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

<p>In the context of big data, traditional detection algorithms can no longer meet today’s needs. For traditional sensors, there are shortcomings such as long working times, high complexity, and high false detection rates. This paper proposes a new type of sensor and an algorithm for detecting anomalies in sensors based on Flink. The sensor network senses the monitoring targets in the monitoring area in real time and transmits them to the end users through wireless communication. Then, the fusion rule for target detection in sensor networks is studied. Then, the detection algorithm is computed using Flink and predicted using a sliding window and ARIMA model on the Flink platform. Then, the confidence intervals of the prediction results are calculated, and the outliers are evaluated. Finally, the K-Means++ algorithm is used for clustering and probability assessment of previously obtained outliers. Then, compared with traditional sensors, comparative analysis shows that the proposed method in this paper has some advantages in terms of both sensitivity (S) and quality factor (Q). The value of S is more than 320 than the 2D PhC sensor array. The value of Q is more than 5295 than the 2D PhC sensor array. This paper’s new sensor detection method is more diverse than the traditional method. The detection time is reduced. The detection accuracy is also improved compared to the traditional method. It can be summarized by comparison that the new sensor in this paper is more excellent than the traditional method of detection.</p>
</abstract>

In the context of big data, traditional detection algorithms can no longer meet today’s needs. For traditional sensors, there are shortcomings such as long working times, high complexity, and high false detection rates. This paper proposes a new type of sensor and an algorithm for detecting anomalies in sensors based on Flink. The sensor network senses the monitoring targets in the monitoring area in real time and transmits them to the end users through wireless communication. Then, the fusion rule for target detection in sensor networks is studied. Then, the detection algorithm is computed using Flink and predicted using a sliding window and ARIMA model on the Flink platform. Then, the confidence intervals of the prediction results are calculated, and the outliers are evaluated. Finally, the K-Means++ algorithm is used for clustering and probability assessment of previously obtained outliers. Then, compared with traditional sensors, comparative analysis shows that the proposed method in this paper has some advantages in terms of both sensitivity (S) and quality factor (Q). The value of S is more than 320 than the 2D PhC sensor array. The value of Q is more than 5295 than the 2D PhC sensor array. This paper’s new sensor detection method is more diverse than the traditional method. The detection time is reduced. The detection accuracy is also improved compared to the traditional method. It can be summarized by comparison that the new sensor in this paper is more excellent than the traditional method of detection.

Research on the Development of New Sensors and Sensor Detection Technology in the Context of Big Data

Hebei Key Laboratory of Seismic Disaster Instrument and Monitoring Technology

Applied Mathematics and Nonlinear Sciences

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

In the context of big data, traditional detection algorithms can no longer meet today’s needs. For traditional sensors, there are shortcomings such as long...

Research on the Development of New Sensors and Sensor Detection Technology in the Context of Big Data

Published Online: Aug 05, 2024

Page range: -

Received: Mar 23, 2024

Accepted: Jun 16, 2024

DOI: https://doi.org/10.2478/amns-2024-2134

Keywords
Big data, sensors, Flink, K-Means++ algorithm, Wireless communication

© 2024 Jiahui Wang, published by Sciendo

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

Research on the Development of New Sensors and Sensor Detection Technology in the Context of Big Data

Published Online: Aug 05, 2024

Page range: -

Received: Mar 23, 2024

Accepted: Jun 16, 2024

DOI: https://doi.org/10.2478/amns-2024-2134

KeywordsBig data, sensors, Flink, K-Means++ algorithm, Wireless communication

© 2024 Jiahui Wang, published by Sciendo

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

Keywords
Big data, sensors, Flink, K-Means++ algorithm, Wireless communication