Intelligent Blockchain-Enabled Adaptive Collaborative Resource Scheduling in Large-Scale Industrial Internet of Things

Lin, Kai; Gao, Jian; Han, Guangjie; Wang, Haohua; Li, Chao

doi:10.1109/tii.2022.3169457

Cited by 11 publications

(3 citation statements)

References 21 publications

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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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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

View full text Add to dashboard Cite

show abstract

“…Due to the fact that the joint optimization of virtual network function (VNF) embedding and flow scheduling faces several challenges of a differentiated QoS guarantee, the coupling and externality of VNF embedding, and route selection conflicts, in [19], a multi-timescale VNF embedding and flow scheduling algorithm named NEWS was proposed to maximize the throughput while reducing the VNF embedding cost and energy consumption. In [20], the issue that the lack of interconnection and collaboration between devices leaded to poor timeliness and security in IIoT resource scheduling was found, and an intelligent blockchain-enabled adaptive collaborative resource scheduling method was proposed. It introduced blockchain and AI technology to support dynamic resource scheduling in untrustworthy environments.…”

Section: Throughput Optimization Strategy Based On New Fog Architecturementioning

confidence: 99%

Hybrid Services Collaborative Resource Scheduling Strategy towards Artificial Intelligence of Things

Li¹,

Yao²,

Ji³

et al. 2023

Applied Sciences

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The increasing number of Artificial Intelligence of Things (AIoT) devices at the edge layer brings serious challenges to the traditional access network architecture, which results in a decrease in data transmission due to different QoS requirements. To improve the QoS of the URLLC service and mMTC service in the AIoT, a Hybrid Services Collaborative Resource Scheduling Strategy (HSCRS) is proposed. First, a multi-layer collaborative resource scheduling framework for the AIoT hybrid services is designed based on the F-RAN. Then, a throughput weighting model for hybrid services is constructed to analyze the throughput characteristics of mMTC service and URLLC service. Furthermore, a sub-channel allocation and power control method is designed to solve the resource scheduling strategy of hybrid services. Experimental results show that the proposed method can largely improve the network throughput performance.

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“…In addition, two-stage task allocation due to task failure has also been adequately considered [8]. Resource scheduling, on the other hand, is an effective guarantee to ensure that these tasks can be carried out according to the established plan [9][10][11], In the context of Industry 4.0, there are higher requirements for resource scheduling.…”

Section: Introductionmentioning

confidence: 99%

Grid Operation and Inspection Resource Scheduling Based on an Adaptive Genetic Algorithm

Bingnan Tang,

Jing Bao,

Nan Pan

et al. 2024

Int. j. eng. technol. innov.

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Grid operation and inspection a key links to ensure the safe operation of the power system, which requires efficient task allocation and resource scheduling. To address this problem, this paper proposes a resource scheduling model for grid operation and inspection based on bi-level programming. Firstly, the O&I process is analyzed and defined as a combined optimization problem of the multiple traveling salesman problem (MTSP) and the job-shop scheduling problem (JSP). Secondly, a bi-level programming model of MTSP and JSP is established according to the characteristics of the problem. Finally, an adaptive genetic algorithm is used to solve the problem. The feasibility of the model and the advancement of the algorithm are verified through the simulation of real scenarios and a large number of tests, which provide strong support for the sustainable development of the power system.

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Intelligent Blockchain-Enabled Adaptive Collaborative Resource Scheduling in Large-Scale Industrial Internet of Things

Cited by 11 publications

References 21 publications

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

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

Hybrid Services Collaborative Resource Scheduling Strategy towards Artificial Intelligence of Things

Grid Operation and Inspection Resource Scheduling Based on an Adaptive Genetic Algorithm

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