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<p>In order to improve the ability of micromechanical sensors to update data-matching nodes in real-time and speed up the establishment of a neighbor relationship between nodes, this paper proposes a personalized recommendation algorithm for micromechanical sensors based on the cloud model to improve the stability and real-time performance of micromechanical sensors. The algorithm uses the service attribute values of the cloud computing model and cloud clustering method to set feature term labels and establish a cloud service similarity matrix so as to meet the user-matched manufacturing service requirements. The intra-class clustering technique is applied to measure the clustering effect, and the evaluation function of each clustering number is calculated on the basis of considering the time sequence to determine the best clustering result to complete the personalized recommendation path for micromechanical sensors. To verify the application effect of the personalized recommendation algorithm for micromechanical sensors based on the cloud model, experiments are conducted. The results show that the recommendation algorithm in this paper can always control the node energy consumption below 2.5×103, and the average discovery delay is stable between 41-43 seconds. And the sensor response time is 12.1 seconds, and the average absolute deviation value is 0.23, which is nearly 1.3 times smaller than 0.53 and 0.52 of the collaborative recommendation algorithm and hybrid recommendation algorithm. It can be seen that the recommendation algorithm in this paper solves the problem of excessive neighbor discovery delay in the communication process of micromechanical sensors and effectively improves the personalized recommendation performance of micromechanical sensors.</p>
</abstract>

In order to improve the ability of micromechanical sensors to update data-matching nodes in real-time and speed up the establishment of a neighbor relationship between nodes, this paper proposes a personalized recommendation algorithm for micromechanical sensors based on the cloud model to improve the stability and real-time performance of micromechanical sensors. The algorithm uses the service attribute values of the cloud computing model and cloud clustering method to set feature term labels and establish a cloud service similarity matrix so as to meet the user-matched manufacturing service requirements. The intra-class clustering technique is applied to measure the clustering effect, and the evaluation function of each clustering number is calculated on the basis of considering the time sequence to determine the best clustering result to complete the personalized recommendation path for micromechanical sensors. To verify the application effect of the personalized recommendation algorithm for micromechanical sensors based on the cloud model, experiments are conducted. The results show that the recommendation algorithm in this paper can always control the node energy consumption below 2.5×103, and the average discovery delay is stable between 41-43 seconds. And the sensor response time is 12.1 seconds, and the average absolute deviation value is 0.23, which is nearly 1.3 times smaller than 0.53 and 0.52 of the collaborative recommendation algorithm and hybrid recommendation algorithm. It can be seen that the recommendation algorithm in this paper solves the problem of excessive neighbor discovery delay in the communication process of micromechanical sensors and effectively improves the personalized recommendation performance of micromechanical sensors.

Research on personalized recommendation algorithm for micromechanical sensors based on cloud model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In order to improve the ability of micromechanical sensors to update data-matching nodes in real-time and speed up the establishment of a neighbor relationship...

Research on personalized recommendation algorithm for micromechanical sensors based on cloud model

Online veröffentlicht: 28. Apr. 2023

Seitenbereich: -

Eingereicht: 02. Mai 2022

Akzeptiert: 07. Sept. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00080

Schlüsselwörter
Micromechanical sensors, Cloud clustering methods, Recommendation algorithms, Cloud computing models, Intra-class clustering techniques

© 2023 Baojun Geng, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on personalized recommendation algorithm for micromechanical sensors based on cloud model

Online veröffentlicht: 28. Apr. 2023

Seitenbereich: -

Eingereicht: 02. Mai 2022

Akzeptiert: 07. Sept. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00080

SchlüsselwörterMicromechanical sensors, Cloud clustering methods, Recommendation algorithms, Cloud computing models, Intra-class clustering techniques

© 2023 Baojun Geng, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Micromechanical sensors, Cloud clustering methods, Recommendation algorithms, Cloud computing models, Intra-class clustering techniques