IoT workload offloading efficient intelligent transport system in federated ACNN integrated cooperated edge-cloud networks

Abdullah Lakhan, Tor-Morten Grønli, Paolo Bellavista, Sajida Memon, Maher Alharby, Orawit Thinnukool
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Abstract

Intelligent transport systems (ITS) provide various cooperative edge cloud services for roadside vehicular applications. These applications offer additional diversity, including ticket validation across transport modes and vehicle and object detection to prevent road collisions. Offloading among cooperative edge and cloud networks plays a key role when these resources constrain devices (e.g., vehicles and mobile) to offload their workloads for execution. ITS used different machine learning and deep learning methods for decision automation. However, the self-autonomous decision-making processes of these techniques require significantly more time and higher accuracy for the aforementioned applications on the road-unit side. Thus, this paper presents the new offloading ITS for IoT vehicles in cooperative edge cloud networks. We present the augmented convolutional neural network (ACNN) that trains the workloads on different edge nodes. The ACNN allows users and machine learning methods to work together, making decisions for offloading and scheduling workload execution. This paper presents an augmented federated learning scheduling scheme (AFLSS). An algorithmic method called AFLSS comprises different sub-schemes that work together in the ITS paradigm for IoT applications in transportation. These sub-schemes include ACNN, offloading, scheduling, and security. Simulation results demonstrate that, in terms of accuracy and total time for the considered problem, the AFLSS outperforms all existing methods.
联盟 ACNN 集成合作边缘云网络中的物联网工作负载卸载高效智能传输系统
智能交通系统(ITS)为路边车辆应用提供各种合作性边缘云服务。这些应用提供了额外的多样性,包括跨运输模式的票据验证以及车辆和物体检测,以防止道路碰撞。当这些资源限制设备(如车辆和移动设备)卸载其工作负载以执行时,合作边缘和云网络之间的卸载就发挥了关键作用。智能交通系统采用了不同的机器学习和深度学习方法来实现决策自动化。然而,这些技术的自主决策过程需要更多的时间和更高的准确性,这对于上述在道路单元侧的应用是非常不利的。因此,本文提出了合作边缘云网络中物联网车辆的新型卸载 ITS。我们提出了增强卷积神经网络(ACNN),可在不同的边缘节点上训练工作负载。ACNN 允许用户和机器学习方法协同工作,为卸载和调度工作负载的执行做出决策。本文提出了一种增强型联合学习调度方案(AFLSS)。一种名为 AFLSS 的算法方法由不同的子方案组成,这些子方案在 ITS 范例中协同工作,适用于交通领域的物联网应用。这些子方案包括 ACNN、卸载、调度和安全。仿真结果表明,就所考虑问题的准确性和总时间而言,AFLSS 优于所有现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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