Distributed and Scalable Trusted Vehicular System for Secure and Optimized Workflow Execution in Vehicular Edge Computing
Keywords:
Cloud computing, Edge computing, Internet of Vehicles (IoV), Trust management, Vehicular edge computing (VEC), Workflow schedulingAbstract
The rapid growth of the Internet of Vehicles (IoV) has created a strong need for low-latency, high throughput, and secure vehicular edge–cloud (VEC) systems capable of handling large-scale and heterogeneous workloads. However, existing solutions often struggle with limited scalability, rigid trust mechanisms, high computational cost, and weak real-time performance. To address these issues,
this work proposes DSTVS-SWE, a distributed and scalable framework for secure workflow execution across vehicular, edge, and cloud layers. The approach models workflows as Directed Acyclic Graphs and combines hierarchical edge–cloud scheduling with dynamic multi-layer trust evaluation using both direct and indirect metrics. The system was implemented in CloudSim and evaluated against the Multi-Agent Deep Deterministic Policy Gradient – Federated Intrusion Detection with Collaborative Cooperative Offloading using Deep Reinforcement Learning (MADDPG-Fed-IDCCO-DRL) benchmark using Montage workloads. Results show notable improvements, including significant reductions in processing time and energy consumption, along with higher throughput and lower latency. Overall, DSTVS-SWE offers a reliable and efficient solution for secure and scalable inter-VEC communication in IoV environments
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