DNBP-CCA: A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use of Body Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, including contention during finite backoff periods, association delays, and traffic channel access through clear channel assessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions, and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet delivery ratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear Channel Assessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination of the Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA) scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system to quantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. On the other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission to the coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result, simulations demonstrate that our proposed schemes are better than some existing representative approaches and enhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, and decrease average packet drop rate and packet delay.