Adaptive graph-theoretic localization of radiation sources via real-time density-aware clustering for IoT

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.