Joint Stochastic Computation Offloading and Trajectory Optimization for Unmanned-Aerial-Vehicle-Assisted Mobile Edge Computing
In Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAVs) equipped with communication and computation capabilities can be used as an edge node, which can not only satisfy the user’s demand for high computation power and low latency, but also extend the range of computation services...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10818606/ |
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| author | Yi Zhou |
| author_facet | Yi Zhou |
| author_sort | Yi Zhou |
| collection | DOAJ |
| description | In Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAVs) equipped with communication and computation capabilities can be used as an edge node, which can not only satisfy the user’s demand for high computation power and low latency, but also extend the range of computation services and enhance the mission quality in environments with limited communication facilities. In this study, we investigate a UAV-assisted MEC system with stochastic computing tasks. The system seeks to minimize overall energy consumption by optimizing computation offloading, resource allocation, and the UAV’s trajectory scheduling. This objective corresponds to a stochastic optimization problem. Given the problem’s non-convex nature and the temporal coupling of variables, the Lyapunov optimization method is employed to analyze the task queue, breaking down the original optimization problem into three independent and more manageable subproblems. A joint optimization algorithm with iterative solving is proposed for solving the three sub-problems and then obtaining the stochastic computation offloading, resource allocation, and trajectory scheduling strategy. The simulation results show that the proposed strategy is able to achieve an effective compromise between the system’s energy consumption and queue stability by adjusting the Lyapunov parameters, and significantly reduce energy consumption compared to the baseline strategies. |
| format | Article |
| id | doaj-art-04acaf6d6d7c44399b6b7a00aa7edb9d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-04acaf6d6d7c44399b6b7a00aa7edb9d2025-01-07T00:01:45ZengIEEEIEEE Access2169-35362025-01-01132034204410.1109/ACCESS.2024.351959810818606Joint Stochastic Computation Offloading and Trajectory Optimization for Unmanned-Aerial-Vehicle-Assisted Mobile Edge ComputingYi Zhou0https://orcid.org/0009-0009-2962-323XCollege of Computer Science, Chongqing University, Chongqing, ChinaIn Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAVs) equipped with communication and computation capabilities can be used as an edge node, which can not only satisfy the user’s demand for high computation power and low latency, but also extend the range of computation services and enhance the mission quality in environments with limited communication facilities. In this study, we investigate a UAV-assisted MEC system with stochastic computing tasks. The system seeks to minimize overall energy consumption by optimizing computation offloading, resource allocation, and the UAV’s trajectory scheduling. This objective corresponds to a stochastic optimization problem. Given the problem’s non-convex nature and the temporal coupling of variables, the Lyapunov optimization method is employed to analyze the task queue, breaking down the original optimization problem into three independent and more manageable subproblems. A joint optimization algorithm with iterative solving is proposed for solving the three sub-problems and then obtaining the stochastic computation offloading, resource allocation, and trajectory scheduling strategy. The simulation results show that the proposed strategy is able to achieve an effective compromise between the system’s energy consumption and queue stability by adjusting the Lyapunov parameters, and significantly reduce energy consumption compared to the baseline strategies.https://ieeexplore.ieee.org/document/10818606/Mobile edge computingunmanned aerial vehiclecomputation offloadingresource allocationtrajectory scheduling |
| spellingShingle | Yi Zhou Joint Stochastic Computation Offloading and Trajectory Optimization for Unmanned-Aerial-Vehicle-Assisted Mobile Edge Computing IEEE Access Mobile edge computing unmanned aerial vehicle computation offloading resource allocation trajectory scheduling |
| title | Joint Stochastic Computation Offloading and Trajectory Optimization for Unmanned-Aerial-Vehicle-Assisted Mobile Edge Computing |
| title_full | Joint Stochastic Computation Offloading and Trajectory Optimization for Unmanned-Aerial-Vehicle-Assisted Mobile Edge Computing |
| title_fullStr | Joint Stochastic Computation Offloading and Trajectory Optimization for Unmanned-Aerial-Vehicle-Assisted Mobile Edge Computing |
| title_full_unstemmed | Joint Stochastic Computation Offloading and Trajectory Optimization for Unmanned-Aerial-Vehicle-Assisted Mobile Edge Computing |
| title_short | Joint Stochastic Computation Offloading and Trajectory Optimization for Unmanned-Aerial-Vehicle-Assisted Mobile Edge Computing |
| title_sort | joint stochastic computation offloading and trajectory optimization for unmanned aerial vehicle assisted mobile edge computing |
| topic | Mobile edge computing unmanned aerial vehicle computation offloading resource allocation trajectory scheduling |
| url | https://ieeexplore.ieee.org/document/10818606/ |
| work_keys_str_mv | AT yizhou jointstochasticcomputationoffloadingandtrajectoryoptimizationforunmannedaerialvehicleassistedmobileedgecomputing |