Delay and Energy Consumption Oriented UAV Inspection Business Collaboration Computing Mechanism in Edge Computing Based Electric Power IoT

作     者：SHAO Sujie LI Yi GUO Shaoyong WANG Chenhui CHEN Xingyu QIU Xuesong 

作者机构：State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Blockchain Research Department China Electronics Standardization Institute 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2023年第32卷第1期

页      面：13-25页

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] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 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 Natural Science Foundation of China (62071070) Test Bed Construction of Industrial Internet Platform in Specific Scenes (New Mode) 

主　　题：Energy consumption Heuristic algorithms Collaboration Autonomous aerial vehicles Delays Power systems Resource management 

摘      要：With the development of Internet of things(IoT) technology and smart grid infrastructure,edge computing has become an effective solution to meet the delay requirements of the electric power IoT. Due to the limitation of battery capacity and data transmission mode of IoT terminals, the business collaboration computing must consider the energy consumption of the terminals. Since delay and energy consumption are the optimization goals of two co-directional changes, it is difficult to find a business collaboration computing mechanism that simultaneously minimizes delay and energy *** paper takes the unmanned aerial vehicle(UAV) inspection business scenario in the electric power IoT based on edge computing as the representative, and proposes a two-stage business collaboration computing mechanism including resources allocation and task allocation to optimize the business delay and energy consumption of UAV by decoupling the complex correlation between resource allocation and task allocation. A steepest descent resource allocation algorithm is proposed. On the basis of resource allocation, an improved multiobjective evolutionary algorithm based on decomposition by dynamically adjusting the size of neighborhood and the cross distribution index is proposed as a task allocation algorithm to minimize energy consumption and business delay. Simulation results show that our algorithms can respectively reduce the business delay and energy consumption by more than6.4% and 9.5% compared with other algorithms.