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Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

Real time data fusion and mathematical modeling techniques for intelligent IoT systems

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pp. 1357–1368Vol. 46Issue 4-BMay 2025DOI: 10.47974/JIOS-1995XML
Received:
08 Oct 2024
Published Online:
31 May 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1995
Pages:
1357–1368

Abstract

In the context of IoT, both system integration and real-time processing, diverse sensor data are essential for creating smart and adaptive systems. This paper introduces a complete set of algorithms for the efficient real-time combination of information and predictive analytics for intelligent IoT networks. Adaptive Kalman Filters is used in the proposed methodology to manage data integration of multiple sensors needed for state estimation which could experience high noise levels or system dynamics. To improve the very predictive performance then, Long Short-Term Memory (LSTM) networks are utilized thus ensuring that the model is capable of capturing temporal dependencies and yielding more accurate values of future states. The experimental assessments employing Synthetic datasets are as follows, the Kalman Filter has an MSE 0.015 and LSTM model has an MSE of 0.012. Moreover, latency analyses for edge computing scenarios show that the processing time takes 30% less time than in the case of cloud-only networks. Of this, the long existing feature of the system to detect inconsistencies in the data gathered by the sensors proves effective in increasing the efficacy and safety of IoT operations. The proposed techniques present considerable improvements that real-time data fusion and predictive capabilities for IoT systems that can underpin multiple applications especially across different industries.

Keywords

Subject Classifications

94A6020C0520C07

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