Traffic-Aware Fuzzy Classification Model to Perform IoT Data Traffic Sourcing with the Edge Computing

作     者：Huixiang Xu 

作者机构：College of Information EngineeringZhengzhou University of TechnologyZhengzhou450044China 

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

年 卷 期：2024年第78卷第2期

页      面：2309-2335页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：This research is funded by 2023 Henan Province Science and Technology Research Projects:Key Technology of Rapid Urban Flood Forecasting Based onWater Level Feature Analysis and Spatio-Temporal Deep Learning(No.232102320015) Henan Provincial Higher Education Key Research Project Program(Project No.23B520024) a Multi-Sensor-Based Indoor Environmental Parameters Monitoring and Control System 

主　　题：Internet of Things(IoT) edge computing traffic data self-learning fuzzy-learning 

摘      要：The Internet of Things(IoT)has revolutionized how we interact with and gather data from our surrounding *** devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable *** rapid proliferation of Internet of Things(IoT)devices has ushered in an era of unprecedented data generation and *** IoT devices,equipped with many sensors and actuators,continuously produce vast volumes of ***,the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant ***,transmitting all this data to a centralized cloud infrastructure for processing and analysis can be inefficient and impractical due to bandwidth limitations,network latency,and scalability *** paper proposed a Self-Learning Internet Traffic Fuzzy Classifier(SLItFC)for traffic data *** proposed techniques effectively utilize clustering and classification procedures to improve classification accuracy in analyzing network traffic *** addresses the intricate task of efficiently managing and analyzing IoT data traffic at the *** employs a sophisticated combination of fuzzy clustering and self-learning techniques,allowing it to adapt and improve its classification accuracy over *** adaptability is a crucial feature,given the dynamic nature of IoT environments where data patterns and traffic characteristics can evolve *** the implementation of the fuzzy classifier,the accuracy of the clustering process is improvised with the reduction of the computational *** can reduce computational time while maintaining high classification *** efficiency is paramount in edge computing,where resource constraints demand streamlined data ***,SLItFC’s performance advantages make it a compelling choice for organizations seeking to harness the potential of IoT data for real-time insig