Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing
Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources. However, the overwhelming upload traffic may lead to unacceptable uploading time. To tackle this issue, for...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-07-01
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| Series: | Journal of Information and Intelligence |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949715924000106 |
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| author | Ruijin Sun Yao Wen Nan Cheng Wei Wang Rong Chai Yilong Hui |
| author_facet | Ruijin Sun Yao Wen Nan Cheng Wei Wang Rong Chai Yilong Hui |
| author_sort | Ruijin Sun |
| collection | DOAJ |
| description | Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources. However, the overwhelming upload traffic may lead to unacceptable uploading time. To tackle this issue, for tasks taking environmental data as input, the data perceived by roadside units (RSU) equipped with several sensors can be directly exploited for computation, resulting in a novel task offloading paradigm with integrated communications, sensing and computing (I-CSC). With this paradigm, vehicles can select to upload their sensed data to RSUs or transmit computing instructions to RSUs during the offloading. By optimizing the computation mode and network resources, in this paper, we investigate an I-CSC-based task offloading problem to reduce the cost caused by resource consumption while guaranteeing the latency of each task. Although this non-convex problem can be handled by the alternating minimization (AM) algorithm that alternatively minimizes the divided four sub-problems, it leads to high computational complexity and local optimal solution. To tackle this challenge, we propose a creative structural knowledge-driven meta-learning (SKDML) method, involving both the model-based AM algorithm and neural networks. Specifically, borrowing the iterative structure of the AM algorithm, also referred to as structural knowledge, the proposed SKDML adopts long short-term memory (LSTM) network-based meta-learning to learn an adaptive optimizer for updating variables in each sub-problem, instead of the handcrafted counterpart in the AM algorithm. Furthermore, to pull out the solution from the local optimum, our proposed SKDML updates parameters in LSTM with the global loss function. Simulation results demonstrate that our method outperforms both the AM algorithm and the meta-learning without structural knowledge in terms of both the online processing time and the network performance. |
| format | Article |
| id | doaj-art-320d5a74e9ef4657b66aabb7bff90d79 |
| institution | OA Journals |
| issn | 2949-7159 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Information and Intelligence |
| spelling | doaj-art-320d5a74e9ef4657b66aabb7bff90d792025-08-20T02:37:36ZengKeAi Communications Co., Ltd.Journal of Information and Intelligence2949-71592024-07-012430232410.1016/j.jiixd.2024.02.005Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computingRuijin Sun0Yao Wen1Nan Cheng2Wei Wang3Rong Chai4Yilong Hui5State Key Laboratory of ISN, Xidian University, Xi'an 710071, ChinaState Key Laboratory of ISN, Xidian University, Xi'an 710071, ChinaState Key Laboratory of ISN, Xidian University, Xi'an 710071, China; Corresponding author.State Key Laboratory of ISN, Xidian University, Xi'an 710071, ChinaChongqing Key Laboratory of Mobile Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaState Key Laboratory of ISN, Xidian University, Xi'an 710071, ChinaTask offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources. However, the overwhelming upload traffic may lead to unacceptable uploading time. To tackle this issue, for tasks taking environmental data as input, the data perceived by roadside units (RSU) equipped with several sensors can be directly exploited for computation, resulting in a novel task offloading paradigm with integrated communications, sensing and computing (I-CSC). With this paradigm, vehicles can select to upload their sensed data to RSUs or transmit computing instructions to RSUs during the offloading. By optimizing the computation mode and network resources, in this paper, we investigate an I-CSC-based task offloading problem to reduce the cost caused by resource consumption while guaranteeing the latency of each task. Although this non-convex problem can be handled by the alternating minimization (AM) algorithm that alternatively minimizes the divided four sub-problems, it leads to high computational complexity and local optimal solution. To tackle this challenge, we propose a creative structural knowledge-driven meta-learning (SKDML) method, involving both the model-based AM algorithm and neural networks. Specifically, borrowing the iterative structure of the AM algorithm, also referred to as structural knowledge, the proposed SKDML adopts long short-term memory (LSTM) network-based meta-learning to learn an adaptive optimizer for updating variables in each sub-problem, instead of the handcrafted counterpart in the AM algorithm. Furthermore, to pull out the solution from the local optimum, our proposed SKDML updates parameters in LSTM with the global loss function. Simulation results demonstrate that our method outperforms both the AM algorithm and the meta-learning without structural knowledge in terms of both the online processing time and the network performance.http://www.sciencedirect.com/science/article/pii/S2949715924000106Knowledge-driven meta-learningIntegration of communicationSensing and computingTask offloadingVehicular networks |
| spellingShingle | Ruijin Sun Yao Wen Nan Cheng Wei Wang Rong Chai Yilong Hui Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing Journal of Information and Intelligence Knowledge-driven meta-learning Integration of communication Sensing and computing Task offloading Vehicular networks |
| title | Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing |
| title_full | Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing |
| title_fullStr | Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing |
| title_full_unstemmed | Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing |
| title_short | Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing |
| title_sort | structural knowledge driven meta learning for task offloading in vehicular networks with integrated communications sensing and computing |
| topic | Knowledge-driven meta-learning Integration of communication Sensing and computing Task offloading Vehicular networks |
| url | http://www.sciencedirect.com/science/article/pii/S2949715924000106 |
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