Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network

Chen, Xing; Liu, Guizhong

doi:10.3390/s22134738

Cited by 21 publications

(16 citation statements)

References 34 publications

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“…It represents a significant leap forward in mobile communication technology, offering faster data speeds, lower latency, increased connectivity, and improved support for a wide range of applications and devices [ 157 ]. In the research on some task offloading mechanisms, full consideration has been given to using the characteristics of the 5G network to optimize the transmission process of task data [ 86 , 87 , 141 , 94 , 99 , 105 , 124 , 125 , 149 , 158 , 159 ]. In addition, there are also a small number of research attempts to explore the advanced features of future networks based on 6G to build IoT task offloading mechanisms in MEC [ 111 ].…”

Section: Resultsmentioning

confidence: 99%

A comprehensive review on internet of things task offloading in multi-access edge computing

Dayong,

Bin Abu Bakar,

Isyaku

et al. 2024

Heliyon

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Section: Resultsmentioning

confidence: 99%

A comprehensive review on internet of things task offloading in multi-access edge computing

Dayong,

Bin Abu Bakar,

Isyaku

et al. 2024

Heliyon

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“…They have explored centralized training and decentralized execution, hence, each IOT device will be a decision making agent. Xing Chen et al [26] explore a federated DDPG solution for combined optimization of energy consumption and reduction in latency. The federated learning procedure ensures privacy of user data because only parameters of locally trained models are sent to central servers.…”

Section: Related Workmentioning

confidence: 99%

Actor Critic-based Multi Objective Reinforcement Learning for Multi Access Edge Computing

Khot,

Pai

et al. 2024

IJACSA

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In recent times, large applications that need near real-time processing are increasingly being used on devices with limited resources. Multi access edge computing is a computing paradigm that provides a solution to this problem by placing servers as close to resource constrained devices as possible. However, the edge device must consider multiple conflicting objectives, viz., energy consumption, latency, task drop rate and quality of experience. Many previous approaches optimize on only one objective or a fixed linear combination of multiple objectives. These approaches don't ensure best performance for applications that run on edge servers, as there is no guarantee that the solution obtained by these approaches lies on the paretofront. In this work, Multi Objective Reinforcement Learning with Actor-Critic model is proposed to optimize the drop rate, latency and energy consumption parameters during offloading decision. The model is compared with MORL-Tabular, MORL-Deep Q Network and MORL-Double Deep Q Network models. The proposed model outperforms all the other models in terms of drop rate and latency.

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“…RL was based on animal psychology research [ 22 ], where learning was based on reward and punishment. In RL, learning happens through repetition as well as a trial and error process that makes it a powerful approach to dynamic and unknown environments [ 23 ]. Due to the features of RL, this method was developed for distributed systems, one of which is multi-agent environments [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

Multi-Agent Credit Assignment and Bankruptcy Game for Improving Resource Allocation in Smart Cities

Yarahmadi

Shiri

Challenger

et al. 2023

Sensors

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In recent years, the development of smart cities has accelerated. There are several issues to handle in smart cities, one of the most important of which is efficient resource allocation. For the modeling of smart cities, multi-agent systems (MASs) can be used. In this paper, an efficient approach is proposed for resource allocation in smart cities based on the multi-agent credit assignment problem (MCA) and bankruptcy game. To this end, the resource allocation problem is mapped to MCA and the bankruptcy game. To solve this problem, first, a task start threshold (TST) constraint is introduced. The MCA turns into a bankruptcy problem upon introducing such a constraint. Therefore, based on the concept of bankruptcy, three methods of TS-Only, TS + MAS, and TS + ExAg are presented to solve the MCA. In addition, this work introduces a multi-score problem (MSP) in which a different reward is offered for solving each part of the problem, and we used it in our experiments to examine the proposed methods. The proposed approach is evaluated based on the learning rate, confidence, expertness, efficiency, certainty, and correctness parameters. The results reveal the better performance of the proposed approach compared to the existing methods in five parameters.

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Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network

Cited by 21 publications

References 34 publications

A comprehensive review on internet of things task offloading in multi-access edge computing

A comprehensive review on internet of things task offloading in multi-access edge computing

Actor Critic-based Multi Objective Reinforcement Learning for Multi Access Edge Computing

Multi-Agent Credit Assignment and Bankruptcy Game for Improving Resource Allocation in Smart Cities

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