ItemArticle

Real-Time Data Fusion Algorithm for Multi-Modal Environmental Sensor Networks Using Kalman Filtering and IoT Integration

Title
Real-Time Data Fusion Algorithm for Multi-Modal Environmental Sensor Networks Using Kalman Filtering and IoT Integration
Creator
Mary, S. A. Sahaaya Arul; Prasad, K.D.V.; Raj, S. Dinakar; Sathyapriya, S.; Lalitha, R.V.S.; Rao, G. Siva Nageswara
Description
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 & Francis Group, LLC.
Source
Analytical Letters;
Date
01-01-2026
Publisher
Taylor and Francis Ltd.
Subject
Adaptive Kalman filtering; distributed architectures; edge computing; environmental monitoring; fault-tolerant systems; IoT integration; multi-sensor data fusion
Coverage
Mary S.A.S.A., Department of AIML & 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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 32719; CODEN: ANALB
Format
online
Language
English
Type
Article

Citation

Mary, S. A. Sahaaya Arul; Prasad, K.D.V.; Raj, S. Dinakar; Sathyapriya, S.; Lalitha, R.V.S.; Rao, G. Siva Nageswara, “Real-Time Data Fusion Algorithm for Multi-Modal Environmental Sensor Networks Using Kalman Filtering and IoT Integration,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22625.

Output Formats