Sensing Cloud Computing in Internet of Things: A Novel Data Scheduling Optimization Algorithm

Sun, Zeyu; Lv, Zhiguo; Li, Zhixian; Jia, Fuqian; Lai, Chunxiao

doi:10.1109/access.2020.2977643

Cited by 3 publications

(2 citation statements)

References 46 publications

(36 reference statements)

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“…Nowadays, more and more artificial intelligence products are listed and integrated into people's life. In the near future, artificial intelligence technology will have a significant impact on human society and human production and life [1,2]. Seeing the rapid development of information industry and Internet in the past, most of the credit depends on the rapid development of integrated circuits.…”

Section: Introductionmentioning

confidence: 99%

The Application of AI Technology in GPU Scheduling Algorithm Optimization

Yan

Liu

Shao

2022

Wireless Communications and Mobile Computing

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With the rapid development of integrated circuit technology, GPU computing capabilities continue to improve. Due to the continuous improvement and improvement of GPU programming capabilities, functions, and performance, GPUs have been widely used in the field of high-tech general-purpose computers. This article is aimed at studying the optimization of GPU scheduling algorithm based on AI technology. Through a combination of theoretical analysis and simulation experiments, the concepts of artificial intelligence technology and GPU scheduling are explained, and the impact of GPU architecture and GPGPU load on the energy efficiency of GPGPU is explained. On the basis of comprehensive analysis of GPU cluster characteristics, a new GA-TP scheduling algorithm based on genetic algorithm was designed, and based on the energy efficiency of the cluster, a simulation verification platform was built for the accuracy of simulation. Experimental results show that the acceleration rate of the GA-TP algorithm is significantly lower than that of the HEFT algorithm, the average acceleration rate is reduced by nearly 25%, and the scheduling efficiency of the GA-TP algorithm is higher.

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Section: Introductionmentioning

confidence: 99%

The Application of AI Technology in GPU Scheduling Algorithm Optimization

Yan

Liu

Shao

2022

Wireless Communications and Mobile Computing

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“…IoT systems can be victimized due to certain prevalent vulnerabilities. An intrusion detection system (IDS) is one of the crucial components of the cybercrime investigation process [3]. An IDS model scrutinizes the system activities to predict and detect attack patterns and to monitor every user's behavior in order to prevent any kind of security violations.…”

Section: Introductionmentioning

confidence: 99%

Design of an Intrusion Detection Model for IoT-Enabled Smart Home

et al. 2023

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Machine learning (ML) provides effective solutions to develop efficient intrusion detection system (IDS) for various environments. In the present paper, a diversified study of various ensemble machine learning (ML) algorithms has been carried out to propose design of an effective and time-efficient IDS for Internet of Things (IoT) enabled environment. In this paper, data captured from network traffic and real-time sensors of the IoT-enabled smart environment has been analyzed to classify and predict various types of network attacks. The performance of Logistic Regression, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine classifiers have been benchmarked using an open-source largely imbalanced dataset 'DS2OS' that consists of 'normal' and 'anomalous' network traffic. An intrusion detection model ''LGB-IDS'' has been proposed using the LGBM library of ML after validating its superiority over other algorithms using ensemble techniques and on the basis of majority voting. The performance of the proposed intrusion detection system is suitably validated using certain performance metrics of machine learning such as train and test accuracy, time efficiency, error-rate, true-positive rate (TPR), and false-negative rate (FNR). The experimental results reveal that XGB and LGBM have almost equal accuracy, but the time efficiency of LGBM is much better than RF, and XGB classifiers. The main objective of the present paper is to propose a design of an efficient intrusion detection model with high accuracy, better time efficiency, and reduced false alarm rate. The experimental results show that the proposed model achieves an accuracy of 99.92% and the time efficiency comes to be much higher than other prevalent algorithms-based models. The threat detection rate is greater than 90% and less than 100%. Time complexity of LGBM is also very much low as compared to other ML algorithms.INDEX TERMS Machine learning classification algorithms, ensemble classifiers, gradient boosting algorithms, light gradient boosting machines (LGBM), and intrusion detection systems (IDS).Open Access funding provided by 'Università degli Studi di Enna "KORE"' within the CRUI CARE Agreement

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Self-supervised anomaly pattern detection for large scale industrial data

Tang

Zeng

et al. 2023

Neurocomputing

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Sensing Cloud Computing in Internet of Things: A Novel Data Scheduling Optimization Algorithm

Cited by 3 publications

References 46 publications

The Application of AI Technology in GPU Scheduling Algorithm Optimization

The Application of AI Technology in GPU Scheduling Algorithm Optimization

Design of an Intrusion Detection Model for IoT-Enabled Smart Home

Self-supervised anomaly pattern detection for large scale industrial data

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