Energy-efficient computation offloading with privacy preservation for edge computing-enabled 5g networks

Nowadays, due to the developments in wireless communication, the amount of data produced by mobile devices is increasing rapidly. The mobile devices can hardly handle these data immediately as they have limitations on their computing power. In edge computing, the computing tasks can be offloaded from the mobile devices to nearby edge nodes (ENs) for implementing. Combined with 5G networks, the computing tasks can be offloaded to the central units (CUs), enhanced into ENs, or the cloud infrastructure via distributed units (DUs) for processing. In this way, the above phenomenon will be effectively released. However, how to select the appropriate ENs for executing, aiming to keep a balance between the load balance and the energy consumption, is still a big problem waiting to be solved. In this paper, an optimization problem is formulated to improve the load balance and reduce the energy consumption of all the ENs for edge computing-enabled 5G networks while considering the privacy conflicts and time consumption. Then, an energy-efficient computation offloading method with privacy preservation, named ECOP, is proposed. Finally, experimental results and evaluations confirm our proposed method is feasible.