Abstract
The advancement of networking, information, and communication technologies has fueled the popularity of Wireless Body Area Networks (WBANs) in both medical (remote patient monitoring) and non-medical sectors. Due to low, medium, and high data traffic requirements, WBAN performance suffers during the synchronization process that generates periodic beacon frames between sensor nodes and the coordinator. It also suffers when sensor nodes implicitly send data to the coordinator during the fixed time slot using the Contention Access Period (CAP). In this study, we propose a solution called Dynamic Next Beacon Interval and Superframe Duration Scheme (DNBISD) to tackle these issues of the IEEE 802.15.4 standard. This standard relies on a Beacon Interval (BI) and CAP for synchronization and data transmission between sensor nodes and the coordinator. However, the standard must adapt to BI and CAP's changing traffic load requirements, resulting in drawbacks such as prolonged packet delays, increased energy consumption, and potential data loss, particularly in real-time patient monitoring applications. In order to overcome these challenges, our DNBISD scheme employs a fuzzy approach to adapt the BI and CAP based on requested synchronization and data considering input parameters like packet received ratio and buffer ratio. The inference system utilizes the Takagi, Sugeno, and Kang (TSK) fuzzy model for rational quantitative analysis. Simulations demonstrate that our proposed scheme significantly enhances data transmission, boosts the average packet delivery ratio and throughput, and reduces the coordinator's average packet loss ratio and energy consumption. Consequently, this improvement allows for more efficient data transfer among numerous nodes within the specified superframe structure.
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