Performance evaluation of the congestion severity aware rate regulation (CSRR) algorithm in wireless body area networks

Wireless body area network (WBAN) is a potential low-cost technology for privacy-sensitive telemedicine and e-health monitoring and services. However, it faces limited protocol and physical resource support challenges, which can result in packet transfer difficulties. In particular, WBAN requires an emergency-aware technology that ensures a promising quality of service (QoS). One significant issue affecting QoS and energy efficiency in WBAN is congestion. Effective congestion control techniques are essential for achieving proper load balancing. To address these challenges, we propose a congestion severity aware rate control (CSRR) algorithm that enhances packet transmission rate by reducing packet losses and retransmissions. The CSRR algorithm incorporates a fuzzy controller to predict congestion rates based on runtime metrics. To regulate congestion window growth in different algorithm phases, we introduce sequences such as the Fibonacci retracement sequence, knight's move sequence, and the binary logarithm of the $N^{\mathrm{th}}$ primorial sequence to regulate congestion window growth in the different phases of the proposed algorithm. We mathematically analyze the proposed CSRR algorithm using a Markov model. The simulation results demonstrate the superiority of our algorithm compared to existing approaches. Specifically, our algorithm achieves significant optimizations in terms of throughput (52.92%), packet loss (38.11%), delay (37.23%), and remaining energy (36.86%) when compared to existing algorithms.