<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The characteristics of data aggregation with different network environments and dynamic changes in channel availability make some problems in IoT data aggregation. Therefore, this paper proposes an FMA-coverage model for algorithm design based on edge information. The FMA-coverage model includes the method of edge frequency, the method of primitive length (stroke), the texture energy metric of Laws and the method of fractal texture description. The FMA-coverage model can improve the network performance of IoT data aggregation. From the computational analysis, it can be seen that the security of data storage is only 17%. After the improvement of the fast matching algorithm, the security is up to 87%. After the network coding scheme, the IoT performance of data aggregation is up to 95%. It is important to note that, in this case, the required transmission volume in the network can be greatly reduced when the links are long. The IoT performance is up to 97% with the compression-aware scheme. By cross-sectional comparison, the IoT-based mobile model has the highest accuracy, with 98% accuracy of data aggregation. This paper extends the data aggregation mechanism by introducing fast-matching algorithms for device authentication and secure storage.</p>
</abstract>

The characteristics of data aggregation with different network environments and dynamic changes in channel availability make some problems in IoT data aggregation. Therefore, this paper proposes an FMA-coverage model for algorithm design based on edge information. The FMA-coverage model includes the method of edge frequency, the method of primitive length (stroke), the texture energy metric of Laws and the method of fractal texture description. The FMA-coverage model can improve the network performance of IoT data aggregation. From the computational analysis, it can be seen that the security of data storage is only 17%. After the improvement of the fast matching algorithm, the security is up to 87%. After the network coding scheme, the IoT performance of data aggregation is up to 95%. It is important to note that, in this case, the required transmission volume in the network can be greatly reduced when the links are long. The IoT performance is up to 97% with the compression-aware scheme. By cross-sectional comparison, the IoT-based mobile model has the highest accuracy, with 98% accuracy of data aggregation. This paper extends the data aggregation mechanism by introducing fast-matching algorithms for device authentication and secure storage.

Research on IoT data aggregation by fusing fast matching algorithms

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Research on IoT data aggregation by fusing fast matching algorithms"}

The characteristics of data aggregation with different network environments and dynamic changes in channel availability make some problems in IoT data...

Research on IoT data aggregation by fusing fast matching algorithms

Published Online: Sep 09, 2023

Page range: -

Received: Nov 04, 2022

Accepted: Mar 20, 2022

DOI: https://doi.org/10.2478/amns.2023.2.00305

Keywords
Internet of things, Data aggregation, Fast matching algorithm, FMA-coverage model, Network performance

© 2023 Congshi Jiang et al., published by Sciendo

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

Research on IoT data aggregation by fusing fast matching algorithms

Published Online: Sep 09, 2023

Page range: -

Received: Nov 04, 2022

Accepted: Mar 20, 2022

DOI: https://doi.org/10.2478/amns.2023.2.00305

KeywordsInternet of things, Data aggregation, Fast matching algorithm, FMA-coverage model, Network performance

© 2023 Congshi Jiang et al., published by Sciendo

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

Keywords
Internet of things, Data aggregation, Fast matching algorithm, FMA-coverage model, Network performance