A new publication was recently published in the domain of intelligent data management in IoT networks:
Kylafas, C., Kolomvatsos, K., ‘Probabilistic Clustering with Temporal Prediction for Adaptive Data Management in IoT Networks’, Computing, Springer, vol. 108, art. no. 168, 2026. https://doi.org/10.1007/s00607-026-01759-1
The paper proposes a clustering algorithm for IoT sensor networks that combines Gaussian Mixture Models for probabilistic cluster characterization with Hidden Markov Models for predicting cluster evolution over time. A top-k neighbor maintenance strategy keeps the computational cost near-linear, so the approach scales across different network sizes.