The Internet of Things (IoT) is permeating our daily lives through continuous environmental monitoring and data collection. The promise of low latency communication, enhanced security, and efficient bandwidth utilization lead to the shift from mobile cloud computing to mobile edge computing. In this study, we propose an advanced deep reinforcement resource allocation and security-aware data offloading model that considers the constrained computation and radio resources of industrial IoT devices to guarantee efficient sharing of resources between multiple users. This model is formulated as an optimization problem with the goal of decreasing energy consumption and computation delay. This type of problem is non-deterministic polynomial time-hard due to the curse-of-dimensionality challenge, thus, a deep learning optimization approach is presented to find an optimal solution. In addition, a 128-bit Advanced Encryption Standard-based cryptographic approach is proposed to satisfy the data security requirements. Experimental evaluation results show that the proposed model can reduce offloading overhead in terms of energy and time by up to 64.7% in comparison with the local execution approach. It also outperforms the full offloading scenario by up to 13.2%, where it can select some computation tasks to be offloaded while optimally rejecting others. Finally, it is adaptable and scalable for a large number of mobile devices.

中文翻译：

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用于工业移动边缘计算中资源分配和安全感知数据卸载的高级深度学习

物联网 (IoT) 通过持续的环境监测和数据收集渗透到我们的日常生活中。低延迟通信、增强安全性和高效带宽利用的承诺导致从移动云计算向移动边缘计算的转变。在本研究中，我们提出了一种先进的深度强化资源分配和安全感知数据卸载模型，该模型考虑了工业物联网设备的受限计算和无线电资源，以保证多个用户之间的资源高效共享。该模型被表述为一个优化问题，其目标是降低能耗和计算延迟。由于维度诅咒挑战，这种类型的问题是非确定性多项式时间困难的，因此，提出了一种深度学习优化方法来寻找最佳解决方案。此外，提出了一种基于 128 位高级加密标准的加密方法来满足数据安全要求。实验评估结果表明，与本地执行方法相比，所提出的模型在能量和时间方面可以减少高达 64.7% 的卸载开销。它还优于完全卸载场景高达 13.2%，它可以选择一些要卸载的计算任务，同时以最佳方式拒绝其他计算任务。最后，它对大量移动设备具有适应性和可扩展性。实验评估结果表明，与本地执行方法相比，所提出的模型可以在能量和时间方面减少卸载开销高达 64.7%。它还优于完全卸载场景高达 13.2%，它可以选择一些要卸载的计算任务，同时以最佳方式拒绝其他计算任务。最后，它对大量移动设备具有适应性和可扩展性。实验评估结果表明，与本地执行方法相比，所提出的模型在能量和时间方面可以减少高达 64.7% 的卸载开销。它还优于完全卸载场景高达 13.2%，它可以选择一些要卸载的计算任务，同时以最佳方式拒绝其他计算任务。最后，它对大量移动设备具有适应性和可扩展性。