Chronic patients are adapting to an emerging healthcare system assisted by the wireless body area network (WBAN). Medical data are not normal at all times; hence, the preference for critical data is expected to be high, and the requirement of security has become essential. This paper addresses a novel healthcare monitoring system (HMS) that adopts the IEEE 802.15.6 standard for WBAN. A modified IEEE 802.15.6 MAC is designed for predicting data types and residual energy on body sensors. An enhanced MAC-based HMS (E-MHMS) is developed for delay aware data transmission to perform data aggregation, key distribution, channel selection and data classification. A smartphone acts as a coordinator that aggregates data from the WBAN; upon receiving the data, it determines the best channel from the available multiple inputs for assured data transmission. E-MHMS uses the novel time-based elliptic curve algorithm and the ASCII RSA algorithm for key distribution and encryption. Finally, the data reaches the monitoring servers that classify the data using hybrid naïve Bayesian neural network. The proposed E-MHMS setup in an OMNeT++ simulation environment and the improvements are demonstrated in terms of important network parameters such as delay, throughput, packet drop, security and accuracy.

慢性患者正在适应由无线体域网（WBAN）协助的新兴医疗系统。医疗数据并非始终都是正常的；因此，人们期望对关键数据的偏爱很高，并且对安全性的要求变得至关重要。本文介绍了一种采用针对WBAN的IEEE 802.15.6标准的新型医疗监控系统（HMS）。修改后的IEEE 802.15.6 MAC用于预测人体传感器上的数据类型和剩余能量。开发了基于MAC的增强型HMS（E-MHMS），用于感知延迟的数据传输，以执行数据聚合，密钥分配，通道选择和数据分类。智能手机充当协调器，可汇总WBAN中的数据；接收到数据后，它将根据可用的多个输入确定最佳通道，以确保数据传输。E-MHMS使用新颖的基于时间的椭圆曲线算法和ASCII RSA算法进行密钥分发和加密。最后，数据到达使用混合朴素贝叶斯神经网络对数据进行分类的监视服务器。提议的E-MHMS在OMNeT ++仿真环境中的设置和改进措施在重要的网络参数方面得到了证明，例如延迟，吞吐量，丢包，安全性和准确性。