GFuCWO: A genetic fuzzy logic technique to optimize contention window of IEEE-802.15.6 WBAN

Wireless Body Area Networks (WBANs) are a cost-effective, low-power technology that has been advancing due to the need to improve their performance. The predominant challenges encompass packet delivery ratio (PDR), packet loss ratio (PLR), and end-to-end (E2D) delay. Advances in wireless technology emphasize the urgency to surmount these issues. Channel congestion and collisions, increased latency, unfairness in access, high energy consumption, performance heterogeneity, QoS degradation, interference and reliability are some of the particular concerns largely caused by CW that influence WBAN performance. Researchers and developers strive to create adaptive CW techniques and protocols that dynamically modify CWsize in response to traffic loads, network conditions, and the particular needs of WBAN applications in order to maximise WBAN performance. In response, we propose GFuCWO—a genetic fuzzy logic technique—for optimizing contention windows in IEEE-802.15.6 WBANs. This study introduces three distinct algorithms to accomplish this. We evaluate the efficiency of the GFuCWO technique against the ABEB and Improved-CSMA/CA algorithms. The approach is implemented in Castalia OMNeT++. The study calculates PDR, PLR, and E2D delay using experimental data from four sensor nodes under different traffic conditions. The GFuCWO technique demonstrated superior performance in PDR, PLR, and E2D, with average enhancements of 4%-11% and 3%-13%, respectively, with a 95% confidence interval, indicating its potential for community benefit and medical performance improvements.