Neutrosophic Fuzzy Power Management (NFPM): Tackling Uncertainty in Energy Harvesting for Sensor Networks

Abstract

This paper presents a novel approach known as Neutrosophic Fuzzy Power 
Management (NFPM) aimed at addressing the critical challenge of uncertain energy availability in 
Energy Harvesting Sensor Networks (EHWSNs). The main objective of this research is to enhance 
the management of energy resources within these networks, which traditional fuzzy logic methods 
often fail to do, leading to power failures and reduced reliability. NFPM utilizes neutrosophic logic 
to effectively model uncertainty by representing the degrees of truth, indeterminacy, and falsity of 
both harvested and residual energy levels. Through a fuzzy inference system, NFPM dynamically 
allocates energy budgets for each time slot based on these neutrosophic sets, resulting in more 
adaptive and conservative energy distribution. The results are validated through numerical 
examples and extensive simulations, demonstrating NFPM's superiority over traditional fuzzy 
logic, with significant improvements such as a 25% reduction in power failures, 95% enhanced 
network connectivity, a 15% increase in data transmission success rates, and overall improvements 
in energy efficiency and robustness to fluctuations and noise. This research establishes NFPM as a 
promising solution to the uncertainties inherent in EHWSNs. Future research directions include 
exploring the integration of NFPM with machine learning algorithms for predictive energy 
management, assessing its scalability in larger networks, and examining its applicability in other 
domains requiring energy management under uncertainty. 

DOI: 10.5281/zenodo.13988295