Academic Journal
Change Point Monitoring in Wireless Sensor Networks Under Heavy-Tailed Sequence Environments.
| Title: | Change Point Monitoring in Wireless Sensor Networks Under Heavy-Tailed Sequence Environments. |
|---|---|
| Authors: | Wang, Liwen, Hu, Hongbo, Jin, Hao |
| Source: | Mathematics (2227-7390); Feb2026, Vol. 14 Issue 3, p523, 23p |
| Subject Terms: | Change-point problems, Wireless sensor networks, Outlier detection, Electronic data processing, Mathematical statistics |
| Abstract: | In the special case of a heavy-tailed sequence environment, change point monitoring in wireless sensor networks faces many serious challenges, such as high communication overhead, particularly sensitivity to sparse changes, and dependence on strict parameter assumptions. In order to solve these limitations, a distributed robust M-estimator-based change point monitoring (DRM-CPM) method is proposed. This method combines ratio statistics with sliding window technology so that in online detection, there is no need to know the distribution before and after changes in advance. A threshold-triggered communication strategy is introduced, where sensors exchange local statistics only when exceeding predefined thresholds, significantly reducing energy consumption. By means of theoretical analysis, the asymptotic characteristics of the statistics are confirmed, and the robustness of the algorithm to heavy-tail noise and unknown parameters is also proved. Simulation results show that the algorithm is better than the existing methods in terms of empirical size control, empirical power, and communication efficiency, particularly in the face of sparse variation or heavy-tailed data. This framework provides a scalable solution for real-time anomaly monitoring with non-Gaussian data characteristics in industrial and environmental applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Change Point Monitoring in Wireless Sensor Networks Under Heavy-Tailed Sequence Environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Liwen%22">Wang, Liwen</searchLink><br /><searchLink fieldCode="AR" term="%22Hu%2C+Hongbo%22">Hu, Hongbo</searchLink><br /><searchLink fieldCode="AR" term="%22Jin%2C+Hao%22">Jin, Hao</searchLink> – Name: TitleSource Label: Source Group: Src Data: Mathematics (2227-7390); Feb2026, Vol. 14 Issue 3, p523, 23p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Change-point+problems%22">Change-point problems</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+sensor+networks%22">Wireless sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+statistics%22">Mathematical statistics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the special case of a heavy-tailed sequence environment, change point monitoring in wireless sensor networks faces many serious challenges, such as high communication overhead, particularly sensitivity to sparse changes, and dependence on strict parameter assumptions. In order to solve these limitations, a distributed robust M-estimator-based change point monitoring (DRM-CPM) method is proposed. This method combines ratio statistics with sliding window technology so that in online detection, there is no need to know the distribution before and after changes in advance. A threshold-triggered communication strategy is introduced, where sensors exchange local statistics only when exceeding predefined thresholds, significantly reducing energy consumption. By means of theoretical analysis, the asymptotic characteristics of the statistics are confirmed, and the robustness of the algorithm to heavy-tail noise and unknown parameters is also proved. Simulation results show that the algorithm is better than the existing methods in terms of empirical size control, empirical power, and communication efficiency, particularly in the face of sparse variation or heavy-tailed data. This framework provides a scalable solution for real-time anomaly monitoring with non-Gaussian data characteristics in industrial and environmental applications. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/math14030523 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 523 Subjects: – SubjectFull: Change-point problems Type: general – SubjectFull: Wireless sensor networks Type: general – SubjectFull: Outlier detection Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Mathematical statistics Type: general Titles: – TitleFull: Change Point Monitoring in Wireless Sensor Networks Under Heavy-Tailed Sequence Environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Liwen – PersonEntity: Name: NameFull: Hu, Hongbo – PersonEntity: Name: NameFull: Jin, Hao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22277390 Numbering: – Type: volume Value: 14 – Type: issue Value: 3 Titles: – TitleFull: Mathematics (2227-7390) Type: main |
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