Advanced Energy-Efficient Computation Offloading Using Deep Reinforcement Learning in MTC Edge Computing

Khan, Israr Ali; Tao, Xiaofeng; Rahman, G. M. Shafiqur; Rehman, Waheed Ur; Salam, Tabinda

doi:10.1109/access.2020.2991057

Cited by 27 publications

(18 citation statements)

References 32 publications

(46 reference statements)

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“…However, network resources may be over-consumed during the training and data transmission process. To solve the complex and dynamic control issues, a federated deep reinforcement learning-based [101,107,108,118,123,133,134,136,144,145,154,159,161,165,167,168,171], [174-177, 179, 183, 188, 192, 198, 200], [204,208,210,213,216,221,222,[225][226][227] [177-179, 181, 187, 188, 190, 196, 197, 200, 201, 203, 206, 207, 211, 214, 218, 223], [234,236,239,243,245,248,252,254,258,275,[277][278][279], [284, 286-292, 294, 297, 300], [305,306, Wireless, radio, antenna, signal [14,18,28…”

Section: Centralized and Federated Methodsmentioning

confidence: 99%

“…Referred publications Markov decision process [12,23,24,37,64,70,75,84,96,100,101,104,127,130,133,138,144,153,165,167,170,177,188,191,199], [203, 207, 211, 212, 214, 217, 220, 231, 252, 256-259, 263, 264, 272, 274, 281, 291, 309, 313, 320, 340, 343, 346], [369][370][371][372][373][374][375][376] Multiarmed bandit [61,66,102,198,351,377,378] Dynamic programming [16,19,27,52,68,70,84,90,93,…”

Section: Approachmentioning

confidence: 99%

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The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

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Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.

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Section: Centralized and Federated Methodsmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

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“…This section discussed on related works on different strategies on energy efficiency in Cloud-IoT environment. Khan et al [16] proposed a scheme for energy efficiency computation in edge computing systems. Deep reinforcement learning was employed for the energy conservation.…”

Section: Related Workmentioning

confidence: 99%

Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT

Mukherjee¹,

Ghosh²,

Pal³

et al. 2022

Computers, Materials &Amp; Continua

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Numerous Internet of Things (IoT) systems produce massive volumes of information that must be handled and answered in a quite short period. The growing energy usage related to the migration of data into the cloud is one of the biggest problems. Edge computation helps users unload the workload again from cloud near the source of the information that must be handled to save time, increase security, and reduce the congestion of networks. Therefore, in this paper, Optimized Energy Efficient Strategy (OEES) has been proposed for extracting, distributing, evaluating the data on the edge devices. In the initial stage of OEES, before the transmission state, the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT system. The initial stage is followed by the reconstructing and the processing state. The processed data is transmitted to the nodes through controlled deep learning techniques. The entire stage of data collection, transmission and data reduction between edge devices uses less energy. The experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive accuracy. Energy consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES scheme. Predictive accuracy is 97.5 percent, data performance rate was 97.65 percent, and execution time is 14.49 ms.

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“…For example, [306] uses DRL to improve online computing offloading for non-orthogonal multiple access, [307] and [308] use multi-agent RL for cooperative caching and task offloading in MEC, respectively. Authors in [309] propose DRL-based energy-efficient task offloading for machine type communication in the edge. Minimizing the per-bit energy consumption in 5G MECs using DNNs is discussed in [310].…”

Section: B Ml-based Optimization Of the Edge Infrastructurementioning

confidence: 99%

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

et al. 2020

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The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction. INDEX TERMS Communication networks, machine learning, physical layer, MAC layer, network layer, SDN, NFV, MEC, security, artificial intelligence (AI) I. INTRODUCTION T HE security, availability and performance demands of new applications, services and devices are increasing at a pace higher than anticipated. Real-time responsiveness in application areas like e-health, traffic, and industry requires communication networks to make real-time decisions autonomously. Such real-time autonomous decision-making requires that the network must react and learn from the environment, and control itself without human interventions. However, communication networks have until now taken a different path. Traditional networks rely on human involvement to respond manually to changes such as traffic variation, updates in network functions and services, security breaches, and faults. Human-machine interactions have resulted in network downtime [1], have opened the network to security vulnerabilities [2], and lead to many other challenges in current communication networks [3], [4]. The requirement for human interaction or manual configuration constitutes a major hindrance for a network to use its past experiences to adapt to changing requirements. The general idea is to predict the (future) behavior of a service, network segment, user or User Equipment (UE), and tune the network at run-time based on this information. For instance, the movement trajectory of a user can be predicted using

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Advanced Energy-Efficient Computation Offloading Using Deep Reinforcement Learning in MTC Edge Computing

Cited by 27 publications

References 32 publications

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

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