D2D-Assisted Computation Offloading for Mobile Edge Computing Systems with Energy Harvesting

Li, Molin; Chen, Tong; Zeng, Jiaxin; Zhou, Xiaobo; Li, Keqiu; Qi, Heng

doi:10.1109/pdcat46702.2019.00028

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Their experimental results demonstrated that for different types of task graphs, the algorithm achieved an optimization performance of 99.1%, while significantly reducing the computational complexity compared to existing optimization methods. Li et al [13] proposed a dynamic task offloading algorithm based on Lyapunov optimization to solve the problems of computational offloading and energy harvest transfer in device-to-device architectures. They verified the effectiveness of their algorithm through extensive simulation experiments, which proved that their approach reduced the average task execution time by approximately 50% compared to the baseline algorithm.…”

Section: Related Studiesmentioning

confidence: 99%

A Multiagent Meta-Based Task Offloading Strategy for Mobile-Edge Computing

Ding

Luo

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

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Task offloading in mobile edge computing (MEC) improves the efficacy of mobile devices in terms of computing performance, data storage, and energy consumption by offloading computational tasks to edge servers. Efficient task offloading can leverage MEC technology to reduce task processing latency and energy consumption. By integrating the reasoning ability and machine intelligence of the cognitive computing architecture, such as SOAR and ACT-R, reinforcement learning (RL) algorithms have been applied to resolve the task offloading in MEC. To solve the problem that conventional Deep RL (DRL) algorithms cannot adapt to dynamic environments, this paper proposed a task offloading scheduling strategy which combined multiagent reinforcement learning and meta-learning. In order to make the two actions of charging time and offloading strategy fully considered at the same time, we implemented a learning network of two agent on a mobile device. To efficiently train the policy network, we proposed a first order approximation method based on clipped surrogate objective. Finally, the experiments are designed with variety of the number of subtasks, transmission rate, and edge server performance, and the results show that the MRL-based strategy has the overwhelming overall performance and can be quickly applied in various environments with good stability and generalization.

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Section: Related Studiesmentioning

confidence: 99%

A Multiagent Meta-Based Task Offloading Strategy for Mobile-Edge Computing

Ding

Luo

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

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“…Li et al [24] studied the D2D-assisted task offloading problem with EH in an MEC system and aimed to minimize the execution time of each task. Some existing works have considered single-user scenarios, while others have focused on energy consumption in MEC systems.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply

Chen

Zhao

et al. 2023

Tsinghua Sci. Technol.

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Mobile edge computing (MEC), as a new distributed computing model, satisfies the low energy consumption and low latency requirements of computation-intensive services. The task offloading of MEC has become an important research hotspot, as it solves the problems of insufficient computing capability and battery capacity of Internet of things (IoT) devices. This study investigates task offloading scheduling in a dynamic MEC system. By integrating energy harvesting technology into IoT devices, we propose a hybrid energy supply model. We jointly optimize local computing, offloading duration, and edge computing decisions to minimize system cost. On the basis of stochastic optimization theory, we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME. DTOME can make task offloading decisions by weighing system cost and queue stability. We quote dynamic programming theory to obtain the optimal task offloading strategy. Simulation results verify the effectiveness of DTOME, and show that DTOME entails lower system cost than two baseline task offloading strategies.

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“…Recently, there has been growing interest in using a D2D communication link to offload the computing task to a nearby mobile device. It is worth noting that some excellent work has been devoted to examine the performance of D2D offloading in MEC systems [13]- [15]. These studies have demonstrated that D2D-aided computing offloading can not only be used to ensure low latency and high data transmission rate, but also to make up the problem of MEC server shortage.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin Assisted Task Offloading for Aerial Edge Computing and Networks

Liu

Tan

et al. 2022

IEEE Trans. Veh. Technol.

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Considering the user mobility and unpredictable mobile edge computing (MEC) environments, this paper studies the intelligent task offloading problem in unmanned aerial vehicle (UAV)-enabled MEC with the assistance of digital twin (DT). We aim at minimizing the energy consumption of the entire MEC system by jointly optimizing mobile terminal users (MTUs) association, UAV trajectory, transmission power distribution and computation capacity allocation while respecting the constraints of mission maximum processing delays. Specifically, double deep Q-network (DDQN) algorithm stemming from deep reinforcement learning is first proposed to effectively solve the problem of MTUs association and UAV trajectory. Then, the closed-form expression is employed to handle the problem of transmission power distribution and the computation capacity allocation problem is further addressed via an iterative algorithm. Numerical results show that our proposed scheme is able to converge and significantly reduce the total energy consumption of the MEC system compared to the benchmark schemes.

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D2D-Assisted Computation Offloading for Mobile Edge Computing Systems with Energy Harvesting

Cited by 6 publications

References 10 publications

A Multiagent Meta-Based Task Offloading Strategy for Mobile-Edge Computing

A Multiagent Meta-Based Task Offloading Strategy for Mobile-Edge Computing

Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply

Digital Twin Assisted Task Offloading for Aerial Edge Computing and Networks

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