Computation Offloading in Multi-UAV-Enhanced Mobile Edge Networks: A Deep Reinforcement Learning Approach

Li, Bin; Yu, Shiming; Su, Jian; Ou, Jianghong; Fan, Dahua

doi:10.1155/2022/6216372

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…The offloading mode has also an impact on the complexity and behavior of UAVs; table 5 shows how those factors are related. Table 5 shows that the solution of offloading problem is become more complex with multi-objective problems and multi-UAV network [149] [146] [147]. Also the offloading mode and formulated model is affects the complexity of solution algorithms so RL algorithms are the suitable solutions [145] [147].…”

Section: Markov Decision Process and Reinforcement Learningmentioning

confidence: 99%

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Baktayan¹,

Al-Baltah²

2022

Sustainable Engineering and Innovation

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The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network.

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Section: Markov Decision Process and Reinforcement Learningmentioning

confidence: 99%

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Baktayan¹,

Al-Baltah²

2022

Sustainable Engineering and Innovation

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“…Various researches have been made to improve the system whole performance of MEC networks by optimizing the offloading ratio, which decides the part of tasks to be calculated by the ENs. For example, the authors in [21][22][23][24] derived analytical expressions of offloading strategy for some typical MEC networks such as one-to-one and one-to-two MEC networks. For some more complicated MEC networks, some intelligent algorithms such as deep-Q networks (DQN) based deep reinforcement learning (DRL) algorithms can be applied to find a feasible solution to the offloading strategy, in order to help enhance the system performance by reducing the latency and EC during the communication and computation.…”

Section: Introductionmentioning

confidence: 99%

Outage Probability Analysis for UAV-Aided Mobile Edge Computing Networks

Zhang

Wang

et al. 2022

EAI Endorsed Trans Ind Net Intel Syst

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This paper studies one typical mobile edge computing (MEC) system, where a single user has some intensively calculating tasks to be computed by M edge nodes (ENs) with much more powerful calculating capability. In particular, unmanned aerial vehicle (UAV) can act as the ENs due to its flexibility and high mobility in the deployment. For this system, we propose several EN selection criteria to improve the system whole performance of computation and communication. Specifically, criterion I selects the best EN based on maximizing the received signal-to-noise ratio (SNR) at the EN, criterion II performs the selection according to the most powerful calculating capability, while criterion III chooses one EN randomly. For each EN selection criterion, we perform the system performance evaluation by analyzing outage probability (OP) through deriving some analytical expressions. From these expressions, we can obtain some meaningful insights regarding how to design the MEC system. We finally perform some simulation results to demonstrate the effectiveness of the proposed MEC network. In particular, criterion I can exploit the full diversity order equal to M.

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“…A novel approach for model-free DRL was suggested by [12], integrating an asynchronous advantage actor-critic (A3C) algorithm. This method aims to optimize offloading decisions efficiently.…”

mentioning

confidence: 99%

Secured Computation Offloading in Multi-Access Mobile Edge Computing Networks through Deep Reinforcement Learning

Rijal Abdullah,

Noorulsadiqin Azbiya Yaacob,

Anas A. Salameh

et al. 2024

Int. J. Interact. Mob. Technol.

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Mobile edge computing (MEC) has emerged as a pivotal technology to address the computational demands of resource-constrained mobile devices by offloading tasks to nearby edge servers. However, ensuring the security and efficiency of computation offloading in multiaccess MEC networks remains a critical challenge. This paper proposes a novel approach that leverages deep reinforcement learning (DRL) for secure computation offloading in multi-access MEC networks. The proposed framework utilizes DRL agents to dynamically make offloading decisions based on the current network conditions, resource availability, and security requirements. The agents learn optimal offloading policies through interactions with the environment, aiming to maximize task completion efficiency while minimizing security risks. To enhance security, the framework integrates encryption techniques and access control mechanisms to protect sensitive data during offloading. The proposed approach undergoes comprehensive simulations to assess its performance in terms of security, efficiency, and scalability. The results demonstrate that the DRL-based approach effectively balances the tradeoffs between security and efficiency, achieving robust and adaptive computation offloading in multi-access MEC networks. This study contributes to advancing the state-of-the-art in secure and efficient mobile edge computing systems, fostering the development of intelligent and resilient MEC solutions for future mobile networks.

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Computation Offloading in Multi-UAV-Enhanced Mobile Edge Networks: A Deep Reinforcement Learning Approach

Cited by 5 publications

References 29 publications

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Outage Probability Analysis for UAV-Aided Mobile Edge Computing Networks

Secured Computation Offloading in Multi-Access Mobile Edge Computing Networks through Deep Reinforcement Learning

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