智慧楼宇环境下基于数据压缩和改进灰狼算法的 边缘计算卸载方法.

In the edge computing environment of smart buildings, to minimize the overall system latency and energy consumption in resource-constrained and complex conditions, this paper proposed an edge computing offloading method based on data compression and improved gray wolf algorithm (CLGWO). Firstly, it used the differential dictionary encoding compression method to estimate the data compression rate and the overhead incurred by compression. It combined the Lévy flight algorithm and spiral asymptotic hunting method to enhance the global search capability of the grey wolf optimizer. Finally, it determined the optimal offloading scheme by combining the estimated compression effects with the improved grey wolf optimizer. Experimental results indicate that the CLGWO method reduces the overall latency and energy consumption of computing task offloading, thereby verifying its effectiveness and feasibility. This approach provides a new solution to the edge computing offloading problem in buildings. [ABSTRACT FROM AUTHOR]