Naive-LSTM enabled service identification of edge computing in power internet of things

作     者：Bai Huifeng Huo Chao Zhang Ganghong Yin Zhibin 

作者机构：Beijing SmartChip Microelectronics Technology Company Limited 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报(英文版))

年 卷 期：2024年

核心收录：

学科分类：080904[工学-电磁场与微波技术] 13[艺术学] 080802[工学-电力系统及其自动化] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 1305[艺术学-设计学（可授艺术学、工学学位）] 0810[工学-信息与通信工程] 081104[工学-模式识别与智能系统] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 081001[工学-通信与信息系统] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 

基　　金：supported by the National Key Research and Development Program of China (2021YFB2401304) 

摘      要：Great challenges and demands are presented by increasing edge computing services for current Power Internet of Things (Power IoT) to deal with serious diversity and complexity of these services. To improve the matching degree between edge computing and complex services， the services identification function is necessary for Power IoT. This article proposes a naive long short-term memory (Naive-LSTM) based services identification scheme of edge computing devices in the Power IoT， where the Naive-LSTM model makes full use of the most simplified structure and conducts discretization of the LSTM model. Moreover， the Naive-LSTM based services identification scheme can generate the probability output result to determine the task schedule police of Power IoT. After well learning operation， these Naive-LSTM classification engine module in edge computing devices of Power IoT can perform services identification， by obtaining key characteristics from various services traffics. Testing results show that the Naive-LSTM based services identification scheme is feasible and efficient in improving the edge computing ability of the Power IoT.