<p>Mobile Edge Computing (MEC) is capable of inheriting cloud and Internet of Things (IoT) resources to the brim of the computational and communication networks. Conservatively, the MEC is outsourced from the IoT/ cloud platforms to Maximize the workflow and task completion abilities. However, due to outsourcing features, the security requirements for workflow scheduling and task completion rely on trusted device selection and high normalization. To satisfy these security demands, this article introduces a secure workflow scheduling algorithm using the knowledge learning concept. The proposed algorithm verifies the operative and failing device features under diverse allocation parameters. Based on the workflow completion lag, new scheduling or offloading decisions are Made. The decision support is provided by the knowledge learning is retains the previous operational status of the edge devices through stage-based updates. The stages for scheduling, classification, and offloading are updated periodically to Maximize the device selection and to reduce overhead in the process. Thus the consolidated process is adaptable to scheduling, offloading, and workflow completion regardless of the devices, allocation time, and device selection processes. This proposed algorithm is reliable in improving the normalized security by 14.17% by reducing the device selection overhead by 12.82% for the maximum allocation rates.</p>