Abstract The pervasive application of Internet of Things (IoT) has pushed the proliferation of edge computing. There exists potential in edge computing to satisfy the concerns of task delay, network bandwidth, battery endurance and data privacy. The superiority of edge computing is endorsed by the deployment of edge nodes to the device user’s proximity. Within partial capabilities of the cloud server, edge nodes efficaciously alleviate the burden on core network. Considering a mission critical system, enduring battery life is even more accentuated over task latency to maintain the device on operation. So in this paper, we put forward an energy efficient task offloading problem subject to the overall task delay based on Alternating Direction Method of Multipliers (ADMM) in a three-tier MEC network, equipped with both edge nodes and the cloud. The offloading choice is the approximate convergence with demanded precision concluded by persistent ADMM iterations. We also address the privacy disclosure concerns in the data transmission among IoT devices and apply differential privacy to the intricate optimization problem. More specifically, we associate privacy-preserving method with the exhaustive task offloading processes and iteration procedures. At last, simulations and experiments demonstrate the performance and convergence of our proposed algorithm.

中文翻译：

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物联网协同移动计算的隐私保护和节能任务卸载：一种 ADMM 方法

摘要 物联网（IoT）的广泛应用推动了边缘计算的普及。边缘计算存在满足任务延迟、网络带宽、电池耐用性和数据隐私等问题的潜力。边缘计算的优势得到了边缘节点部署到设备用户附近的认可。在云服务器的部分能力范围内，边缘节点有效地减轻了核心网的负担。考虑到任务关键型系统，持久的电池寿命比任务延迟更重要，以保持设备运行。因此，在本文中，我们提出了一个基于乘法器交替方向法（ADMM）在一个同时配备边缘节点和云的三层 MEC 网络中受总体任务延迟影响的节能任务卸载问题。卸载选择是通过持续 ADMM 迭代得出的具有所需精度的近似收敛。我们还解决了物联网设备之间数据传输中的隐私披露问题，并将差异隐私应用于复杂的优化问题。更具体地说，我们将隐私保护方法与详尽的任务卸载过程和迭代过程联系起来。最后，仿真和实验证明了我们提出的算法的性能和收敛性。我们将隐私保护方法与详尽的任务卸载过程和迭代过程联系起来。最后，仿真和实验证明了我们提出的算法的性能和收敛性。我们将隐私保护方法与详尽的任务卸载过程和迭代过程联系起来。最后，仿真和实验证明了我们提出的算法的性能和收敛性。