Secure Task Offloading in Blockchain-Enabled Mobile Edge Computing With Deep Reinforcement Learning

Samy, Ahmed; Elgendy, Ibrahim A.; Yu, Haining; Zhang, Weizhe; Zhang, Hongli

doi:10.1109/tnsm.2022.3190493

Cited by 36 publications

(9 citation statements)

References 45 publications

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“…Offloading tasks are vulnerable to security and privacy threats such as data manipulation, private data leaking, and redundant data. To overcome these issues, Samy et al 165 proposed a blockchain‐based framework for offloading tasks securely.…”

Section: Authentication and Traceability Based Offloading In Ecmentioning

confidence: 99%

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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SummaryIn today's era, Internet of Things (IoT) devices generate a vast amount of data, which is typically stored in the cloud environment and can be accessed by edge and IoT devices. The data generated by these devices are offloaded through computation offloading (CO) techniques in an edge/cloud computing environment. This paper conducts a systematic literature review (SLR) to review the state‐of‐the‐art CO techniques in edge computing (EC) in the context of energy, Quality of Service (QoS), authentication and traceability. In this SLR, the evolution of offloading techniques is analyzed in detail. A total of 138 articles, spanning from 2016 to 2023 (till date), have been classified into QoS, energy, and authentication and traceability‐based CO techniques. The optimization‐based techniques are the most preferred choices to improve the QoS and reduce energy in the research field of CO in EC. In addition, this paper explores the significant issues and challenges that require further investigation. For future research, energy, QoS, and data provenance for dependent or dynamic task offloading in mobility scenarios can be explored further.

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Section: Authentication and Traceability Based Offloading In Ecmentioning

confidence: 99%

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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“…Blockchain technology has recently gained significant attention, as it fits in almost everywhere while still providing its distinct features. While investigating the related work for this research, we found that most of the recent studies that combine Blockchain with DRL for cloud systems were mostly applied in the two fields of Internet-of-Things (IoT) and Industrial IoT [13,16,[27][28][29][30]. On the other hand, only a few studies have been completed combining Blockchain and DRL in the field of token-based task scheduling prediction on federated cloud systems and for participants' evaluation, which is our focus in this work.…”

Section: Literature Reviewmentioning

confidence: 99%

A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain

Younis,

Jambi,

Eassa

et al. 2024

Systems

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Intelligent systems are being proposed every day as advances in cloud systems are increasing. Mostly, the services offered by these cloud systems are dependent only on their providers, without the inclusion of services from other providers, specialized third parties, or individuals. This ‘vendor lock-in’ issue and the limitations related to offering tailored services could be resolved by allowing multiple providers or individuals to collaborate through intelligent task scheduling. To address such real-world systems’ limitations in provisioning and executing heterogeneous services, we employed Blockchain and Deep Reinforcement Learning here; the first is used for the token-based secured communication between parties, and the latter is to predict the appropriate task scheduling; hence, we guarantee the quality of not only the immediate decision but also the long-term. The empirical results show a high reward achieved, meaning that it accurately selected the candidates and adaptably assigned the tasks based on job nature and executors’ individual computing capabilities, with 95 s less than the baseline in job completion time to maintain the Quality of Service. The successful collaboration between parties in this tokenized system while securing transactions through Blockchain and predicting the right scheduling of tasks makes it a promising intelligent system for advanced use cases.

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“…Hammad et al fused CNN and SVM classifiers to enhance the feature extraction ability of the model and improved the activation function to enhance the convergence speed of the model [26,27]. Elgendy et al designed Q-learning and Deep-Q-Network algorithms to optimize the computational cost of the model [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Satellite cloud image segmentation based on lightweight convolutional neural network

Chen²,

Jin³

et al. 2023

PLoS ONE

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More than 50% of the images captured by optical satellites are covered by clouds, which reduces the available information in the images and seriously affects the subsequent applications of satellite images. Therefore, the identification and segmentation of cloud regions come to be one of the most important problems in current satellite image processing. Due to the complexity and variability of satellite images, especially when the ground is covered with snow, the boundary information of cloud regions is difficult to be accurately identified. The fast and accurate segmentation of cloud regions is a difficult point in the current research. We propose a lightweight convolutional neural network. Firstly, channel attention is used to optimize the effective information in the feature maps as a way to improve the network’s ability to extract semantic information at each scale. Then, we fuse high and low-dimensional feature maps to enhance the network’s ability to obtain small-scale semantic information. In addition, the feature aggregation module automatically adjusts the input multi-level feature weights to highlight the details of different features. Finally, we design the fully connected conditional random field to solve the problem that some noise in the input image and local minima during training is passed to the output layer resulting in the loss of edge features. Experimental results show that the proposed method achieves 0.9695 and 0.8218 for overall accuracy and recall, respectively, which has higher segmentation accuracy with the shortest time consumption compared with other state-of-the-art methods.

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Secure Task Offloading in Blockchain-Enabled Mobile Edge Computing With Deep Reinforcement Learning

Cited by 36 publications

References 45 publications

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain

Satellite cloud image segmentation based on lightweight convolutional neural network

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