The proposed BiLSTM algorithm significantly improves the reliability and throughput of WBANs by effectively detecting data manipulation by intruders.
A proposed machine learning approach using BiLSTM and rule-based screening can detect data manipulation and improve the reliability of wireless body area networks.
ABSTRACT The recent technologies in IoT‐based smart healthcare services are gaining attention because of their quality of service and reliability. These remote health monitoring systems offer enhanced quality of comfort to individuals and also meet emergency situation management requirements. Automatic data recording, processing, and communication with a third party like a doctor, caretaker, or hospital wirelessly enables the system to supersede conventional healthcare techniques. This paper focuses on the major challenging issues faced in the implementation of wireless body area networks (WBANs) in a dynamic environment, and the performance evaluation of the currently used systems. As the data collected by the sensor nodes are highly critical, a machine learning algorithm, integrating a rule‐based screening and a bidirectional LSTM, is proposed to detect data manipulation by intruders. The performance of the network is then evaluated with the help of various parameters. The study results provide better insight into system performance and optimization of system parameters to achieve better reliability and throughput in the presence of false data.
Shibu et al. (2025) studied this question. The proposed BiLSTM algorithm significantly improves the reliability and throughput of WBANs by effectively detecting data manipulation by intruders.