ScholarWorks@SOONCHUNHYANG UNIVERSITY: Kim, Seok hoon

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Kim, Seok hoon (김석훈)

College of Software Convergence (Department of Computer Software Engineering)

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IoT:9||softwaredefined networking:5||LPWAN:5||blockchain:4||mobile edge computing:4||federated learning:4||Internet of Things:4||deep reinforcement learning:4||machine learning:4||network functions virtualization:3||Mobile edge cloud:3||Data transmission:3||massive Internet of Things:2||data sharing:2||Adaptation models:2||Computational modeling:2||Deep reinforcement learning:2||Mobile network:2||virtual network functions:2||Resource management:2||access control:2||Deep learning:2||MEC:2||Data models:2||SoftwareDefined Networking:2||Machine learning:2||5G:2||traffic steering:2||DSRC:1||Data aggregation:1||Data transmission scheme:1||Deep Learning:1||Deep Qnetworks:1||Delaytolerant network:1||Disk cache:1||E2E:1||Energy efficiency:1||Ethernet backbone:1||Eye tracking:1||Failure detection:1||Federated Learning:1||Federated learning:1||Fronthaul:1||Healthcare:1||Heterogeneous:1||Heterogeneous networks:1||Intelligent system:1||Interference mitigation:1||Internet of Thing:1||Internet of Things (IoT):1||Internet of Vehicles:1||Internet of things:1||Interpretation:1||IoT (Internet of Things):1||IoT data transmission:1||IoT system:1||IoTcloud system:1||LPWA:1||LoRa backscatter:1||Location:1||Location information:1||Machine Learning:1||Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation:1||Massive IoT:1||Massive Traffic:1||Medium access control:1||Mobile Edge Computing:1||Mobile Sensor Network:1||Mobile Service:1||Mobile edge computing:1||Mobile sensor device:1||Mobile video:1||Multi download:1||Multimeida services:1||Musical score:1||NFS:1||Natural language:1||Network Access Control:1||Network Function Virtualization:1||Network Functions Virtualization:1||Network file system:1||Network function virtualization:1||Network virtualization:1||Object detection:1||P2P:1||Personalized service:1||Power monitoring:1||Prediction:1||Priority based transmission:1||Prioritybased data transmission:1||QoE:1||QoS:1||RF source range control:1||Radio resource management:1||Realtime processing:1||Realtime systems:1||Reinforcement learning:1||Resource Allocation:1||Sensor network:1||Sensors:1||Servers:1||Smart factory:1||Software Defined Network:1||Software defined networking:1||Synchronization:1||Tablet:1||Traffic control:1||Traffic management:1||Translation:1||Trusted P2P networking:1||ULL:1||WSN (Wireless Sensor Network):1||Wireless:1||activated device control:1||and communication throughput.:1||and provides highaccuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover:1||and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics:1||channel hopping:1||communicationefficient learning:1||computational resource efficiency:1||data compression:1||data distortion:1||data download:1||data processing:1||data transmission:1||datafiltering system:1||deep qlearning (DQL):1||delegated proof of node:1||distributed system:1||dutycycle:1||edge cloud:1||edge computing:1||edge intelligence:1||electronic medical records:1||embedded software:1||energy efficiency:1||especially for ultrareliable lowlatency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues:1||federated learning (FL):1||fog computing:1||fusion system:1||gateway:1||graph neural networks:1||group numbers:1||healthcare:1||hospital sensor network:1||invehicle network:1||including communication latency:1||infant monitoring:1||information hiding:1||intermediate node:1||internet of things:1||kmeans clustering:1||loT:1||low power wide area network:1||machine:1||management and orchestration:1||medical environment:1||multisink:1||multiple LiDARs:1||naive Bayesian classifier:1||namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the realtime IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations:1||network control:1||network operation cost:1||network slicing:1||offloading strategies:1||optimal gateway deployment:1||permission revocation:1||policy optimization:1||prediction:1||priorityaware orchestration:1||privacy:1||public IoT service:1||quality of service:1||quality of service (QoS):1||realtime image classifications:1||reliability:1||remote software update:1||routing optimization:1||selforganizing networks:1||service function chaining:1||softwaredefined edge virtualization:1||softwaredefined network:1||synchronization:1||there are required lightweight methods for handling realtime Internet of Things (IoT) communication perspectives:1||this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML:1||traffic control:1||transmission efficiency:1||tritraining:1||trusted connection:1||type communication:1||ultradense network:1||unmanned aerial vehicles:1||uplink transmission:1||vehicle gateway:1||wearable system:1||wide area communication:1||시선 추적:1||악보:1||안드로이드:1||어플리케이션:1||3D group management MAC:1||태블릿:1||AHP:1||AHRS algorithm:1||Adaptive data rate control:1||Android:1||Application:1||Artificial intelligence:1||Backscatter communication:1||Bayesian classifier:1||Big Data Computing:1||Big data:1||Bigdata:1||Bitcoin address:1||CIFS:1||CPS:1||Channel selection:1||Cloud Computing:1||Cloud storage:1||Coexistence problem:1||Coexistence problems:1||Cognitive radio:1||Computer architecture:1||Congestion identification:1||Connected car:1||Convergence computing:1||Convolutional neural networks (CNN):1||Covert channels:1||Covert communication:1||

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(31538) 22, Soonchunhyang-ro, Asan-si, Chungcheongnam-do, Republic of Korea+82-41-530-1114

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