Resource-aware relay selection for inter-cell interference avoidance in 5G heterogeneous network for Internet of Things systems

Dao, Nhu‐Ngoc; Park, Min‐Ho; Kim, Joongheon; Paek, Jeongyeup; Cho, Sungrae

doi:10.1016/j.future.2018.03.037

Cited by 41 publications

(22 citation statements)

References 20 publications

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“…The mixed-integer programming of task admission is transformed into an integer programming problem with the optimal substructure by preadmitting resource-restrained devices. Utilizing network-assisted device-to-device collaboration, 28,29 Pu et al 30 proposed efficient task scheduling policies for energy-efficient and incentive-aware offloading in a device-to-device fogging framework. The policies adapt to various features of the task type, the user amount, and the task generation frequency.…”

Section: Literature Reviewmentioning

confidence: 99%

Joint energy and latency optimization for upstream IoT offloading services in fog radio access networks

Dao

Jang

et al. 2018

Trans Emerging Tel Tech

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Recently, the emergence of fog computing and big internet of things (IoT) data have been considered as the main representatives identifying fifth‐generation mobile networks. In fifth generation, cloudization is extended from the core to the access tiers, referred to as fog radio access networks. Fog radio access networks provide ultralow latency offloading services to a massive number of IoT devices in their proximity. In this paper, we propose a joint energy and latency optimization (JELO) scheme for upstream IoT offloading services in fog radio access networks. The JELO scheme controls the offloaded task assignment among fog‐enabled eNodeBs (fog nodes) with strict consideration of the systematic resources and specific characteristics of individual tasks. The joint objective function aims at red optimizing the energy red consumption and offload latency for entire networks. Simulation results demonstrate that the JELO scheme outperforms existing approaches in terms of the energy consumption and load balancing while maintaining IoT service satisfaction.

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Section: Literature Reviewmentioning

confidence: 99%

Joint energy and latency optimization for upstream IoT offloading services in fog radio access networks

Dao

Jang

et al. 2018

Trans Emerging Tel Tech

Self Cite

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“…This is because the service may not be served due to a lack of resources, the service may be blocked in the search for a device to be allocated or reallocated, or it may have its quality of service impaired due to the need to relocate the service. In recent years, many works have proposed different resource allocation mechanisms to allocate services on MEC to provide better services to users [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. However, these strategies do not take into account the service length or user mobility information.…”

Section: Introductionmentioning

confidence: 99%

RELIABLE: Resource Allocation Mechanism for 5G Network using Mobile Edge Computing

Pereira

Lieira

Silva

et al. 2020

Sensors

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Technological advancement is currently focused on the miniaturization of devices, and integrated circuits allow us to observe the increase in the number of Internet of Things (IoT) devices. Most IoT services and devices require an Internet connection, which needs to provide the minimum processing, storage and networking requirements to best serve a requested service. One of the main goals of 5G networks is to comply with the user’s various Quality of Service (QoS) requirements in different application scenarios. Fifth-generation networks use Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) concepts to achieve these QoS requirements. However, the computational resource allocation mechanisms required by the services are considered very complex. Thus, in this paper, we propose an allocation and management resources mechanism for 5G networks that uses MEC and simple mathematical methods to reduce the model complexity. The mechanism decides to allocate the resource in MEC to meet the requirements requested by the user. The simulation results show that the proposed mechanism provides a larger amount of services, leading to a reduction in the service lock number and as a reduction in the blocking ratio of services due to the accuracy of the approach and its load balancing in the process of resource allocation.

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“…Next, we give a brief review of the works related to our research. More related works on the efficient IoT support in 5G can be found in [17][18][19]. Yerrapragada and Kelley [17] investigated a perfect interference alignment scheme for multiple-input multiple-output systems and applied it to a 5G-enabled IoT architecture.…”

Section: Introductionmentioning

confidence: 99%

“…Yerrapragada and Kelley [17] investigated a perfect interference alignment scheme for multiple-input multiple-output systems and applied it to a 5G-enabled IoT architecture. Since intercell interference significantly degraded the performance of IoT communications, Dao et al [18] proposed a novel algorithm for finding the most appropriate pair of IoT terminal or its associated BS to provide a relay-assisted communication for the IoT terminal with poor signals in the inter-cell interference area. For IoT in cognitive 5G networks, the multiband cooperative spectrum sensing and resource allocation framework was presented in [19].…”

Section: Introductionmentioning

confidence: 99%

A game-theoretic learning approach to QoE-driven resource allocation scheme in 5G-enabled IoT

Dai

Zhang

Wei

et al. 2019

J Wireless Com Network

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To significantly promote Internet of Things (IoT) development, 5G network is enabled for supporting IoT communications without the limitation of distance and location. This paper investigates the channel allocation problem for IoT uplink communications in the 5G network, with the aim of improving the quality of experience (QoE) of smart objects (SOs). To begin with, we define a mean opinion score (MOS) function of transmission delay to measure QoE of each SO. For the sum-MOS maximization problem, we leverage a game-theoretic learning approach to solve it. Specifically, the original optimization problem is equivalently transformed into a tractable form. Then, we formulate the converted problem as a game-theoretical framework and define a potential function which has a near-optimum as the optimization objective. To optimize the potential function, a distributed channel allocation algorithm is proposed to converge to the best Nash equilibrium solution which is the global optimum of maximizing the potential function. Finally, numerical results verify the effectiveness of the proposed scheme.

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Resource-aware relay selection for inter-cell interference avoidance in 5G heterogeneous network for Internet of Things systems

Cited by 41 publications

References 20 publications

Joint energy and latency optimization for upstream IoT offloading services in fog radio access networks

Joint energy and latency optimization for upstream IoT offloading services in fog radio access networks

RELIABLE: Resource Allocation Mechanism for 5G Network using Mobile Edge Computing

A game-theoretic learning approach to QoE-driven resource allocation scheme in 5G-enabled IoT

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