Evaluating the Quality of Experience Performance Metric for UAV-Based Networks

Khakimov, Abdukodir; Mokrov, Evgeny; Poluektov, Dmitry; Samouylov, Konstantin; Koucheryavy, Andrey

doi:10.3390/s21175689

Cited by 2 publications

(1 citation statement)

References 25 publications

(25 reference statements)

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“…Also, this work can be considered a continuation of [17], which allows, by placing the MEC server on the UAV, not only to reduce the load on the servers of the video content provider, but also to increase the QoS and QoE parameters for the mobile users.…”

Section: Discussionmentioning

confidence: 99%

Development and analysis of models for service migration to the MEC server based on hysteresis approach

Poluektov

Khakimov

2022

Discrete and Continuous Models

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Online video services are among the most popular ways of content consumption. Video hosting servers have a very high load every day, which we propose to reduce by migrating the application with the video content in demand to the local Multi-access Edge Computing (MEC) server of the target. This makes it possible to improve the quality of services (QoS) provided to users by reducing the transmission delay. Therefore, an architecture has been proposed that allows, at times of increased demand for the same video content, to migrate the video service application to the edge servers of the network operator. To evaluate the performance of this approach, a mathematical model was developed in the form of a queuing system. The results of the numerical experiment make it possible to optimize the time of using local MEC servers to provide video content.

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Section: Discussionmentioning

confidence: 99%

Development and analysis of models for service migration to the MEC server based on hysteresis approach

Poluektov

Khakimov

2022

Discrete and Continuous Models

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Dynamic Offloading in Flying Fog Computing: Optimizing IoT Network Performance with Mobile Drones

Min,

Khakimov,

Ateya

et al. 2023

Drones

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The rapid growth of Internet of Things (IoT) devices and the increasing need for low-latency and high-throughput applications have led to the introduction of distributed edge computing. Flying fog computing is a promising solution that can be used to assist IoT networks. It leverages drones with computing capabilities (e.g., fog nodes), enabling data processing and storage closer to the network edge. This introduces various benefits to IoT networks compared to deploying traditional static edge computing paradigms, including coverage improvement, enabling dense deployment, and increasing availability and reliability. However, drones’ dynamic and mobile nature poses significant challenges in task offloading decisions to optimize resource utilization and overall network performance. This work presents a novel offloading model based on dynamic programming explicitly tailored for flying fog-based IoT networks. The proposed algorithm aims to intelligently determine the optimal task assignment strategy by considering the mobility patterns of drones, the computational capacity of fog nodes, the communication constraints of the IoT devices, and the latency requirements. Extensive simulations and experiments were conducted to test the proposed approach. Our results revealed significant improvements in latency, availability, and the cost of resources.

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Evaluating the Quality of Experience Performance Metric for UAV-Based Networks

Cited by 2 publications

References 25 publications

Development and analysis of models for service migration to the MEC server based on hysteresis approach

Development and analysis of models for service migration to the MEC server based on hysteresis approach

Dynamic Offloading in Flying Fog Computing: Optimizing IoT Network Performance with Mobile Drones

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