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Abstract <p>Wireless Body Area Networks (WBANs) are precisely defined as wireless networks comprising various sensors strategically positioned on the human body, these sensors have been either worn externally on the body or surgically implanted beneath the skin. Sensitive information is susceptible in many ways when it is transmitted across unsecure networks, therefore robust security measures are necessary to guard against possible attackers. Thus, the proposed model developed a secure and efficient patient monitoring using ElGamal-LCA for encryption with routing algorithm and MSDGCN based intrusion detection system. The process begins with a WBAN employing 12 sensors such as ECG, EMG, PPG, EEG, temperature, blood pressure, SPO2, respiration rate, accelerometer, glucose, gyroscope and galvanic skin response for capturing vital physiological signals from the human body. Then these readings are sent to a control unit which further aggregates the sensor data. For securing the data transmission Elgamal-Lightweight Cryptography Algorithm (Elgamal-LCA) is employed. Elgamal cryptosystem handles key generation while lightweight encryption encrypts the data. The data transmission causes interchannel interference due to overlapping signal from same or adjacent channels which are mitigated by utilizing a Stochastic Learning Algorithm (SLA) to prevent data loss and collisions. Once if interference is mitigated, data is transmitted to base station using Quality of service (QoS) based Minimal Latency Routing Strategy. At the base station intrusion detection was performed and the process involves preprocessing using Hyperbolic Tangent (HT) normalization and Slim Generative Adversarial Imputation Network (SGAIN) for imputing missing data followed by classification utilizing Modified Spatial Dynamic Graph Convolutional Network (MSDGCN) with Dynamic Composable Multi-Head Attention (DCMHA) for effective detection. Finally, alerts are sent if an intrusion is found otherwise data is stored securely in the cloud. The proposed approach achieves an execution time of 77.51 sec, packet loss of 4.7%, accuracy of 99.17% and F1_Score of 98.68%. The proposed approach provides an effective transmission in WBAN-IoMT ensure secure data forwarding and enabling low latency communication with intrusion prevention for enhanced patient care.</p>

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Real-Time and Secure Patient Monitoring in WBAN-IoMT Using Intelligent Routing and Threat Detection for Low-Latency Communication and Intrusion Prevention

  • 期刊论文

  • 发表日期：

    2026年3月26日

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  • U. Hariharan,
  • Chin-Shiuh Shieh,
  • Mong-Fong Horng

摘要

Abstract

Wireless Body Area Networks (WBANs) are precisely defined as wireless networks comprising various sensors strategically positioned on the human body, these sensors have been either worn externally on the body or surgically implanted beneath the skin. Sensitive information is susceptible in many ways when it is transmitted across unsecure networks, therefore robust security measures are necessary to guard against possible attackers. Thus, the proposed model developed a secure and efficient patient monitoring using ElGamal-LCA for encryption with routing algorithm and MSDGCN based intrusion detection system. The process begins with a WBAN employing 12 sensors such as ECG, EMG, PPG, EEG, temperature, blood pressure, SPO2, respiration rate, accelerometer, glucose, gyroscope and galvanic skin response for capturing vital physiological signals from the human body. Then these readings are sent to a control unit which further aggregates the sensor data. For securing the data transmission Elgamal-Lightweight Cryptography Algorithm (Elgamal-LCA) is employed. Elgamal cryptosystem handles key generation while lightweight encryption encrypts the data. The data transmission causes interchannel interference due to overlapping signal from same or adjacent channels which are mitigated by utilizing a Stochastic Learning Algorithm (SLA) to prevent data loss and collisions. Once if interference is mitigated, data is transmitted to base station using Quality of service (QoS) based Minimal Latency Routing Strategy. At the base station intrusion detection was performed and the process involves preprocessing using Hyperbolic Tangent (HT) normalization and Slim Generative Adversarial Imputation Network (SGAIN) for imputing missing data followed by classification utilizing Modified Spatial Dynamic Graph Convolutional Network (MSDGCN) with Dynamic Composable Multi-Head Attention (DCMHA) for effective detection. Finally, alerts are sent if an intrusion is found otherwise data is stored securely in the cloud. The proposed approach achieves an execution time of 77.51 sec, packet loss of 4.7%, accuracy of 99.17% and F1_Score of 98.68%. The proposed approach provides an effective transmission in WBAN-IoMT ensure secure data forwarding and enabling low latency communication with intrusion prevention for enhanced patient care.