Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading
The rapid advancements in vehicular technologies have enabled modern autonomous vehicles (AVs) to perform complex tasks, such as augmented reality, real-time video surveillance, and automated parking. However, these applications require significant computational resources, which AVs often lack. To a...
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MDPI AG
2024-10-01
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| author | Mohamed Kamel Benbraika Okba Kraa Yassine Himeur Khaled Telli Shadi Atalla Wathiq Mansoor |
| author_facet | Mohamed Kamel Benbraika Okba Kraa Yassine Himeur Khaled Telli Shadi Atalla Wathiq Mansoor |
| author_sort | Mohamed Kamel Benbraika |
| collection | DOAJ |
| description | The rapid advancements in vehicular technologies have enabled modern autonomous vehicles (AVs) to perform complex tasks, such as augmented reality, real-time video surveillance, and automated parking. However, these applications require significant computational resources, which AVs often lack. To address this limitation, Vehicular Edge Computing (VEC) has emerged as a promising solution, allowing AVs to offload computational tasks to nearby vehicles and edge servers. This offloading process, however, is complicated by factors such as high vehicle mobility and intermittent connectivity. In this paper, we propose the Hungarian Algorithm for Task Offloading (HATO), a novel approach designed to optimize the distribution of computational tasks in 5G-enabled VEC systems. HATO leverages 5G’s low-latency, high-bandwidth communication to efficiently allocate tasks across edge servers and nearby vehicles, utilizing the Hungarian algorithm for optimal task assignment. By designating an edge server to gather contextual information from surrounding nodes and compute the best offloading scheme, HATO reduces computational burdens on AVs and minimizes task failures. Through extensive simulations in both urban and highway scenarios, HATO achieved a significant performance improvement, reducing execution time by up to 75.4% compared to existing methods under full 5G coverage in high-density environments. Additionally, HATO demonstrated zero energy constraint violations and achieved the highest task processing reliability, with an offloading success rate of 87.75% in high-density urban areas. These results highlight the potential of HATO to enhance the efficiency and scalability of VEC systems for autonomous vehicles. |
| format | Article |
| id | doaj-art-a051ae881d5749f5a145a623c3a2ca2f |
| institution | OA Journals |
| issn | 2073-431X |
| language | English |
| publishDate | 2024-10-01 |
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| spelling | doaj-art-a051ae881d5749f5a145a623c3a2ca2f2025-08-20T02:08:14ZengMDPI AGComputers2073-431X2024-10-01131127910.3390/computers13110279Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task OffloadingMohamed Kamel Benbraika0Okba Kraa1Yassine Himeur2Khaled Telli3Shadi Atalla4Wathiq Mansoor5Artificial Intelligence and its Applications Laboratory (LIAP), University of Echahid Hamma Lakhdar, El Oued P.O. Box 789, AlgeriaEnergy Systems Modelling (MSE) Laboratory, Mohamed Khider University, Biskra P.O. Box 145 RP 07000, AlgeriaCollege of Engineering and Information Technology, University of Dubai, Dubai P.O. Box 14143, United Arab EmiratesEnergy Systems Modelling (MSE) Laboratory, Mohamed Khider University, Biskra P.O. Box 145 RP 07000, AlgeriaCollege of Engineering and Information Technology, University of Dubai, Dubai P.O. Box 14143, United Arab EmiratesCollege of Engineering and Information Technology, University of Dubai, Dubai P.O. Box 14143, United Arab EmiratesThe rapid advancements in vehicular technologies have enabled modern autonomous vehicles (AVs) to perform complex tasks, such as augmented reality, real-time video surveillance, and automated parking. However, these applications require significant computational resources, which AVs often lack. To address this limitation, Vehicular Edge Computing (VEC) has emerged as a promising solution, allowing AVs to offload computational tasks to nearby vehicles and edge servers. This offloading process, however, is complicated by factors such as high vehicle mobility and intermittent connectivity. In this paper, we propose the Hungarian Algorithm for Task Offloading (HATO), a novel approach designed to optimize the distribution of computational tasks in 5G-enabled VEC systems. HATO leverages 5G’s low-latency, high-bandwidth communication to efficiently allocate tasks across edge servers and nearby vehicles, utilizing the Hungarian algorithm for optimal task assignment. By designating an edge server to gather contextual information from surrounding nodes and compute the best offloading scheme, HATO reduces computational burdens on AVs and minimizes task failures. Through extensive simulations in both urban and highway scenarios, HATO achieved a significant performance improvement, reducing execution time by up to 75.4% compared to existing methods under full 5G coverage in high-density environments. Additionally, HATO demonstrated zero energy constraint violations and achieved the highest task processing reliability, with an offloading success rate of 87.75% in high-density urban areas. These results highlight the potential of HATO to enhance the efficiency and scalability of VEC systems for autonomous vehicles.https://www.mdpi.com/2073-431X/13/11/279fifth generation (5G)Hungarian algorithmtask offloadingvehicular edge computing (VEC)wireless access in vehicular environments (WAVE) |
| spellingShingle | Mohamed Kamel Benbraika Okba Kraa Yassine Himeur Khaled Telli Shadi Atalla Wathiq Mansoor Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading Computers fifth generation (5G) Hungarian algorithm task offloading vehicular edge computing (VEC) wireless access in vehicular environments (WAVE) |
| title | Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading |
| title_full | Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading |
| title_fullStr | Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading |
| title_full_unstemmed | Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading |
| title_short | Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading |
| title_sort | enhancing 5g vehicular edge computing efficiency with the hungarian algorithm for optimal task offloading |
| topic | fifth generation (5G) Hungarian algorithm task offloading vehicular edge computing (VEC) wireless access in vehicular environments (WAVE) |
| url | https://www.mdpi.com/2073-431X/13/11/279 |
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