Design and performance analysis of prime number based backoff counter adjustment (PBCA) algorithm for wireless body area networks

Wireless Body Area Networks (WBANs) are critical in medical emergency services within the Internet of Things (IoT). Ensuring efficient channel utilization while avoiding collisions and optimizing route allocation is a significant research focus in this field. However, the existing CSMA/CA methods need help updating the contention window and achieving fair node distribution. This paper proposes a novel Prime number-based Backoff Counter Adjustment (PBCA) strategy to address this issue. The PBCA algorithm dynamically adjusts the contention window based on the success counter and the prime backoff stages (BS). A mathematical analysis of the proposed PBCA algorithm uses Discrete-Time Markov Chains to study its impact on saturated throughput and collision probability. The proposed algorithm simulation experiments demonstrate the superiority of the PBCA algorithm over existing approaches. The proposed strategy significantly enhances throughput, reduces packet loss and delay, and ensures the reliability of the WBAN network. Compared to existing algorithms, the PBCA algorithm optimizes 48.1% in throughput, 39.47% in packet loss, 45.11% in delay, and 78.62% in reliability. These results outstand the performance of the existing algorithms. This advancement holds promise for enhancing performance and reliability in medical emergency services within IoT applications. The future extension of this work involves integrating reinforcement learning techniques to assess network status accurately, enabling adaptive and intelligent channel allocation in wireless body area networks.