Deep learning‐influenced joint vehicle‐to‐infrastructure and vehicle‐to‐vehicle communication approach for internet of vehicles

Mekala, M. S.; Patan, Rizwan; Kallam, Suresh; Ramana, Kadiyala; Yadav, Kusum; Alharbi, Ali O.

doi:10.1111/exsy.12815

Cited by 16 publications

(15 citation statements)

References 53 publications

(58 reference statements)

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“…The service allocation issue is treated as a N P-complete problem because the tasks are recursively assigned more than one time to machines. In Wang [5] and Mekala et al [6], the authors have concentrated on the same issue with proof of comprehensive research method, and it is a delicate mechanism because of the substantial number of services with various resources. In [7], intelligent offloading method (IOM) has been designed for effective service scheduling.…”

Section: Related Workmentioning

confidence: 99%

A DRL-Based Service Offloading Approach Using DAG for Edge Computational Orchestration

Mekala

Srivastava

Nain

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high required computational social service requests (SRs) in a time slot based on directed acyclic graph (DAG) is an N P-complete problem. Most state-of-art methods concentrate on the energy preservation of networks but neglect the resource sharing cost and dynamic subservice execution time (SET) during the computation and resource sharing. This article proposes a two-step deep reinforcement learning (DRL)-based service offloading (DSO) approach to diminish edge server costs through a DRL influenced resource and SET analysis (RSA) model. In the first level, the service and edge server cost is considered during service offloading. In the second level, the R-retaliation method evaluates resource factors to optimize resource sharing and SET fluctuations. The simulation results show that the proposed DSO approach achieves low execution costs by streamlining dynamic service completion and transmission time, server cost, and deadline violation rate attributes. Compared to the state-of-art approaches, Manuscript

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Section: Related Workmentioning

confidence: 99%

A DRL-Based Service Offloading Approach Using DAG for Edge Computational Orchestration

Mekala

Srivastava

Nain

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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“…erefore, in addition to focusing on the storage and application layers, data asset management should also consider how to manage and share data as a unique asset securely. One of the current methods is to use block chain technology to give data assets the characteristics of safe sharing, tamper resistance, and traceability [17,18].…”

Section: Data Resource Managementmentioning

confidence: 99%

Customizable Encryption Algorithms to Manage Data Assets Based on Blockchain Technology in Smart City

Al‐Dhlan

Alreshidi

Pervez

et al. 2022

Mathematical Problems in Engineering

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The adoption of blockchain technology can provide data asset management’s high security, privacy, and traceability. After a comprehensive investigation of the present blockchain-based data asset management mechanism, it was determined that it is only relevant to a portion of the blockchain system design. To address this issue, a new model of data asset management based on blockchain technology is being developed, which incorporates applications at all levels of the blockchain system. This model implements a network layer node authority control mechanism, a consensus layer consensus mechanism with customizable attributes, improved data query efficiency at the data layer by optimizing the structure and building indexes, intelligent data management, and smart contract layer management. Furthermore, at the transaction layer, sharing information encryption using customizable encryption algorithms is introduced. The experimental results reveal that, when compared to the traditional paradigm, the new blockchain-based data asset management strategy enhances the efficiency of on-chain data queries by 2.33 times.

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“…Logistic regression originated as a classification technique rather than a linear regression. Scikit-learn solves multiclass classification issues with a more appropriate logistic regression implementation technique [33][34][35].…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

A Supervised Learning Identification System for Prognosis of Breast Cancer

Rawat

Gulati

Kaur

et al. 2022

Mathematical Problems in Engineering

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Breast cancer is one of the most dangerous cancers, accounting for a large number of fatalities each year. It is the leading cause of mortality among women globally. It is getting a lot of interest in the scientific community because of its possible life-threatening danger. As a consequence, many machine learning methods (MLMs) have been modified to provide the best results for early diagnosis of this malignancy. Machine learning methods (MLMs) offer several beneficial implications in breast cancer, including early prognosis, detection, and diagnosis. Compared to traditional statistical analysis, machine learning methods (MLMs) have the capacity to improve the analysis of various health data, such as unstructured, complicated, and noisy data. With the demanding prevalence of breast cancer and the arrival of “data reformation,” it is thus imperative to mention the ethical consequences of machine learning (ML) on society and cancer care. It offers conclusively strong tools, smart methods, and efficient algorithms that can help in the prognosis of breast cancer. The focus of this review is on supervised techniques such as classification and regression that may be implemented and used for breast cancer data analysis. Some supervised learning methods like Naive Bayes, AdaBoost, and support vector machine are presented in this work in the early identification of breast cancer. These algorithms have been analyzed for their accuracy and efficiency using various assessment metrics and methods.

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Deep learning‐influenced joint vehicle‐to‐infrastructure and vehicle‐to‐vehicle communication approach for internet of vehicles

Cited by 16 publications

References 53 publications

A DRL-Based Service Offloading Approach Using DAG for Edge Computational Orchestration

A DRL-Based Service Offloading Approach Using DAG for Edge Computational Orchestration

Customizable Encryption Algorithms to Manage Data Assets Based on Blockchain Technology in Smart City

A Supervised Learning Identification System for Prognosis of Breast Cancer

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