Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Shrichandran, G.V.; Warrier, Gayathry S.; Vignesh, K.; Rajendran, A.; Qamar, Shamimul; Shuaib, Mohammed; Rajaram, A. Title A name given to the resource All-Optical Plasmonic Neurosensor for Self-Learning Anomaly Detection in Smart IoT Systems Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Plasmonics;Volume;21;Issue;2;pp.1683-1700 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11468-026-03339-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11468-026-03339-7</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028196469?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028196469?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Shrichandran G.V., Department of computer science and engineering, School of computer science engineering, SRM Institute of science and technology, Ramapuram, Tamil Nadu, Chennai, 600089, India; Warrier G.S., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bangalore, 560073, India; Vignesh K., Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, 626126, India; Rajendran A., Department of ECE, Karpagam College of Engineering, Tamil Nadu, India; Qamar S., Computer Science &amp; Engineering, Applied College, Dhahran Al Janoub Campus, King Khalid University, Abha, Saudi Arabia; Shuaib M., Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia; Rajaram A., Department of Electronics and Communication Engineering, E.G.S Pillay Engineering College, Tamilnadu, Nagapattinam, 611002, India Description An account of the resource An integrated plasmonic neurosensing platform is introduced to enable ultrafast, self-learning anomaly detection within next-generation Internet of Things (IoT) environments. The research attempts to design an all-optical plasmonic neurosensor that can monitor irregularities as well as at the same time learns in hardware without the aid of electronics. The big picture is to develop an ultra-fast energy-saving sensorial unit that can scale to large tissues of IoT network applications and, autonomously, adjusts to varying conditions. The most significant invention of the paper is that localized surface plasmon resonance (LSPR) nanostructures are proposed to combine both nonlinear optical memory-effect and physical learning in sensor plasmonic gap. The technique is a hybrid between FDTD/FEM electromagnetic modelling, nanoimprint based production of sub-20-nm bow-tie antennas, nonlinear optical modulation experimental studies, and scalability analysis on the network level. A simulated system determined the optimal bow-tie configuration that resonated at 817nm with a field enhancement of approximately 28x with gap dimensions of 10nm long. Fabricated devices attained resonance of 823nm with Q-factor of 18.7. A refractive-index modulation was achieved of 3.1 10? and overall shift of the resonance at 51nm of 50 cycles in optical learning. The IoT level testing had 94.6% anomaly-detection errors and 47 ps response time, whereas the scalability experiment enabled the growth of bandwidth linearly with WDM and 92% fabrication yield. These findings provide an answer to the consequences that will lead to ultra-dense self-learning photonic IoT designs. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. Subject The topic of the resource All-optical computation; Distributed IoT sensing; LSPR resonance tuning; Neuromorphic photonics; Photonic edge intelligence; Plasmonic neurosensor Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 15571955; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online