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ORIGINAL RESEARCH article

Front. Comput. Sci.

Sec. Networks and Communications

Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1698914

This article is part of the Research TopicResource Coordination and Joint Optimization in Cloud-Edge-End SystemsView all 4 articles

Adaptive Graph-Theoretic Localization of Radiation Sources via Real-Time Density-Aware Clustering for IoT

Provisionally accepted
Wei  ChenWei ChenZiSen  QiZiSen QiLei  JiangLei JiangQingWei  MengQingWei MengHua  XuHua Xu*
  • Equipment Management and Unmanned Aerial Vehicle College, Air Force Engineering University, Xi'an, China

The final, formatted version of the article will be published soon.

The increasing complexity of Internet of Things and modern battlefield electromagnetic environments poses significant challenges to radiation source localization, especially under electronic countermeasures, cross-density distributions, and iterative data updates. Existing methods based on fixed-parameter clustering or single geometric discrimination often fail to handle localization divergence caused by dynamic density variations. To overcome this limitation, this paper proposes an adaptive graph-theoretic localization method via real-time density-aware clustering, integrating dynamic density clustering, probabilistic model verification, and graph clique analysis. This approach enables real-time discrimination of potential noise during data density fluctuations and reconstructs trusted subsets for radiation source localization.During the dynamic clustering stage, an adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed to rapidly separate preliminary the potential noise from target clusters. Subsequently, Gaussian Mixture Model (GMM) is utilized for the secondary partitioning of ambiguous clusters, enhancing the accuracy of target identification. In the clique analysis phase, a probabilistic adjacency matrix is constructed based on the outputs of GMM. Through the application of maximum clique algorithms, consistent targets are effectively extracted from the adjacency matrix, enabling precise localization. Experimental results show that the proposed method improves localization accuracy by at least 70% in dynamic updating scenarios compared to conventional techniques, demonstrating strong practical applicability and scalability for real-world deployments.

Keywords: Radiation source localization, clustering, Maximum clique, Adaptation strategy, Internrt of Things

Received: 04 Sep 2025; Accepted: 03 Oct 2025.

Copyright: © 2025 Chen, Qi, Jiang, Meng and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Hua Xu, xu.hua@139.com

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