A NB-IoT data transmission scheme based on dynamic resource sharing of MEC for effective convergence computing

Math, Sa; Tam, Prohim; Lee, Ah Young; Kim, Seokhoon

doi:10.1007/s00779-020-01449-5

Cited by 6 publications

(3 citation statements)

References 22 publications

(25 reference statements)

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“…e MEC server is integrated with MTG to empower radio serving capability for both computation and communication services. Furthermore, the adopted SDMEC in the fronthaul network enables MANO intelligent resources and services for vertical and horizontal network slicing (NS) [16][17][18]. e lightweight classification approaches based on machine learning (ML) models have been introduced for resourceconstraint application, requiring minimal computation and quick computing [19,20].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Offloading Decision and Resource Allocations Schemes Based on RNN/DQN for Reliability Assurance in Software-Defined Massive Machine-Type Communications

Math

Tam

Kim

et al. 2022

Security and Communication Networks

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The heterogeneous novelty applications present in the 5th generation (5G) era, including machine-type communication (mMTC), enhanced mobile broadband (eMBB) communication, and ultra-reliable low latency communication (URLLC), which required mobile edge computing (MEC) for local computation and services. The next-generation radio networking (NGRN) will rely on new radio (NR) with the millimeter-wavelength (mmWave) technologies that enable ultra-dense connectivities of the deployed heterogeneous mobile terminal gateways (MTG). However, the mission-critical mMTC applications will suffer from inadequate radio resource management and orchestration (MANO), which will diminish end-to-end (E2E) communication reliability in edge areas. This paper proposed optimal MTG selections and resource allocation (RA) based on the complementary between MTG loading prediction based on recurrent neural network-based long short-term memory (RNN-LSTM) and MTG loading adjustment based on the applied deep reinforcement learning (DRL) approaches, respectively. Furthermore, the RNN-LSTM enhances offloading and handover decisions with discrete-time predictions, while the DRL plays an essential role in adjusting the determined MTG during congestion situations. The proposed method contributed to remarkable outcomes in crucial performance metrics over reference approaches regarding computation and communication quality of service (QoS).

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Section: Related Workmentioning

confidence: 99%

Intelligent Offloading Decision and Resource Allocations Schemes Based on RNN/DQN for Reliability Assurance in Software-Defined Massive Machine-Type Communications

Math

Tam

Kim

et al. 2022

Security and Communication Networks

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“…Since cloud computing and data centers, the network service requests of end users are very important for quality of service (QoS) [1]. The increasing demand has accelerated the evolution of network architectures towards utilization.…”

Section: Introductionmentioning

confidence: 99%

Wireless Network Virtualization Resource Sharing Based on Dynamic Resource Allocation Algorithm

Ren

2022

Wireless Communications and Mobile Computing

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In recent years, with the continuous development of IT industry, network virtualization technology has gradually developed into a hotspot of research and application in the field of communication because IT allows the construction of customized virtual network (VN) on shared physical infrastructure. In particular, the proposal of wireless network virtualization for the development of the enhanced Internet. Because these technologies require effective algorithms named virtual network mapping (VNE) to instantiate virtual networks on the SN, on the basis of satisfying the isolation between wireless network slices, a resource allocation mechanism based on bilateral auction is proposed. At present, many researchers have proposed that network virtualization technology is a feasible way to solve the rigid Internet architecture. In the field of existing research focuses on the cable network virtualization, mainly related to the data center network and backbone, then, the side of the wireless access network virtualization the key technology research is still in its infancy, in order to accelerate the process of wireless skill for innovation and meet the demand of the difference of the emerging business, wireless network virtualization becomes the research hotspot and focus of academia and industry. However, no one has applied the dynamic resource allocation algorithm to wireless network virtualization resource sharing field. Here, this paper analyzes and summarizes the existing virtual network resource allocation schemes at home and abroad. The wireless virtual network resource allocation model and cross-domain virtual network resource allocation model are established, and the network centrality theory in social network and complex network is introduced to analyze the topology properties of virtual network and physical network, and two efficient virtual network resource allocation methods are proposed.

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“…ere exist abundant lightweight ML algorithms including Support Vector Machine (SVM), K-Mean, K-nearest Neighbour (KNN), Decision Tree (DT), and Random Forest (RF) which are appropriate for lightweight MEC devices that have constrained computing capacity. Subsequently, lightweight ML operates in simple computing machines and requires less computing time [21][22][23]. Moreover, SDN performs an influential position in next-generation 5G/6G fronthaul networks and intends for distributed Management and Orchestration (MNO) between the computing resources and user traffic [24].…”

Section: G Fronthaul Network Environmentsmentioning

confidence: 99%

Intelligent Media Forensics and Traffic Handling Scheme in 5G Edge Networks

Math

Tam

Kim

2021

Security and Communication Networks

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The 5th generation (5G) communications evolved with heterogeneous user terminals and applications. A convergence of Mobile Edge Computing (MEC) and Software-Defined Networks (SDN) delivers gigantic challenges and opportunities for enhancing computing resources and user Quality of Service (QoS) in fronthaul and backhaul networks. Due to the precipitous expansion of user media in the 5G epoch, efficient media forensics methods are mandatory for specifying and offering effective safety handling based on individual application requirements. According to the exponential increment of Heterogeneous Internet of Things (HetIoT) devices, gigantic traffic will generate through bottleneck 5G fronthaul gateways. 5G fronthaul network environments consist of inadequate resources to surmount the enormous user traffic and communications, QoS will be reduced when the existence of traffic congestion occurs. To confront the aforementioned issues, this paper proposed intelligent media forensics and traffic handling scheme for controlling the Uplink (UL) transmission according to the Downlink (DL) statuses. Support Vector Machine (SVM) algorithm was applied to conduct the media forensics and MEC server integrated into fronthaul gateways, in which gateways resources are divided into UL and DL. Caching technology will be a part of 5G environments, and DL will be utilized for traffic caching. So, it is compulsory to adjust the communication traffic according to UL/DL resource utilization and control the forwarding traffic which relies on resource availability. The experiment was conducted by using computer software, and the proposed scheme illustrated a noteworthy outperformance over the conventional method in terms of diverse significant QoS factors including reliability, latency, and communication throughput.

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A NB-IoT data transmission scheme based on dynamic resource sharing of MEC for effective convergence computing

Cited by 6 publications

References 22 publications

Intelligent Offloading Decision and Resource Allocations Schemes Based on RNN/DQN for Reliability Assurance in Software-Defined Massive Machine-Type Communications

Intelligent Offloading Decision and Resource Allocations Schemes Based on RNN/DQN for Reliability Assurance in Software-Defined Massive Machine-Type Communications

Wireless Network Virtualization Resource Sharing Based on Dynamic Resource Allocation Algorithm

Intelligent Media Forensics and Traffic Handling Scheme in 5G Edge Networks

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