Joint Power and QoE Optimization Scheme for Multi-UAV Assisted Offloading in Mobile Computing

Wang, Qi; Gao, Ang; Hu, Yansu

doi:10.1109/access.2021.3055335

Cited by 29 publications

(19 citation statements)

References 38 publications

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“…It proposed efficient computation offloading and trajectory scheduling for multi-UAV but ignored processing time. In [31], the authors presented a method to maximize energy efficiency for user equipment transmission, and the position of UAVs should be carefully evaluated to ensure a high quality of experience for user equipment with various priorities. It optimized UAV under several constraints but with ideal assumptions which are not practical.…”

Section: Non Ai-based Solutionsmentioning

confidence: 99%

AI-Enabled UAV Communications: Challenges and Future Directions

et al. 2022

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Recently, unmanned aerial vehicles (UAVs) communications gained significant concentration as a talented technology for future wireless communications using its remarkable advantages and broad applicability. Furthermore, UAV networks' high complex configurations and designs encourage researchers to leverage relevant artificial intelligence (AI) techniques for better beyond fifth-generation (B5G)/sixth-generation (6G) services. This article summarizes AI-aided UAV solutions designated for forthcoming wireless networks. Besides, we deliver a comprehensive summary of machine learning (ML) approaches, including their applications and valuable contributions towards effective UAV network implementations, particularly advanced ML ones like bandits, federated learning (FL), meta-learning, etc. Finally, detailed UAV communication-related future research scopes and challenges is highlighted.INDEX TERMS Unmanned aerial vehicles (UAVs), artificial intelligence (AI), deep learning (DL), metalearning, federated learning (FL), and reinforcement learning (RL).

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Section: Non Ai-based Solutionsmentioning

confidence: 99%

AI-Enabled UAV Communications: Challenges and Future Directions

et al. 2022

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“…In [88], with the UAV acting as a mobile relay between users and the BS, UAV trajectory, power allocation, and user scheduling scheme are jointly optimized to minimize the total latency of all users. In [89], by utilizing the multiple input multiple output (MIMO) technique, users' tasks can be transmitted to multiple UAVs in parallel and next further offloaded to the BS by UAV relays. Then, a genetic based heuristic joint power and quality of experience (HJPQ) algorithm is proposed to minimize the weighted sum of energy consumption and delay.…”

Section: When Uavs Serve As Relaysmentioning

confidence: 99%

A comprehensive survey on aerial mobile edge computing: Challenges, state-of-the-art, and future directions

Song

Qin

Hao

et al. 2022

Computer Communications

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“…Gao et al [10] proposed a potential game combined multi-agent deep deterministic policy gradient (MADDPG) approach to optimize multiple UAVs' trajectory with the consideration of ground users' offloading delay, energy efficiency as well as obstacle avoidance system. In [11], with multi-UAV and ground mobile UEs in the system, heterogeneous performance requirement as well as fast-changing communication condition make the system more complicated. Wang et al proposed a heuristic joint power and quality of experience (HJPQ) algorithm where the user equipments' (GEs') offloading delay, MIMO channel, transmission power, as well as UAVs' placement are jointly optimized.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Task Offloading and Resource Allocation with Hybrid Energy Supply for UAV-Assisted Multi-Clouds

Zhou

et al. 2022

Preprint

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Cloud computing has emerged as a promising paradigm for meeting the growing resource demands of Internet of Things (IoT) devices. Meanwhile, with the popularity of mobile aerial base stations, Unmanned Aerial Vehicle (UAV) assisted cloud computing is essential for providing diversified service at areas without available infrastructure. However, it is difficult to meet the requirements of a number of IoT devices which distribute a large area through one single UAV cloud server, and thus multi-clouds have been applied in large-scale IoT environments. Due to the limited battery capacity of UAV, hybrid energy supply has been considered as an effective approach. How to allocate the computing resources and offload the tasks to the UAV-assisted clouds is a challenging task. In this paper, we study the trade-off between the energy consumption and system performance in a UAV-assisted multi-clouds system. Considering the transmission and execution cost, a dynamic optimization problem with the objective of minimizing the power consumption of UAVs with the constraint of queue stability is formulated, which is further decomposed into three sub-problems using stochastic optimization techniques. A collaborative task offloading and resources allocation algorithm (CTORAA) based on artificial intelligent technique is proposed to jointly determine task offloading and energy harvesting. We provide corresponding mathematical analysis showing that CTORAA can reach the arbitrary profit-stability trade-off. Finally, we conduct simulation experiments to validate the efficacy of our algorithm.

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Joint Power and QoE Optimization Scheme for Multi-UAV Assisted Offloading in Mobile Computing

Cited by 29 publications

References 38 publications

AI-Enabled UAV Communications: Challenges and Future Directions

AI-Enabled UAV Communications: Challenges and Future Directions

A comprehensive survey on aerial mobile edge computing: Challenges, state-of-the-art, and future directions

Collaborative Task Offloading and Resource Allocation with Hybrid Energy Supply for UAV-Assisted Multi-Clouds

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