Along with the ubiquitous deployment of IoT devices, requirements on sensing data computation and analysis increase rapidly. However, the traditional cloud-based architecture is no longer sustained the computation load from these tremendous IoT devices, which bring the paradox of delay tolerance and bandwidth insufficiency. Fortunately, the edge computing is emerged and incorporated with the IoT network. Meanwhile, new questions arises. When and how to select among edge computing servers, and also achieve a well balance between consumed energy, transmission delay and data privacy. In this paper, we consider the problem that how IoT devices allocate their computation loads among edge computing servers and their on-chip computation units, to balance energy efficiency and data privacy in physical layer. Firstly, the optimization function of IoT devices is derived which reflects the energy consumption, transmission delay and also privacy requirement; Secondly, the direct transmission scenario is analyzed, and optimal transmit power are derived with or without privacy factors; Thirdly, we extend the model to relay transmission scenario when edge computing servers are far away, and propose the relay selection algorithm for IoT devices; Finally, by extensive simulations, two main conclusions are verified: the energy consumption remains the same with data privacy protection, energy saved 54.9% on average using relay IoT devices compared to direct transmission case.

随着物联网设备的无处不在部署，对传感数据计算和分析的需求迅速增加。但是，传统的基于云的体系结构不再承受来自这些巨大的物联网设备的计算负荷，这带来了延迟容忍和带宽不足的悖论。幸运的是，边缘计算应运而生，并与物联网网络结合在一起。同时，出现了新的问题。何时以及如何在边缘计算服务器之间进行选择，并且还可以在能耗，传输延迟和数据隐私之间实现良好的平衡。在本文中，我们考虑了物联网设备如何在边缘计算服务器及其片上计算单元之间分配计算负载，以平衡物理层的能效和数据隐私性的问题。首先，推导了物联网设备的优化功能，该功能反映了能耗，传输延迟以及隐私要求。其次，分析了直接传输场景，并在有或没有隐私因素的情况下得出了最佳的传输功率。第三，将模型扩展到边缘计算服务器距离较远的中继传输场景，提出了物联网设备的中继选择算法。最后，通过广泛的模拟，验证了两个主要结论：与数据隐私保护相比，能耗保持不变，与直接传输的情况相比，使用中继物联网设备平均节省了54.9％的能源。在有或没有隐私因素的情况下得出最佳发射功率；第三，将模型扩展到边缘计算服务器距离较远的中继传输场景，提出了物联网设备的中继选择算法。最后，通过广泛的模拟，验证了两个主要结论：与数据隐私保护相比，能耗保持不变，与直接传输的情况相比，使用中继物联网设备平均节省了54.9％的能源。在有或没有隐私因素的情况下得出最佳发射功率；第三，将模型扩展到边缘计算服务器距离较远的中继传输场景，提出了物联网设备的中继选择算法。最后，通过广泛的仿真，验证了两个主要结论：与数据隐私保护相比，能耗保持不变，与直接传输的情况相比，使用中继物联网设备平均节省了54.9％的能源。