Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT

作     者：Dibyendu Mukherjee Shivnath Ghosh Souvik Pal D.Akila N.Z.Jhanjhi Mehedi Masud Mohammed A.AlZain 

作者机构：Department of Computer Science and EngineeringBrainware UniversityBarasat700125India Department of Computer Science and EngineeringGlobal Institute of Management and TechnologyKrishnanagar741102India Department of Information TechnologySchool of Computing SciencesVels Institute of ScienceTechnology&Advanced StudiesChennai600117India School of Computer Science and EngineeringSCE Taylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Computer ScienceCollege of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information TechnologyCollege of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第7期

页      面：125-140页

核心收录：

学科分类：0710[理学-生物学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors would like to thank for the support from Taif University Researchers Supporting Project number(TURSP-2020/98) Taif University Taif Saudi Arabia 

主　　题：Energy efficient internet of things transmission performance cloud computing edge devices 

摘      要：Numerous Internet of Things(IoT)systems produce massive volumes of information that must be handled and answered in a quite short *** growing energy usage related to the migration of data into the cloud is one of the biggest *** computation helps users unload the workload again from cloud near the source of the information that must be handled to save time,increase security,and reduce the congestion of ***,in this paper,Optimized Energy Efficient Strategy(OEES)has been proposed for extracting,distributing,evaluating the data on the edge *** the initial stage of OEES,before the transmission state,the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT *** initial stage is followed by the reconstructing and the processing *** processed data is transmitted to the nodes through controlled deep learning *** entire stage of data collection,transmission and data reduction between edge devices uses less *** experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive *** consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES *** accuracy is 97.5 percent,data performance rate was 97.65 percent,and execution time is 14.49 ms.