Dynamic Data Rate Prediction Based on Congestion Severity in Wireless Body Area Networks

Wireless Body Area Network (WBAN) is a vital application of IoT and EDGE technologies, which enable remote patient monitoring by transmitting real-time health data to a hospital server. WBAN faces congestion as a severe threat due to its delay sensitivity, which leads to the loss of essential data. To address this issue, this paper proposes a Dynamic Datarate Prediction Based on Congestion Severity (DPCS) in Wireless Body Area Networks which categorizes sensor nodes as normal or critical, based on the patient’s health status. The data flow from these sensors is efficiently regulated with the aid of the critical function with four parameters such as buffer size, queue size, remaining energy of the node, and channel bandwidth. With these dynamic run time parameters, the proposed critical function yields four thresholds to predict the congestion severity. The proposed technique predicts an optimal data rate based on the node priority and the severity of the congestion, as opposed to having a constant data rate for all types of sensors. The proposed algorithm is simulated and its performance is compared with the existing algorithms in terms of throughput, packet loss ratio, remaining energy, and packet delivery ratio.