Fractional Dynamics and Fractal Geometry for Robust Fault Detection in Wireless Sensor Networks
Year: 2025
Type: article
Abstract: A novel event detection framework for wireless sensor networks (WSNs) is presented, integrating fractional-order calculus and fractal geometry to address fault tolerance and detection accuracy challenges. The methodology comprises five synergistic components: mul-multifractal event region simulation, fractional-order dynamic trust evaluation, Monte Carlo-based event estimation guided by fractional information gain, fractional-order fault propagation modelling, and a multi-layer fractional consensus mechanism. These components enable the system to adaptively assess node reliability, predict fault propagation, and detect spatially complex events with high precision. The framework's simulation results demonstrate significant outperformance over conventional methods (binary decision trees, majority voting, and Shannon-entropy-based detection), achieving 96.2% detection accuracy, a 1.8% false positive rate, a 0.982 area under the curve, and a 28.4% reduction in data transmission overhead. These improvements highlight the practical potential of integrating fractional-al and fractal intelligence in the design of robust, memory-aware, and energy-efficient WSNs architectures suitable for harsh and dynamic-ic environments deployment.
DOI: 10.14419/aj2xxz30
Author: Abdullah Shawan Alotaibi
Institution: Shaqra University
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4416247958
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