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 Mary, S. A. Sahaaya Arul; Prasad, K.D.V.; Raj, S. Dinakar; Sathyapriya, S.; Lalitha, R.V.S.; Rao, G. Siva Nageswara Title A name given to the resource Real-Time Data Fusion Algorithm for Multi-Modal Environmental Sensor Networks Using Kalman Filtering and IoT Integration 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 Analytical Letters; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1080/00032719.2026.2615824" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/00032719.2026.2615824</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029404632?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029404632?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 Mary S.A.S.A., Department of AIML &amp; Data Science, School of Engineering and Technology, CHRIST University, Bangalore, India; Prasad K.D.V., Faculty (Research), Symbiosis Institute of Business Management, Hyderabad, Symbiosis International (Deemed University), Pune, India; Raj S.D., Department of Electrical and Electronics Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India; Sathyapriya S., Assistant Professor in Information Technology, K. Ramakrishnan College of Engineering, Trichy, India; Lalitha R.V.S., Department of CSE, Aditya University, Surampalem, India; Rao G.S.N., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India Description An account of the resource Fusion of heterogeneous, noisy, and asynchronous multimodal data streams is essential to environmental sensor networks, given the computational, memory, and energy constraints of IoT devices. This paper introduces a real-time data fusion framework integrating hybrid adaptive Kalman filtering, distributed edge computing, and seamless IoT connectivity. The proposed framework incorporates three key innovations. First, a hybrid adaptive Kalman filtering mechanism employs the Unscented Kalman Filter (UKF) sigma-point technique, augmented with Long Short-Term Memory (LSTM) neural networks and fuzzy logic, for dynamic noise correction and robust nonlinear state estimation. Second, a three-tier distributed fusion architecture employs edge computing for local data processing, reducing network latency, communication overhead, and energy consumption. Third, a modular Service-Oriented Architecture enables seamless IoT integration, remote data access, and adaptive system reconfiguration. The framework also incorporates multi-criteria fault detection that combines chi-square tests, sequential probability ratio tests, and LSTM-based predictive compensation during sensor failures. Experimental validation employed 150 sensors for urban air-quality monitoring, industrial facility surveillance, and water-quality measurement. Sensor nodes utilized ESP32-S3 microcontrollers with LoRa communication, while Raspberry Pi 4 devices served as edge gateways connected to AWS IoT infrastructure. Compared to standard Kalman filtering, the proposed method achieved: (i) 25.2% reduction in root mean square estimation error, (ii) 41% energy reduction driven by 70% communication savings through predictive transmission and edge compression, (iii) sub-100 ms end-to-end latency representing 54% improvement, and (iv) robust performance maintaining below 10% degradation at 15% sensor failure rates. 2026 Taylor &amp; Francis Group, LLC. Subject The topic of the resource Adaptive Kalman filtering; distributed architectures; edge computing; environmental monitoring; fault-tolerant systems; IoT integration; multi-sensor data fusion Publisher An entity responsible for making the resource available Taylor and Francis Ltd. Relation A related resource ISSN: 32719; CODEN: ANALB 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