Feng Mingwei, Yao Haiqing, Li Jie
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China.
Entropy (Basel). 2023 Jan 10;25(1):139. doi: 10.3390/e25010139.
In recent years, as more and more vehicles request service from roadside units (RSU), the vehicle-to-infrastructure (V2I) communication links are under tremendous pressure. This paper first proposes a dynamic dense traffic flow model under the condition of fading channel. Based on this, the reliability is redefined according to the real-time location information of vehicles. The on-board units (OBU) migrate intensive computing tasks to the appropriate RSU to optimize the execution time and calculating cost at the same time. In addition, competitive delay is introduced into the model of execution time, which can describe the channel resource contention and data conflict in dynamic scenes of the internet of vehicles (IoV). Next, the task scheduling for RSU is formulated as a multi-objective optimization problem. In order to solve the problem, a task scheduling algorithm based on a reliability constraint (TSARC) is proposed to select the optimal RSU for task transmission. When compared with the genetic algorithm (GA), there are some improvements of TSARC: first, the quick non-dominated sorting is applied to layer the population and reduce the complexity. Second, the elite strategy is introduced with an excellent nonlinear optimization ability, which ensures the diversity of optimal individuals and provides different preference choices for passengers. Third, the reference point mechanism is introduced to reserve the individuals that are non-dominated and close to reference points. TSARC's Pareto based multi-objective optimization can comprehensively measure the overall state of the system and flexibly schedule system resources. Furthermore, it overcomes the defects of the GA method, such as the determination of the linear weight value, the non-uniformity of dimensions among objectives, and poor robustness. Finally, numerical simulation results based on the British Highway Traffic Flow Data Set show that the TSARC performs better scalability and efficiency than other methods with different numbers of tasks and traffic flow densities, which verifies the previous theoretical derivation.
近年来，随着越来越多的车辆向路边单元（RSU）请求服务，车与基础设施（V2I）通信链路承受着巨大压力。本文首先提出了一种衰落信道条件下的动态密集交通流模型。在此基础上，根据车辆的实时位置信息重新定义了可靠性。车载单元（OBU）将密集计算任务迁移到合适的RSU，以同时优化执行时间和计算成本。此外，将竞争延迟引入执行时间模型，其可以描述车联网（IoV）动态场景中的信道资源争用和数据冲突。接下来，将RSU的任务调度问题表述为一个多目标优化问题。为了解决该问题，提出了一种基于可靠性约束的任务调度算法（TSARC），用于选择最优的RSU进行任务传输。与遗传算法（GA）相比，TSARC有以下一些改进：首先，应用快速非支配排序对种群进行分层，降低复杂度。其次，引入具有优异非线性优化能力的精英策略，确保最优个体的多样性，并为乘客提供不同的偏好选择。第三，引入参考点机制，保留非支配且接近参考点的个体。基于TSARC的帕累托多目标优化能够全面衡量系统的整体状态，并灵活调度系统资源。此外，它克服了GA方法的缺陷，如线性权重值的确定、目标间维度的不均匀性以及鲁棒性差等问题。最后，基于英国公路交通流数据集的数值模拟结果表明，在不同任务数量和交通流密度下，TSARC比其他方法具有更好的可扩展性和效率，验证了先前的理论推导。
