Game Theory for Distributed IoV Task Offloading With Fuzzy Neural Network in Edge Computing
Game Theory for Distributed IoV Task Offloading With Fuzzy Neural Network in Edge Computing
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DOI：
10.1109/tfuzz.2022.3158000
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发表时间：
2022-11
影响因子：
11.9
通讯作者：
Xiaolong Xu;Q. Jiang;Peiming Zhang;Xuefei Cao;Mohammad Hossein Khosravi;Linss T. Alex;Lianyong Qi-
中科院分区：
文献类型：
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作者：
Xiaolong Xu;Q. Jiang;Peiming Zhang;Xuefei Cao;Mohammad Hossein Khosravi;Linss T. Alex;Lianyong Qi-
车联网的发展催生了碰撞预警等一系列驾驶辅助服务，提高了交通的安全性和智能化。在车联网中，由于车辆行驶速度快，辅助驾驶服务需要及时满足。通过将边缘计算引入车联网，改善车辆本地计算资源不足的问题，为用户提供高质量的服务。然而，边缘服务器提供的资源往往有限，无法同时满足车联网用户的所有需求。因此，如何在边缘服务器资源有限的情况下最小化用户的任务处理延迟仍然是一个挑战。为了解决上述问题，设计了一种基于 Takagi-Sugeno 模糊神经网络（T-S FNN）和博弈论的任务卸载方案模糊任务卸载和资源分配（F-TORA）。首先，云服务器通过T-S FNN预测每个路段的未来交通流量，并将预测结果传输到路边单元（RSU）。然后，RSU 根据捕获的未来流量数据调整当前负载。各RSU负载均衡后，通过博弈论为用户确定最优的任务卸载策略。接下来，边缘服务器充当代理，通过 $Q$ 学习算法为卸载的任务分配计算资源。最后，通过对比实验验证了所提方法的鲁棒性能。
The development of the Internet of vehicles (IoV) has spawned a series of driving assistance services (e.g., collision warning), which improves the safety and intelligence of transportation. In IoV, the driving assistance services need to be met in time due to the rapid speed of vehicles. By introducing edge computing into the IoV, the insufficiency of local computation resources in vehicles is improved, providing high quality services for users. Nevertheless, the resources provided by edge servers are often limited, which fail to meet all the needs of users in IoV simultaneously. Thereby, how to minimize the tasks processing latency of users in the case of limited edge server resources is still a challenge. To handle the above problem, a task offloading scheme fuzzy-task-offloading-and-resource-allocation (F-TORA) based on Takagi–Sugeno fuzzy neural network (T–S FNN) and game theory is designed. Primarily, the cloud server predicts the future traffic flow of each section through T–S FNN and transmits the prediction results to the roadside units (RSUs). Then, the RSU adjusts the current load based on the captured future traffic flow data. After the load balancing of each RSU, the optimal task offloading strategy is determined for the users by game theory. Following, the edge server acts as an agent to allocate computing resources for the offloaded tasks by $Q$-learning algorithm. Finally, the robust performance of the proposed method is validated by comparative experiments.