Edge-Enabled Two-Stage Scheduling Based on Deep Reinforcement Learning for Internet of Everything

Zhou, Xiaokang; Liang, Wei; Yan, Ke; Liu, Weimin; Wang, Kevin I‐Kai; Ma, Jun; Jin, Qun

doi:10.1109/jiot.2022.3179231

Cited by 78 publications

(7 citation statements)

References 32 publications

(26 reference statements)

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“…It highlights challenges in blockchain adoption for EHR management and suggests solutions for efficient storage and sharing. The review emphasizes the importance of EHR interoperability for seamless healthcare services and evaluates healthcare ontologies and open standards for achieving semantic interoperability [4]. This article reviews research on knowledge discovery in cryptocurrency transactions, focusing on transaction tracing, collective user behaviours, and individual user behaviours.…”

Section: IImentioning

confidence: 99%

Enhancing Administrative Penalty Enforcement in Market Supervision through Block chain-based Control and Timeliness Assurance

Ratna

2024

IJRASET

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This study delves into the complexities of market supervision in China, aiming to explore the integration of blockchain technology into this regulatory landscape. It primarily focuses on tackling challenges related to data access control and timely enforcement of administrative penalties using blockchain. Leveraging its decentralized and immutable ledger system, blockchain offers a promising solution to enhance security and transparency across sectors. By applying blockchain to market supervision, the objective is to establish a robust platform for managing administrative penalties efficiently and accountably. To assess its viability, sophisticated software simulation techniques are employed. Rigorous analysis evaluates security protocols, data availability, and overall system performance. This assessment is vital for identifying vulnerabilities and ensuring compliance with China's regulatory standards. Additionally, the study recognizes the importance of advancing blockchain technology continually. Staying updated on emerging trends is crucial for optimizing system performance, bolstering data privacy, and ensuring legal compliance. By addressing these areas, the study aims to provide actionable insights for designing and implementing blockchain solutions in China's market supervision. Through collaborative efforts and knowledge sharing, it seeks to drive innovation and enhance transparency, efficiency, and accountability in the market environment

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Section: IImentioning

confidence: 99%

Enhancing Administrative Penalty Enforcement in Market Supervision through Block chain-based Control and Timeliness Assurance

Ratna

2024

IJRASET

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“…To ensure resource efficiency, Xu et al [58] propose an edge content caching method by service requirement prediction based on the deep spatio-temporal residual network. To minimize the execution time, Chen et al [59] also use the deep reinforcement learning method [60,61] to realize a distributed computation offloading strategy for intelligent connected vehicles. Another challenging problem is the energy cost, which can be solved by wireless power transfer effectively [62,63].…”

Section: Edge Computingmentioning

confidence: 99%

RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

Shen¹,

Zhang²,

Song³

2023

Computers, Materials &Amp; Continua

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With the rapid development of the internet of things (IoT), electricity consumption data can be captured and recorded in the IoT cloud center. This provides a credible data source for enterprise credit scoring, which is one of the most vital elements during the financial decision-making process. Accordingly, this paper proposes to use deep learning to train an enterprise credit scoring model by inputting the electricity consumption data. Instead of predicting the credit rating, our method can generate an absolute credit score by a novel deep ranking model-ranking extreme gradient boosting net (rankXGB). To boost the performance, the rankXGB model combines several weak ranking models into a strong model. Due to the high computational cost and the vast amounts of data, we design an edge computing framework to reduce the latency of enterprise credit evaluation. Specially, we design a two-stage deep learning task architecture, including a cloud-based weak credit ranking and an edge-based credit score calculation. In the first stage, we send the electricity consumption data of the evaluated enterprise to the computing cloud server, where multiple weak-ranking networks are executed in parallel to produce multiple weak-ranking results. In the second stage, the edge device fuses multiple ranking results generated in the cloud server to produce a more reliable ranking result, which is used to calculate an absolute credit score by score normalization. The experiments demonstrate that our method can achieve accurate enterprise credit evaluation quickly.

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“…Ethereum expands the functionality of the Bitcoin blockchain by introducing smart contracts, enabling a more complex application logic and providing programmability [8]. Blockchain has shown great promise in several fields, such as the IoT (Internet of Things) [9][10][11][12][13], finance, and government [14].…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Access and Timeliness Control for Administrative Punishment Market Supervision

Jiang

et al. 2023

IEEE Internet Things J.

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Administrative punishment is one of the most important ways of enforcing administrative law in the field of market supervision in China. However, at the current stage, the abuse of data access permission and the difficulty in managing timeliness in administrative punishment still remain unresolved, which hinders the legalization and standardization of the administrative punishment system. Inspired by blockchain, which is inherently traceable, tamper-proof, and transparent, we design a system, punishment supervisor (PEATS), which is suitable for administrative punishment and technically overcomes the defects of the traditional administrative punishment procedure. To prevent the abuse of data access permission, we innovatively introduce the ACG (authorization control gateway) to verify the access permission of users based on the records in the MSD (market supervision department) contract. To ensure the timeliness of the administrative punishment procedure, we design a special case contract that has the same status as the general case processing state of administrative punishment to track case progress on the blockchain. We experiment and evaluate PEATS in terms of functionality and performance and find that PEATS provides traceability, transparency, and timeliness assurance. In addition, PEATS has 80.9% of the throughput of a traditional server with no more than an additional 3% latency and at most 60 KB additional storage space per case.

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Edge-Enabled Two-Stage Scheduling Based on Deep Reinforcement Learning for Internet of Everything

Cited by 78 publications

References 32 publications

Enhancing Administrative Penalty Enforcement in Market Supervision through Block chain-based Control and Timeliness Assurance

Enhancing Administrative Penalty Enforcement in Market Supervision through Block chain-based Control and Timeliness Assurance

RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

Blockchain-Based Access and Timeliness Control for Administrative Punishment Market Supervision

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