Liao, Yangzhe and Song, Yuanyan and Xia, Siyu and Han, Yi and Xu, Ning and Zhai, Xiaojun (2024) Energy Minimization of RIS-Assisted Cooperative UAV-USV MEC Network. IEEE Internet of Things Journal. p. 1. DOI https://doi.org/10.1109/jiot.2024.3432151 (In Press)
Liao, Yangzhe and Song, Yuanyan and Xia, Siyu and Han, Yi and Xu, Ning and Zhai, Xiaojun (2024) Energy Minimization of RIS-Assisted Cooperative UAV-USV MEC Network. IEEE Internet of Things Journal. p. 1. DOI https://doi.org/10.1109/jiot.2024.3432151 (In Press)
Liao, Yangzhe and Song, Yuanyan and Xia, Siyu and Han, Yi and Xu, Ning and Zhai, Xiaojun (2024) Energy Minimization of RIS-Assisted Cooperative UAV-USV MEC Network. IEEE Internet of Things Journal. p. 1. DOI https://doi.org/10.1109/jiot.2024.3432151 (In Press)
Abstract
Unmanned surface vehicles (USVs) are becoming increasingly significant in fulfilling integrated sensing, computing and communication with the emergence of bidirectional com- putation tasks. However, QoS provisioning is still challenging since USVs are restricted with limited on-board resources and direct links between them and shore-based terrestrial base stations (TBSs) are frequently blocked. This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted cooperative unmanned aerial vehicle (UAV)-USV mobile edge computing (MEC) network architecture, where RIS-mounted tethered UAV (TUAV) and rotary-wing UAVs (RUAVs) are collaboratively utilized to serve USVs. RUAVs energy minimization is formulated by jointly considering TUAV hovering altitude, RIS phase shift vector, RUAV service selection indicator and RUAVs turning points. A heuristic solution is proposed to tackle the formulated problem, where the original problem is first decoupled into three subproblems, e.g., the joint optimization of RIS phase shift vector and TUAV hovering altitude subproblem, RUAVs service selection indicators subproblem and RUAVs turning points subproblem, each of which is solved by the proposed modified alternative direction method of multiplier (ADMM) algorithm, the proposed enhanced simulated annealing (ESA) algorithm and the proposed successive convex approximation (SCA)-based algorithm. In this way, the challenging problem can be efficiently solved iteratively. The results show that the proposed solution can decrease RUAVs energy consumption by nearly 29% compared to numerous selected advanced algorithms. Moreover, the performance of the proposed solution regarding typical penalty coefficients and number of RIS reflecting elements is investigated.
Item Type: | Article |
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Uncontrolled Keywords: | Unmanned Surface Vehicles, Unmanned Aerial Vehicle, Mobile Edge Computing, Reconfigurable Intelligent Surface, Energy Minimization |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 19 Jul 2024 14:43 |
Last Modified: | 25 Jul 2024 06:00 |
URI: | http://repository.essex.ac.uk/id/eprint/38812 |
Available files
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