Joint trajectory design and resource allocation for UAV-assisted mobile edge computing in power convergence network
Abstract In the power convergence network, a large number of intelligent power terminals (IPTs) are deployed, such as a variety of information collection terminals. Meanwhile, the unmanned aerial vehicles (UAVs) offer dependable services for IPTs in environments with minimal or no infrastructure and...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
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| Online Access: | https://doi.org/10.1186/s13638-024-02426-9 |
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| _version_ | 1841545068523028480 |
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| author | Junbin Cui Yong Wei Jianbo Wang Li Shang Peng Lin |
| author_facet | Junbin Cui Yong Wei Jianbo Wang Li Shang Peng Lin |
| author_sort | Junbin Cui |
| collection | DOAJ |
| description | Abstract In the power convergence network, a large number of intelligent power terminals (IPTs) are deployed, such as a variety of information collection terminals. Meanwhile, the unmanned aerial vehicles (UAVs) offer dependable services for IPTs in environments with minimal or no infrastructure and then combine with the mobile edge computing to realize low-latency task services. In this article, we consider a problem of computation offloading and UAV trajectory design to minimize the task offloading delay. Because the primal problem is non-convex, we first decompose it into two subproblems, i.e., joint computation offloading and resource allocation subproblem, and UAV trajectory design subproblem. For the first subproblem, we first reformulate it as a non-convex problem. And then, recognizing the problem’s high complexity, we choose to decompose it in a distributed form. Following this, leveraging the alternating direction method of multipliers, we introduce the joint computation offloading and resource allocation algorithm. For the second subproblem, we utilize the successive convex approximation method to solve this non-convex problem. Utilizing the solutions obtained from these two subproblems, we have introduced the joint computation offloading and UAV trajectory design (JCOUTD) algorithm to tackle the primal problem. The simulation results reveal that in comparison with other benchmark methods, the proposed JCOUTD algorithm displays enhanced performance in reducing total task offloading delay. |
| format | Article |
| id | doaj-art-9a79bd3e53344ed59adcc0a688d97c25 |
| institution | Kabale University |
| issn | 1687-1499 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj-art-9a79bd3e53344ed59adcc0a688d97c252025-01-12T12:04:56ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992025-01-012025112110.1186/s13638-024-02426-9Joint trajectory design and resource allocation for UAV-assisted mobile edge computing in power convergence networkJunbin Cui0Yong Wei1Jianbo Wang2Li Shang3Peng Lin4Information and Telecommunication Branch, State Grid Hebei Electric Power Co.Ltd.Information and Telecommunication Branch, State Grid Hebei Electric Power Co.Ltd.Information and Telecommunication Branch, State Grid Hebei Electric Power Co.Ltd.Information and Telecommunication Branch, State Grid Hebei Electric Power Co.Ltd.Beijing Vectinfo Technologies Co.Ltd.Abstract In the power convergence network, a large number of intelligent power terminals (IPTs) are deployed, such as a variety of information collection terminals. Meanwhile, the unmanned aerial vehicles (UAVs) offer dependable services for IPTs in environments with minimal or no infrastructure and then combine with the mobile edge computing to realize low-latency task services. In this article, we consider a problem of computation offloading and UAV trajectory design to minimize the task offloading delay. Because the primal problem is non-convex, we first decompose it into two subproblems, i.e., joint computation offloading and resource allocation subproblem, and UAV trajectory design subproblem. For the first subproblem, we first reformulate it as a non-convex problem. And then, recognizing the problem’s high complexity, we choose to decompose it in a distributed form. Following this, leveraging the alternating direction method of multipliers, we introduce the joint computation offloading and resource allocation algorithm. For the second subproblem, we utilize the successive convex approximation method to solve this non-convex problem. Utilizing the solutions obtained from these two subproblems, we have introduced the joint computation offloading and UAV trajectory design (JCOUTD) algorithm to tackle the primal problem. The simulation results reveal that in comparison with other benchmark methods, the proposed JCOUTD algorithm displays enhanced performance in reducing total task offloading delay.https://doi.org/10.1186/s13638-024-02426-9UAV trajectory designPower convergence networkMobile edge computing (MEC)Alternating direction method of multipliers (ADMM)Successive convex approximation (SCA) |
| spellingShingle | Junbin Cui Yong Wei Jianbo Wang Li Shang Peng Lin Joint trajectory design and resource allocation for UAV-assisted mobile edge computing in power convergence network EURASIP Journal on Wireless Communications and Networking UAV trajectory design Power convergence network Mobile edge computing (MEC) Alternating direction method of multipliers (ADMM) Successive convex approximation (SCA) |
| title | Joint trajectory design and resource allocation for UAV-assisted mobile edge computing in power convergence network |
| title_full | Joint trajectory design and resource allocation for UAV-assisted mobile edge computing in power convergence network |
| title_fullStr | Joint trajectory design and resource allocation for UAV-assisted mobile edge computing in power convergence network |
| title_full_unstemmed | Joint trajectory design and resource allocation for UAV-assisted mobile edge computing in power convergence network |
| title_short | Joint trajectory design and resource allocation for UAV-assisted mobile edge computing in power convergence network |
| title_sort | joint trajectory design and resource allocation for uav assisted mobile edge computing in power convergence network |
| topic | UAV trajectory design Power convergence network Mobile edge computing (MEC) Alternating direction method of multipliers (ADMM) Successive convex approximation (SCA) |
| url | https://doi.org/10.1186/s13638-024-02426-9 |
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