Computation Offloading and Resource Allocation for Energy-Harvested MEC in an Ultra-Dense Network
Mobile edge computing (MEC) is a modern technique that has led to substantial progress in wireless networks. To address the challenge of efficient task implementation in resource-limited environments, this work strengthens system performance through resource allocation based on fairness and energy e...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/6/1722 |
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| author | Dedi Triyanto I Wayan Mustika Widyawan |
| author_facet | Dedi Triyanto I Wayan Mustika Widyawan |
| author_sort | Dedi Triyanto |
| collection | DOAJ |
| description | Mobile edge computing (MEC) is a modern technique that has led to substantial progress in wireless networks. To address the challenge of efficient task implementation in resource-limited environments, this work strengthens system performance through resource allocation based on fairness and energy efficiency. Integration of energy-harvesting (EH) technology with MEC improves sustainability by optimizing the power consumption of mobile devices, which is crucial to the efficiency of task execution. The combination of MEC and an ultra-dense network (UDN) is essential in fifth-generation networks to fulfill the computing requirements of ultra-low-latency applications. In this study, issues related to computation offloading and resource allocation are addressed using the Lyapunov mixed-integer linear programming (MILP)-based optimal cost (LYMOC) technique. The optimization problem is solved using the Lyapunov drift-plus-penalty method. Subsequently, the MILP approach is employed to select the optimal offloading option while ensuring fairness-oriented resource allocation among users to improve overall system performance and user satisfaction. Unlike conventional approaches, which often overlook fairness in dense networks, the proposed method prioritizes fairness-oriented resource allocation, preventing service degradation and enhancing network efficiency. Overall, the results of simulation studies demonstrate that the LYMOC algorithm may considerably decrease the overall cost of system execution when compared with the Lyapunov–MILP-based short-distance complete local execution algorithm and the full offloading-computation method. |
| format | Article |
| id | doaj-art-e7f4826139fa4b1aa1d7cee2292745db |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e7f4826139fa4b1aa1d7cee2292745db2025-08-20T02:43:03ZengMDPI AGSensors1424-82202025-03-01256172210.3390/s25061722Computation Offloading and Resource Allocation for Energy-Harvested MEC in an Ultra-Dense NetworkDedi Triyanto0I Wayan Mustika1Widyawan2Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaMobile edge computing (MEC) is a modern technique that has led to substantial progress in wireless networks. To address the challenge of efficient task implementation in resource-limited environments, this work strengthens system performance through resource allocation based on fairness and energy efficiency. Integration of energy-harvesting (EH) technology with MEC improves sustainability by optimizing the power consumption of mobile devices, which is crucial to the efficiency of task execution. The combination of MEC and an ultra-dense network (UDN) is essential in fifth-generation networks to fulfill the computing requirements of ultra-low-latency applications. In this study, issues related to computation offloading and resource allocation are addressed using the Lyapunov mixed-integer linear programming (MILP)-based optimal cost (LYMOC) technique. The optimization problem is solved using the Lyapunov drift-plus-penalty method. Subsequently, the MILP approach is employed to select the optimal offloading option while ensuring fairness-oriented resource allocation among users to improve overall system performance and user satisfaction. Unlike conventional approaches, which often overlook fairness in dense networks, the proposed method prioritizes fairness-oriented resource allocation, preventing service degradation and enhancing network efficiency. Overall, the results of simulation studies demonstrate that the LYMOC algorithm may considerably decrease the overall cost of system execution when compared with the Lyapunov–MILP-based short-distance complete local execution algorithm and the full offloading-computation method.https://www.mdpi.com/1424-8220/25/6/1722mobile edge computingcomputation offloadingultra-dense networkresource allocation |
| spellingShingle | Dedi Triyanto I Wayan Mustika Widyawan Computation Offloading and Resource Allocation for Energy-Harvested MEC in an Ultra-Dense Network Sensors mobile edge computing computation offloading ultra-dense network resource allocation |
| title | Computation Offloading and Resource Allocation for Energy-Harvested MEC in an Ultra-Dense Network |
| title_full | Computation Offloading and Resource Allocation for Energy-Harvested MEC in an Ultra-Dense Network |
| title_fullStr | Computation Offloading and Resource Allocation for Energy-Harvested MEC in an Ultra-Dense Network |
| title_full_unstemmed | Computation Offloading and Resource Allocation for Energy-Harvested MEC in an Ultra-Dense Network |
| title_short | Computation Offloading and Resource Allocation for Energy-Harvested MEC in an Ultra-Dense Network |
| title_sort | computation offloading and resource allocation for energy harvested mec in an ultra dense network |
| topic | mobile edge computing computation offloading ultra-dense network resource allocation |
| url | https://www.mdpi.com/1424-8220/25/6/1722 |
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