IoT Sensor Numerical Data Trust Model Using Temporal Correlation

Karmakar, Gour; Das, Rajkumar; Kamruzzaman, Joarder

doi:10.1109/jiot.2019.2957201

Cited by 31 publications

(29 citation statements)

References 18 publications

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“…Gpmbe called coverage i x m and parameters, can be denoted as i p . First, calculate the average value of all data in the calculation simulation result array, and the 0 a calculation formula is formula (1).…”

Section: Data Trust Analysis Methods Based On Average Deviation Algor...mentioning

confidence: 99%

“…0123 () n aaaaan =++++ L (1) In formula (1), the n th sample data is n a , n represents the sample data volume. After obtaining the average value of all sample data, the deviation of each sample data is calculated immediately , and the i λ calculation formula is formula (2).…”

Section: Data Trust Analysis Methods Based On Average Deviation Algor...mentioning

confidence: 99%

“…The model uses the time relationship to express the time correlation, and uses the average value of this value in the time window to develop a deep neural network model. Compared with contemporary relevance based methods, it has better accuracy and consistency [9] . Reddy V B and other scholars designed a trust mechanism to evaluate wireless sensor networks.…”

Section: Related Workmentioning

confidence: 99%

“…As a quantitative analysis method, computer simulation trust evaluation refers to the degree to which simulation users trust simulation results and specific simulation systems within specific requirements [1][2]. At present, the relevant research mainly focuses on the anti-jamming evaluation system, but it can only determine the degree of trust and correctness, and the final evaluation result has real value for practical applications.…”

Section: Introductionmentioning

confidence: 99%

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Computer Simulation Method for Data Trust Analysis Based on Average Deviation Algorithm

Zhi-qiang

2023

IEEE Access

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The computer simulation trust evaluation enhances the performance of the simulation system and promotes the recognition of the simulation system, but this technology still faces the problem of low calculation accuracy.This research proposes a data trust analysis method based on the improved average deviation degree algorithm . First, the average deviation degree and data trust degree are analyzed, and then the average deviation degree algorithm model based on the improved SecRPSO is constructed. Finally, the performance verification and application analysis of the model are carried out. The research shows that the prediction model established based on SecRPSO optimization algorithm has better fitting degree with the real load curve, and has better prediction accuracy and is relatively stable; Comparing the absolute mean relative error (MAPE) of three different algorithms, the MAPE of the proposed model is generally small, and the trend of the predicted results is close to the actual value, and the error fluctuates within the allowable range of engineering applications ; the data trust analysis method based on the average deviation algorithm can effectively calculate the expected threshold information of the data, and find out the tolerance threshold. Therefore, this method can be extended to various management data such as finance, finance, engineering, and environmental protection.

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Section: Data Trust Analysis Methods Based On Average Deviation Algor...mentioning

confidence: 99%

Section: Data Trust Analysis Methods Based On Average Deviation Algor...mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computer Simulation Method for Data Trust Analysis Based on Average Deviation Algorithm

Zhi-qiang

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Trust is not only imperative in fog computing and 5G, but it is also a persistent issue in the cloud [11]. Various studies of trust management are observed across multiple domains such as the Internet of Things (IoT) [12,13], Wireless Sensor Network (WSN) [14], Intelligent Transport Systems (ITS) [9] and cloud computing [15]. In cloud computing specifically, the authors in [15] presented a trust model specifically designed to assist cloud providers in taking decisions about inter-cloud VM migration based on reliability and reputation.…”

Section: Introductionmentioning

confidence: 99%