Game Theory for Distributed IoV Task Offloading With Fuzzy Neural Network in Edge Computing

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.

I. Introduction

The increase of vehicle ownership has brought a series of problems, such as traffic congestion and driving safety to the city. Meanwhile, the development of the Internet of Things makes people’s demand for travel services more complex and diversified [1]. To improve the travel experience of the users, the Internet of vehicles (IoV) came into being. The IoV is a dynamic mobile communication system that connects vehicles, pedestrians, sensing devices, and service providers as a whole to achieve the communication between vehicles and the public network [2]. Through the IoV, service providers can obtain relevant information, such as the road environment while driving. With these information, users are provided with a range of ancillary services, such as collision warning and automatic driving, which effectively improve the safety and comfort of travel [3], [4].