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 3 publications

(7 citation statements)

References 25 publications

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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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“…UAV throughput describes the transmitted amount of data over a communication link between two UAVs or among the UAV and GCS in a given period of time [82]. The throughput is calculated in terms of bits per second (bps) or bytes per second (Bps, "…”

Section: Throughputmentioning

confidence: 99%

A Survey on UAV Network for Secure Communication and Attack Detection: A focus on Q-learning, Blockchain, IRS and mmWave Technologies

2024

KSII TIIS

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Unmanned Aerial Vehicle (UAV) networks, also known as drone networks, have gained significant attention for their potential in various applications, including communication. UAV networks for communication involve using a fleet of drones to establish wireless connectivity and provide communication services in areas where traditional infrastructure is lacking or disrupted. UAV communication networks need to be highly secured to ensure the technology's security and the users' safety. The proposed survey provides a comprehensive overview of the current state-of-the-art UAV network security solutions. In this paper, we analyze the existing literature on UAV security and identify the various types of attacks and the underlying vulnerabilities they exploit. Detailed mitigation techniques and countermeasures for the protection of UAVs are described in this paper. The survey focuses on the implementation of novel technologies like Q-learning, blockchain, IRS, and mmWave. This paper discusses network simulation tools that range in complexity, features, and programming capabilities. Finally, future research directions and challenges are highlighted.

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“…This occurs for several reasons, the most significant of which are the Doppler shifts brought on by the interaction of drones and IoT nodes in the multipath channel. The delay and Doppler spectra in the channel are influenced by other factors, including the radiation pattern of the antenna and the dynamic changes in individual multipath propagation components (MPCs) [38].…”

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confidence: 99%

“…The channel between mobile entities in a flying fog-based IoT system has a continuous function impulse response, denoted by h(t), at which a signal is transmitted and processed. It is calculated as the aggregation of MPCs as follows [38]:…”

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confidence: 99%

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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 3 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

A Survey on UAV Network for Secure Communication and Attack Detection: A focus on Q-learning, Blockchain, IRS and mmWave Technologies

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

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