Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks

Cheng, Zhipeng; Liwang, Minghui; Chen, Ning; Huang, Lianfen; Guizani, Nadra; Du, Xiaojiang

doi:10.1016/j.dcan.2022.05.026

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…This scheme involves relaying MUs from overloaded resources to underloaded resources. A DRL-based MU association and dynamic resource allocation in UAV-enabled wireless networks are proposed in [25], where the authors deploy multiple UAVs in the environment to improve service resiliency. Multiple MUs are allowed to associate with UAVs jointly, and the UAV resources are dynamically allocated to the MUs using multi-agent DRL algorithms.…”

Section: Uav-assisted Resource Allocationmentioning

confidence: 99%

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JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network

Ahmad

Zhang

Khan

et al. 2023

Drones

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Providing robust communication services to mobile users (MUs) is a challenging task due to the dynamicity of MUs. Unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) are used to improve connectivity by allocating resources to MUs more efficiently in a dynamic environment. However, energy consumption and lifetime issues in UAVs severely limit the resources and communication services. In this paper, we propose a dynamic cooperative resource allocation scheme for MEC–UAV-enabled wireless networks called joint optimization of trajectory, altitude, delay, and power (JO-TADP) using anarchic federated learning (AFL) and other learning algorithms to enhance data rate, use rate, and resource allocation efficiency. Initially, the MEC–UAVs are optimally positioned based on the MU density using the beluga whale optimization (BLWO) algorithm. Optimal clustering is performed in terms of splitting and merging using the triple-mode density peak clustering (TM-DPC) algorithm based on user mobility. Moreover, the trajectory, altitude, and hovering time of MEC–UAVs are predicted and optimized using the self-simulated inner attention long short-term memory (SSIA-LSTM) algorithm. Finally, the MUs and MEC–UAVs play auction games based on the classified requests, using an AFL-based cross-scale attention feature pyramid network (CSAFPN) and enhanced deep Q-learning (EDQN) algorithms for dynamic resource allocation. To validate the proposed approach, our system model has been simulated in Network Simulator 3.26 (NS-3.26). The results demonstrate that the proposed work outperforms the existing works in terms of connectivity, energy efficiency, resource allocation, and data rate.

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Section: Uav-assisted Resource Allocationmentioning

confidence: 99%

“…This subsection provides the comparative analysis of the proposed JO-TADP scheme with the existing approaches such as JO-PUARA [24], EdgeUAV [38], and Multi-UAV [25] in terms of simulation metrics such as connectivity, energy efficiency, utility rate, data rate, delay time and resource allocation efficiency.…”

Section: Comparative Analysismentioning

confidence: 99%

JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network

Ahmad

Zhang

Khan

et al. 2023

Drones

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“…With the rapid advancement of technology, unmanned aerial vehicles (UAVs) have found numerous urban IoT (Internet of Things) applications in fields such as rescue operations [1], surveillance [2,3], edge computing [4,5], disaster area reconstruction [6,7], aerial base stations [8,9], intelligent transportation [10,11], wireless power transfer [12], environmental monitoring [13], and more [14,15]. According to market forecasts, the UAV market will experience a compound annual growth rate (CAGR) of 7.9%, expanding from USD 26.2 billion in 2022 to USD 38.3 billion in 2027 [16].…”

Section: Introductionmentioning

confidence: 99%

UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey

Yan,

Fu,

Lin

et al. 2023

Applied Sciences

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Unmanned aerial vehicles (UAVs) have gained significant popularity across various domains, but their proliferation also raises concerns about security, public safety, and privacy. Consequently, the detection and tracking of UAVs have become crucial. Among the UAV-monitoring technologies, those suitable for urban Internet-of-Things (IoT) environments primarily include radio frequency (RF), acoustic, and visual technologies. In this article, we provide a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems. Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security.

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“…UAVs have emerged as a promising solution to address these requirements, acting as base stations [17]. Due to the mobility of UAVs [18], their base stations can hover or fly to various locations in the air, expanding the wireless coverage area and providing support for a large number of connections [19]. Compared to a single-vehicle service approach, the collaborative transportation of UAVs and vehicles offers significant advantages.…”

Section: Introductionmentioning

confidence: 99%

A Reliable Delivery Logistics System Based on the Collaboration of UAVs and Vehicles

Li,

Zhu,

Tolba

et al. 2023

Sustainability

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In recent years, land–air collaborative logistics delivery is a promising distribution method. It combines the flexibility of unmanned aerial vehicles (UAVs) with the high payload capacity of vehicles, expanding the service range of UAVs while reducing carbon emissions. However, most existing research has focused on path planning and resource allocation for either UAVs or vehicles alone. Therefore, to address the shortcomings of the current research, this paper proposes an intelligent land–air collaboration delivery algorithm for trajectory optimization and resource scheduling. Subsequently, this paper develops a land–air collaboration reliable delivery logistics distribution system, showcasing the driving routes of vehicles and UAVs. Meanwhile, the mode of UAV–vehicle collaboration not only saves operating costs compared to traditional logistics delivery but also achieves energy conservation and emission reduction goals. During the specific design and implementation process of this platform, blockchain technology is integrated into the logistics delivery service to ensure data security and prevent tampering, making the system more efficient and reliable. Finally, through testing and verification of the system’s functionalities, its completeness is demonstrated.

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Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks

Cited by 8 publications

References 29 publications

JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network

JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network

UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey

A Reliable Delivery Logistics System Based on the Collaboration of UAVs and Vehicles

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