Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things

Chen, Ying; Li, Yong; Zhang, Yongchao; Wu, Yuan; Chen, Xin; Zhao, Lian

doi:10.1109/tii.2020.3028963

Cited by 168 publications

(74 citation statements)

References 24 publications

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“…In the end, the solution promoted the sharing of spectrum between different types of users. Chen et al [24] studied the joint power control and dynamic resource management of multi-access edge computing (MEC) in IIoT. The authors transformed this problem into Markov decision process (MDP) and used the dynamic resource management algorithm based on DRL to solve this process.…”

Section: A Related Work Of Industrial Internet Of Things Based On Dee...mentioning

confidence: 99%

Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT

Zhang¹,

Wang²,

Jiang³

et al. 2022

Preprint

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The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. According to the different requirement of end users, these data usually have high heterogeneity and privacy, while most of users are reluctant to expose them to the public view. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue, such that it has attracted extensive attention from academia and industry. As a new machine learning (ML) paradigm, federated learning (FL) has great advantages in training heterogeneous and private data. This paper studies the FL technology applications to manage IIoT equipment data in wireless network environments. In order to increase the model aggregation rate and reduce communication costs, we apply deep reinforcement learning (DRL) to IIoT equipment selection process, specifically to select those IIoT equipment nodes with accurate models. Therefore, we propose a FL algorithm assisted by DRL, which can take into account the privacy and efficiency of data training of IIoT equipment. By analyzing the data characteristics of IIoT equipments, we use MNIST, fashion MNIST and CIFAR-10 data sets to represent the data generated by IIoT. During the experiment, we employ the deep neural network (DNN) model to train the data, and experimental results show that the accuracy can reach more than 97%, which corroborates the effectiveness of the proposed algorithm.

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Section: A Related Work Of Industrial Internet Of Things Based On Dee...mentioning

confidence: 99%

Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT

Zhang¹,

Wang²,

Jiang³

et al. 2022

Preprint

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“…The internet of things (IoT) technology has entered into the next stage with the comprehensive combination of artificial intelligence (AI) and 5G network technology [1]. However, deploying a large number of IoT devices will face two technical challenges: (1) Many IoT-enabled devices are resource-constrained, with only insufficient storage space and limited computing power.…”

Section: Introductionmentioning

confidence: 99%

Federated Deep Reinforcement Learning for Online Task Offloading and Resource Allocation in WPC-MEC Networks

2022

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Mobile edge computing (MEC) is considered a more effective technological solution for developing the Internet of Things (IoT) by providing cloud-like capabilities for mobile users. This article combines wireless powered communication (WPC) technology with an MEC network, where a base station (BS) can transfer wireless energy to edge users (EUs) and execute computation-intensive tasks through task offloading. Traditional numerical optimization methods are time-consuming approaches for solving this problem in time-varying wireless channels, and centralized deep reinforcement learning (DRL) is not stable in large-scale dynamic IoT networks. Therefore, we propose a federated DRL-based online task offloading and resource allocation (FDOR) algorithm. In this algorithm, DRL is executed in EUs, and federated learning (FL) uses the distributed architecture of MEC to aggregate and update the parameters. To further solve the problem of the non-IID data of mobile EUs, we devise an adaptive method that automatically adjusts the FDOR algorithm's learning rate. Simulation results demonstrate that the proposed FDOR algorithm is superior to the traditional numerical optimization method and the existing DRL algorithm in four aspects: convergence speed, execution delay, overall calculation rate and stability in large-scale and dynamic IoT.INDEX TERMS Mobile edge computing, federated learning, deep reinforcement learning, online computing offload, wireless powered communication.

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“…Edge intelligence empowered by artificial intelligence (AI) is promising way to optimize the system performance in the field of the smart city IoT [12][13][14]. In [15], the power control and computing resource allocation optimization problem in Industrial Internet of Things MEC network was studied, a deep reinforcement learning (DRL)-based dynamic resource management algorithm was proposed to minimize the long-term average delay of the tasks. In [16], a content caching problem was investigated, and an actor-critic DRL-based algorithm was studied to maximize the cache bit rate.…”

Section: Introductionmentioning

confidence: 99%

Edge Intelligence Empowered Dynamic Offloading and Resource Management of MEC for Smart City Internet of Things

et al. 2022

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Internet of Things (IoT) has emerged as an enabling platform for smart cities. In this paper, the IoT devices’ offloading decisions, CPU frequencies and transmit powers joint optimization problem is investigated for a multi-mobile edge computing (MEC) server and multi-IoT device cellular network. An optimization problem is formulated to minimize the weighted sum of the computing pressure on the primary MEC server (PMS), the sum of energy consumption of the network, and the task dropping cost. The formulated problem is a mixed integer nonlinear program (MINLP) problem, which is difficult to solve since it contains strongly coupled constraints and discrete integer variables. Taking the dynamic of the environment into account, a deep reinforcement learning (DRL)-based optimization algorithm is developed to solve the nonconvex problem. The simulation results demonstrate the correctness and the effectiveness of the proposed algorithm.

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Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things

Cited by 168 publications

References 24 publications

Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT

Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT

Federated Deep Reinforcement Learning for Online Task Offloading and Resource Allocation in WPC-MEC Networks

Edge Intelligence Empowered Dynamic Offloading and Resource Management of MEC for Smart City Internet of Things

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