With the Internet of Things (IoT) and communication technologies are snowballing, various applications (e.g., e-health and face recognition) are generated by IoT devices (IoTDs). Nevertheless, these IoTDs generally have constrained computation resources. By offloading the IoT applications to be processed by the MEC servers, mobile edge computing (MEC) is envisioned as a promising and effective solution to address this problem. Meanwhile, security is a critical issue for task offloading in MEC. While plenty of studies have focused on IoT tasks offloading, many of them ignored the security issue. Moreover, many previous works ignored the resource allocation of MEC servers. In addition, as dynamic voltage scaling (DVS) technology is flexible in the design of MEC systems, we integrate this technology with task offloading. In this paper, the problem of IoT applications offloading in an MEC system is studied, whose goal is to minimize computation overheads measured by the task processing delay and energy consumption of IoTDs. The AES cryptographic technique is adopted to make sure that the security of the data of the offloaded tasks is guaranteed. An optimization problem of security-aware task offloading is formulated and solved by proposing an efficient resource-allocation scheme. Experimental results are performed to evaluate and confirm the performance of the proposed security model.

中文翻译：

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使用 DVS 的 MEC 系统中安全感知任务卸载的高效资源分配

随着物联网 (IoT) 和通信技术的滚雪球发展，物联网设备 (IoTD) 产生了各种应用程序（例如，电子健康和面部识别）。然而，这些 IoTD 通常具有受限的计算资源。通过卸载由 MEC 服务器处理的物联网应用程序，移动边缘计算 (MEC) 被设想为解决此问题的有前途且有效的解决方案。同时，安全性是 MEC 中任务卸载的关键问题。尽管大量研究都集中在物联网任务卸载上，但其中许多都忽略了安全问题。此外，之前的许多工作都忽略了 MEC 服务器的资源分配。此外，由于动态电压缩放 (DVS) 技术在 MEC 系统的设计中非常灵活，我们将这项技术与任务卸载相结合。在本文中，研究了 MEC 系统中 IoT 应用程序卸载的问题，其目标是最大限度地减少由 IoTD 的任务处理延迟和能耗衡量的计算开销。采用AES加密技术，保证卸载任务数据的安全性。通过提出一种高效的资源分配方案，制定并解决了安全感知任务卸载的优化问题。执行实验结果以评估和确认所提出的安全模型的性能。通过提出一种高效的资源分配方案，制定并解决了安全感知任务卸载的优化问题。执行实验结果以评估和确认所提出的安全模型的性能。通过提出一种高效的资源分配方案，制定并解决了安全感知任务卸载的优化问题。执行实验结果以评估和确认所提出的安全模型的性能。