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Graphs are suitable to model topology and data patterns in systems such as WSNs. To detect change, there is a need for graph comparison, a computationally demanding task difficult to run on constrained devices. For monitoring, the definition of normal patterns, and deviation from normal are required. In this contribution a flexible graph comparison method allowing monitoring of normal patterns and metrics providing measures of deviation from normal are proposed. In this manuscript, we apply the method to the system modelled by synthetic and random graphs. We demonstrate that the fingerprints of normal topology and data patterns can be acquired with the measures of deviation from normal. We discuss applicability of the method at the edge of WSNs.
Original languageEnglish
Title of host publication2022 11th Mediterranean Conference on Embedded Computing, MECO 2022
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)9781665468282
ISBN (Print)978-1-6654-6828-2
Publication statusPublished - 21 Jun 2022
EventMediterranean Conference on Embedded Computing: Cyber-Physical Systems and Internet-of-Things - Budva, Montenegro
Duration: 7 Jun 202210 Jun 2022

Publication series

Name2022 11th Mediterranean Conference on Embedded Computing, MECO 2022


ConferenceMediterranean Conference on Embedded Computing
Abbreviated titleMECO CPSIoT
Internet address

Bibliographical note

Publisher Copyright:
© 2022 IEEE.


  • Wireless Sensor Networks (WSNs)
  • Data pattern
  • Topology
  • Graph Comparison
  • Monitoring
  • Anomaly Detection
  • Cyber Security


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