Enhanced Fuzzy Logic-Based Optimization Framework for IoT Deployments in Wireless Sensor Networks

Abstract

The rapid proliferation of the Internet of Things (IoT) has led to widespread deployment of connected devices across various domains. However, real-world IoT applications face significant challenges, including network congestion, energy limitations, and communication reliability. Traditional deployment strategies often struggle to maintain optimal performance due to the dynamic nature of wireless sensor networks (WSNs). In this paper, we propose a Fuzzy Logic-based optimization framework to enhance IoT device deployment by dynamically adjusting communication parameters, optimizing power consumption, and improving network stability. Our approach introduces a Fuzzy Logic Controller (FLC) that intelligently regulates device sleep modes, data transmission intervals, and routing decisions based on environmental factors and network conditions. Through experimental validation in a smart home environment, we demonstrate that the proposed method achieves a 30% reduction in power consumption, a 25% improvement in network coverage, and enhanced device longevity compared to conventional deployment techniques. The findings suggest that incorporating fuzzy logic into IoT networks enables adaptive decision-making, resulting in more efficient, scalable, and resilient deployments. Future work will explore hybrid approaches integrating fuzzy logic with deep learning to further enhance predictive analytics and autonomous decision-making in IoT ecosystems.