A QoE Based Trustable SDN Framework for IoT Devices in Mobile Edge Computing

Tahaei, Hamid; Ko, Kwangman; Seo, Wonjeong; Joo, Su-Chong

doi:10.1007/978-981-10-7605-3_183

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Wang et al [14] proposed a new DRL-based offloading framework, which can efficiently learn the offloading policy uniquely represented by a neural network. Hamid et al [15] proposed a new and trustable framework for MEC management systems with crucial security and authentication components by which it ensures the delivery of users' QoE. Yazdinejad et al [16] proposed a secure and energy-saving blockchain support architecture for the SDN controller of the IoT, making the blockchain suitable for IoT devices with limited resources.…”

Section: Sdn-based Mec Architecturementioning

confidence: 99%

Deep reinforcement learning-based resource allocation and seamless handover in multi-access edge computing based on SDN

Zhang

Luo

2021

Knowl Inf Syst

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With the access devices that are densely deployed in multi-access edge computing environments, users frequently switch access devices when moving, which causes the imbalance of network load and the decline of service quality. To solve the problems above, a seamless handover scheme for wireless access points based on perception is proposed. First, a seamless handover model based on load perception is proposed to solve the unbalanced network load, in which a seamless handover algorithm for wireless access points is used to calculate the access point with the highest weight, and a software-defined network controller controls the switching process. A joint allocation method of communication and computing resources based on deep reinforcement learning is proposed to minimize the terminal energy consumption and the system delay. A resource allocation model is based on minimizing terminal energy consumption, and system delay is built. The optimal value of task offloading decision and resource allocation vector are calculated with deep reinforcement learning. Experimental results show that the proposed method can reduce the network load and the task execution cost.

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Section: Sdn-based Mec Architecturementioning

confidence: 99%

Deep reinforcement learning-based resource allocation and seamless handover in multi-access edge computing based on SDN

Zhang

Luo

2021

Knowl Inf Syst

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“…For example, in [ 15 ], a QoS architecture for IoT that focused on the control-mechanism for transferring and translation of QoS was proposed to improve the QoS of IoT. In [ 16 ], a new and trustable framework for a Mobile Edge Computing (MEC) management/orchestration system with crucial security and authentication components by which it ensures the delivery of users’ QoE was given. Furthermore, in [ 17 ], a cooperative evolution approach for service composition under the restrictions of QoS was proposed to address the aim of distinguishing prospective services out of many “similar” services and identifying needed services with regard to the criteria of QoS.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Estimation of Service Level Based on Fuzzy Logic for Robustness in the Internet of Things

Jia

Hao

Zhang

et al. 2018

Sensors

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The Internet of things (IoT) technology is developing rapidly, and the IoT services are penetrating broadly into every aspect of people’s lives. As the large amount of services grows dramatically, how to discover and select the best services dynamically to satisfy the actual needs of users in the IoT service set, the elements of which have the same function, is an unavoidable issue. Therefore, for the robustness of the IoT system, evaluating the quality level of the IoT service to provide a reference for the users choosing the most appropriate service has become a hot topic. Most of the current methods just use some static data to evaluate the quality of the service and ignore the dynamic changing trend of the service performance. In this paper, an estimation mechanism for the quality level of the IoT service based on fuzzy logic is conducted to grade the quality of the service. Specifically, the comprehensive factors are taken into account according to the defined level changing rules and the effect of the service in the previous execution process, so that it can provide users with an effective reference. Experiments are carried out by using a simulated service set. It is shown that the proposed algorithm can estimate the quality level of the service more comprehensively and reasonably, which is evidently superior to the other two common methods, i.e., the estimating method by a Randomization Test (RT) and the estimating method by a Single Test in Steps (STS).

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