Intelligent edge computing based on machine learning for smart city

To alleviate the huge computing pressure caused by the single mobile edge server computing mode as the amount of data increases, in this research, we propose a method to conduct calculations in a collaborative way. First, the method needs to consider how to encourage devices to cooperate when they are selfish. Second, the method answers the following question: how can collaborative computing be carried out when the device has the intention to cooperate? For example, how can calculations be conducted when there are extensibility and privacy problems in machine learning tasks? In view of the above challenges, a mobile edge server is taken as the focus, and the available resources around the mobile edge server are used for collaborative computing to further improve the computing performance of a mobile edge computing (MEC) system. The alternating direction multiplier method is used to solve the problem. First, the relevant techniques and theories of MEC, Stackelberg principle -subordinate game theory, and the alternating direction method of multipliers (ADMM) are introduced. Then, the problem description and model construction of distributed task scheduling in MEC and machine learning task-based device coordination computing are introduced, and machine learning is applied in the distributed task scheduling algorithm and distributed device coordination algorithm. Finally, the distributed task scheduling algorithm and distributed device coordination algorithm are tested by experiments. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

This research introduces a collaborative computing method to alleviate the computing pressure of single mobile edge server mode. The method encourages device cooperation and tackles the issue of device intention to cooperate, aiming to improve the computing performance in mobile edge computing systems.