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Adaptive Data Transmission Method According to Wireless State in Long Range Wide Area Networks

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Computers, Materials & Continua 2020年第7期64卷 1-15页

作者： seokhoon kim Dae-Young kim Department of Computer Software Engineering Soonchunhyang UniversityAsan-si31538Korea School of Computer Software Daegu Catholic UniversityGyeongsan-si38430Korea

The Internet of Things(IoT)has enabled various intelligent services,and IoT service range has been steadily extended through long range wide area communication technologies,which enable very long distance wireless data ***-nodes are connected to a gateway with a single *** consume very low-power,using very low data rate to deliver *** long transmission time is consequently needed for each data packet transmission in long range wide area networks,data transmission should be efficiently ***,this paper proposes a multicast uplink data transmission mechanism particularly for bad network *** delay will be increased if only retransmissions are used under bad network ***,employing multicast techniques in bad network conditions can significantly increase packet delivery ***,retransmission can be reduced and hence transmission efficiency ***,the proposed method adopts multicast uplink after network condition *** predict network conditions,the proposed method uses a deep neural network *** proposed method performance was verified by comparison with uplink unicast transmission only,confirming significantly improved performance.

关键词： IoT wide area communication machine learning uplink transmission.

Network-Aided Intelligent Traffic Steering in 5G Mobile Networks

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Computers, Materials & Continua 2020年第10期65卷 243-261页

作者： Dae-Young kim seokhoon kim School of Computer Software Daegu Catholic UniversityGeyongsan38430Korea Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

Recently,the fifth generation(5G)of mobile networks has been deployed and various ranges of mobile services have been *** 5G mobile network supports improved mobile broadband,ultra-low latency and densely deployed massive *** allows multiple radio access technologies and interworks them for services.5G mobile systems employ traffic steering techniques to efficiently use multiple radio access ***,conventional traffic steering techniques do not consider dynamic network conditions *** this paper,we propose a network aided traffic steering technique in 5G mobile network architecture.5G mobile systems monitor network conditions and learn with network *** a machine learning algorithm such as a feed-forward neural network,it recognizes dynamic network conditions and then performs traffic *** proposed scheme controls traffic for multiple radio access according to the ratio of measured ***,it can be expected to improve traffic steering *** performance of the proposed traffic steering scheme is evaluated using extensive computer simulations.

关键词： Mobile network 5G traffic steering machine learning MEC

Data Aggregation-based Transmission Method in Ultra-Dense Wireless Networks

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Intelligent Automation & Soft Computing 2023年第1期35卷 727-737页

作者： Dae-Young kim seokhoon kim Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

As the Internet of Things(IoT)advances,machine-type devices are densely deployed and massive networks such as ultra-dense networks(UDNs)are *** devices attend to the network to transmit data using machine-type communication(MTC),whereby numerous,various are *** devices generally have resource constraints and use wireless *** this kind of network,data aggregation is a key function to provide transmission *** can reduce the number of transmitted data in the network,and this leads to energy saving and reducing transmission *** order to effectively operate data aggregation in UDNs,it is important to select an aggregation point *** total number of transmitted data may vary,depending on the aggregation point to which the data are ***,in this paper,we propose a novel data aggregation scheme to select the appropriate aggregation point and describe the data transmission method applying the proposed aggregation *** addition,we evaluate the proposed scheme with extensive computer *** performances in the proposed scheme are achieved compared to the conventional approach.

关键词： Data aggregation data transmission ultra-dense network machine-type communication Internet of Things

LoRa Backscatter Network Efficient Data Transmission Using RF Source Range Control

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Computers, Materials & Continua 2023年第2期74卷 4015-4025页

作者： Dae-Young kim SoYeon Lee seokhoon kim Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea Department of Software Convergence Soonchunhyang UniversityAsan31538Korea

Networks based on backscatter communication provide wireless data transmission in the absence of a power source.A backscatter device receives a radio frequency(RF)source and creates a backscattered signal that delivers data;this enables new services in battery-less domains with massive Internet-of-Things(IoT)*** is highly energy-efficient in the context of massive IoT ***,long-range(LoRa)backscattering facilitates large IoT services.A backscatter network guarantees timeslot-and contention-based ***-based transmission ensures data transmission,but is not scalable to different numbers of transmission *** contention-based transmission is used,collisions are *** reduce collisions and increase transmission efficiency,the number of devices transmitting data must be *** control device activation,the RF source range can be modulated by adjusting the RF source power during LoRa *** reduces the number of transmitting devices,and thus collisions and retransmission,thereby improving transmission *** performed extensive simulations to evaluate the performance of our method.

关键词： Backscatter communication LoRa backscatter RF source range control activated device control internet of things

Intelligent Real-Time IoT Traffic Steering in 5G Edge Networks

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Computers, Materials & Continua 2021年第6期67卷 3433-3450页

作者： Sa Math Prohim Tam seokhoon kim Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

In the Next Generation Radio Networks(NGRN),there will be extreme massive connectivity with the Heterogeneous Internet of Things(HetIoT)*** millimeter-Wave(mmWave)communications will become a potential core technology to increase the capacity of Radio Networks(RN)and enable Multiple-Input and Multiple-Output(MIMO)of Radio Remote Head(RRH)***,the challenging key issues in unfair radio resource handling remain unsolved when massive requests are occurring *** imbalance of resource utilization is one of the main issues occurs when there is overloaded connectivity to the closest RRH receiving exceeding *** handle this issue effectively,Machine Learning(ML)algorithm plays an important role to tackle the requests of massive IoT devices to RRH with its obvious capacity *** paper proposed a dynamic RRH gateways steering based on a lightweight supervised learning algorithm,namely K-Nearest Neighbor(KNN),to improve the communication Quality of Service(QoS)in real-time IoT *** supervises the model to classify and recommend the user’s requests to optimal RRHs which preserves higher *** experimental dataset was generated by using computer software and the simulation results illustrated a remarkable outperformance of the proposed scheme over the conventional methods in terms of multiple significant QoS parameters,including communication reliability,latency,and throughput.

关键词： Machine learning Internet of Things traffic steering mobile edge computing

Adaptive Partial Task Offloading and Virtual Resource Placement in SDN/NFV-Based Network Softwarization

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Computer Systems Science & Engineering 2023年第5期45卷 2141-2154页

作者： Prohim Tam Sa Math seokhoon kim Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

Edge intelligence brings the deployment of applied deep learning(DL)models in edge computing systems to alleviate the core backbone network *** setup of programmable software-defined networking(SDN)control and elastic virtual computing resources within network functions virtualization(NFV)are cooperative for enhancing the applicability of intelligent edge *** offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization,this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows,link delays,and allocatable bandwidth *** partial task offloading policy considered the DL-based recommendation to modify efficient virtual resource placement for minimizing the completion time and termination drop *** optimization problem of resource placement is tackled by a deep reinforcement learning(DRL)-based policy following the Markov decision process(MDP).The agent observes the state spaces and applies value-maximized action of available computation resources and adjustable resource allocation *** reward formulation primarily considers taskrequired computing resources and action-applied allocation *** defined policies of resource determination,the orchestration procedure is configured within each virtual network function(VNF)descriptor using topology and orchestration specification for cloud applications(TOSCA)by specifying the allocated *** simulation for the control rule installation is conducted using Mininet and Ryu SDN *** delay and task delivery/drop ratios are used as the key performance metrics.

关键词： Deep learning partial task offloading software-defined networking virtual machine virtual network functions

Multi-Agent Deep Q-Networks for Efficient Edge Federated Learning Communications in Software-Defined IoT

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Computers, Materials & Continua 2022年第5期71卷 3319-3335页

作者： Prohim Tam Sa Math Ahyoung Lee seokhoon kim Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Computer Science Kennesaw State UniversityMariettaGA 30060USA Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

Federated learning(FL)activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging ***,in large-scale heterogeneous Internet of Things(IoT)cellular networks,massive multi-dimensional model update iterations and resource-constrained computation are challenging aspects to be tackled *** paper introduces the system model of converging softwaredefined networking(SDN)and network functions virtualization(NFV)to enable device/resource abstractions and provide NFV-enabled edge FL(eFL)aggregation servers for advancing automation and ***-agent deep Q-networks(MADQNs)target to enforce a self-learning softwarization,optimize resource allocation policies,and advocate computation offloading *** gathered network conditions and resource states,the proposed agent aims to explore various actions for estimating expected longterm rewards in a particular state *** exploration phase,optimal actions for joint resource allocation and offloading decisions in different possible states are obtained by maximum Q-value ***-based virtual network functions(VNF)forwarding graph(VNFFG)is orchestrated to map VNFs towards eFL aggregation server with sufficient communication and computation resources in NFV infrastructure(NFVI).The proposed scheme indicates deficient allocation actions,modifies the VNF backup instances,and reallocates the virtual resource for exploitation *** neural network(DNN)is used as a value function approximator,and epsilongreedy algorithm balances exploration and *** scheme primarily considers the criticalities of FL model services and congestion states to optimize long-term *** results presented the outperformance of the proposed scheme over reference schemes in terms of Quality of Service(QoS)performance metrics,including packet

关键词： Deep Q-networks federated learning network functions virtualization quality of service software-defined networking

Intelligent Resource Allocations for Software-Defined Mission-Critical IoT Services

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Computers, Materials & Continua 2022年第11期73卷 4087-4102页

作者： Chaebeen Nam Sa Math Prohim Tam seokhoon kim Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

Heterogeneous Internet of Things(IoT)applications generate a diversity of novelty applications and services in next-generation networks(NGN),which is essential to guarantee end-to-end(E2E)communication resources for both control plane(CP)and data plane(DP).Likewise,the heterogeneous 5th generation(5G)communication applications,including Mobile Broadband Communications(MBBC),massive Machine-Type Commutation(mMTC),and ultra-reliable low latency communications(URLLC),obligate to perform intelligent Quality-of-Service(QoS)Class Identifier(QCI),while the CP entities will be suffered from the complicated massive HIOT ***,the existing management and orchestration(MANO)models are inappropriate for resource utilization and allocation in large-scale and complicated network *** cope with the issues mentioned above,this paper presents an adopted software-defined mobile edge computing(SDMEC)with a lightweight machine learning(ML)algorithm,namely support vector machine(SVM),to enable intelligent MANO for real-time and resource-constraints IoT applications which require lightweight computation ***,the SVM algorithm plays an essential role in performing QCI ***,the software-defined networking(SDN)controller allocates and configures priority resources according to the SVM classification ***,the complementary of SVM and SDMEC conducts intelligent resource MANO for massive QCI environments and meets the perspectives of mission-critical communication with resource constraint *** on the E2E experimentation metrics,the proposed scheme shows remarkable outperformance in key performance indicator(KPI)QoS,including communication reliability,latency,and communication throughput over the various powerful reference methods.

关键词： Mobile edge computing Internet of Things software defined networks traffic classification machine learning resource allocation

Development of Cloud Based Air Pollution Information System Using Visualization

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Computers, Materials & Continua 2019年第6期59卷 697-711页

作者： SangWook Han JungYeon Seo Dae-Young kim seokhoon kim HwaMin Lee Deptartment of Computer Science and Engineering Soonchungyang UniversityAsan31538Korea School of Information Technology Engineering Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Software and Engineering Soonchungyang UniversityAsan31538Korea

Air pollution caused by fine dust is a big problem all over the world and fine dust has a fatal impact on human *** there are too few fine dust measuring stations and the installation cost of fine dust measuring station is very *** this paper,we propose Cloud-based air pollution information system using *** measure fine dust,we have developed an inexpensive measuring device and studied the technique to accurately measure the concentration of fine dust at the user’s *** we have developed the smartphone application to provide air pollution *** our system,we provide collected data based analytical results through effective data *** system provides information on fine dust value and action tips through the air pollution information *** it supports visualization on the map using the statistical program *** user can check the fine dust statistics map and cope with fine dust accordingly.

关键词： Air pollution visualization R big data cloud clusters