1061. Enhancing the reliability and accuracy of wireless sensor networks using a deep learning and blockchain approach with DV‑HOP algorithm for DDoS mitigation and node localizationBhupinder Kaur, Deepak Prashar, Leo Mršić, Ahmad Almogren, Ateeq Ur Rehman, Ayman Altameem, Seada Hussen, 2025, original scientific article Abstract: Wireless sensor networks (WSNs) are subject to distributed denial-of-service (DDoS) attacks that impact data dependability, mobility of nodes, and energy drain. The remedy to these challenges in this work is a solution based on deep learning integrated with a blockchain-aided distance-vector hop (DV-HOP) localization algorithm for reliable and secure node localization. Incorporating a blockchain ledger makes the network more trustworthy as it verifies usual and unusual system activities, whereas the DV-HOP algorithm mitigates localization inaccuracies and enhances node placement. The system is evaluated according to different performance measures like localization error, accuracy ratio, average localization error (ALE), probability of location, false positive rate (FPR), false negative rate (FNR), energy utilization, network stability, node failure rate, node recovery rate, and malicious node detection rate. Experimental results reveal improved security, accuracy, and efficiency with 17% FPR and 15% FNR, outperforming the conventional methods. This model enhances WSN performance in different environments via precise data transmission from the source to the destination. The results confirm that integrating deep learning with blockchain and DV-HOP increases network robustness, thus making WSNs more secure against security attacks while reducing energy consumption and localization accuracy. The proposed model presents a strong solution for real-world applications in wireless network environments.
Keywords: wireless network devices, DV-HOP algorithm, blockchain ledger, reliable network devices, deep learning
Published in DiRROS: 09.09.2025; Views: 309; Downloads: 154
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1063. A fuzzy hypergraph-based framework for secure encryption and decryption of sensitive messages : application in selecting center location to set up a private hospitalAnnamalai Meenakshi, Obel Mythreyi, Leo Mršić, Antonios Kalampakas, Sovan Samanta, 2025, original scientific article Keywords: encryption, decryption, fuzzy hypergraph, dual fuzzy hypergraph, secure network
Published in DiRROS: 09.09.2025; Views: 292; Downloads: 128
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1068. Magnetomechanical detachment of bacterial biofilms using anisotropic magnetic iron oxide nanochainsMatija Šavli, Manca Černila, Maja Caf, Abida Zahirović, Nika Zaveršek, Sebastjan Nemec, Spase Stojanov, Anja Klančnik, Jerica Sabotič, Slavko Kralj, Aleš Berlec, 2025, original scientific article Abstract: Bacterial biofilms attach to various surfaces and represent an important clinical and public health problem, as they are highly recalcitrant and are often associated with chronic, nonhealing diseases and healthcare-associated infections. Antibacterial agents are often not sufficient for their elimination and have to be combined with mechanical removal. Mechanical forces can be generated by actuating nonspherical (anisotropic) magnetically responsive nanoparticles in a rotating magnetic field. We have thus prepared anisotropic superparamagnetic nanochains in the size range of 0.5−1 μm by magnetically assembling several iron oxide nanoparticle clusters and coating them with a layer of silica with different shell morphologies: smooth, moderately rough, and highly rough. The silica surface was additionally functionalized with carboxylic groups to increase colloidal stability. The efficacy of the nanochains in biofilm removal was studied systematically with three different model nonpathogenic bacterial species Escherichia coli, Lactococcus lactis, and Pseudomonas fragi; two different magnetic field strengths; two stirring speeds; and two treatment durations. All bacterial species were engineered to express fluorescent proteins to enable quantification of biofilm removal by colony-forming unit count and fluorescence measurements. Nanochains removed >90% of Gram-negative E. coli and P. fragi with a stronger magnetic field, and <90% of Gram-positive L. lactis with a weaker magnetic field. Surface roughness of nanochains, duration, and stirring speed also affected removal, but the effect could not be generalized. In contrast to their effects on biofilms, the functionalized nanochains showed no toxicity to Caco-2 intestinal epithelial cells, regardless of whether magnetomechanical force was employed or not. In summary, we demonstrated that remotely controlled spatial movement of nanoparticles can generate sufficient mechanical forces to disperse attached biofilms while retaining safety in an epithelial cell model.
Keywords: bacterial biofilm, magnetomechanical detachment, magnetic nanoparticles, nanochains
Published in DiRROS: 09.09.2025; Views: 290; Downloads: 132
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