A fuzzy logic and cross-layered optimization for effective congestion control in wireless sensor networks to improve efficiency and performance

Abstract

Wireless Sensor Networks (WSNs) are a fundamental component of the Internet of Things (IoT), used in diverse applications to detect environmental conditions and send information to the Internet. WSNs are susceptible to congestion issues, leading to increased packet loss, extended delays, and reduced throughput. This research introduces a Fuzzy Logic-based Cross-Layered Optimization Model (FL-CLOM) for WSNs to tackle the problem. FL-CLOM is developed by including the signal-to-noise ratio of the wireless channels in the Transmission Control Protocol (TCP) approach, bridging the transmission layer and Media Access Control (MAC) layer. A fuzzy logic system is created by integrating fuzzy control with congestion control to dynamically manage the queue size in crowded nodes and minimize the effects of external uncertainties. Various simulations were conducted using MATLAB and NS-2.34 to compare the suggested FL-CLOM to conventional methods. The results indicate that FL-CLOM efficiently adjusts to queue size changes and demonstrates rapid convergence, reduced average delay, reduced packet loss, and increased throughput.