Search

This study focuses on enhancing security in wireless body area networks (WBANs) through the application of Support Vector Machine (SVM)-based anomaly detection. The main problem addressed is the insufficient attention to security measures in WBANs, particularly in terms of secure connections and mitigation strategies. The proposed solution involves utilizing SVM to categorize security measures for WBAN telehealth solutions based on relevant attributes, ensuring ongoing utilization. The primary results showcase the successful prediction of vital signs with a remarkable accuracy of 98.63% using SVM, highlighting its effectiveness in enhancing security in WBANs. This paper explores the application of Support Vector Machines (SVMs) to enhance WBAN security updates and intelligence. Specific access management approaches may prove more effective during crisis situations. This study categorizes security measures for WBAN telehealth solutions exclusively using SVM based on relevant security attributes, ensuring their ongoing utilization. Employing SVM, the study predicts a heart rate of 89.087 beats per minute, an RR interval of 673.5 ms, and a QT interval of 271.3 ms, achieving a remarkable accuracy of 98.63 percent with a training dataset comprising 80 percent of the data and a testing dataset encompassing the remaining 20 percent. [ABSTRACT FROM AUTHOR]

WBAN Magnetic Sensor Nodes can be classified based on artificial intelligence using the Multi-Layered Stacked Naïve Bayes Method for Resilient Infrastructure. As wireless body area networks (WBANs) hold considerable potential for monitoring, identifying, forecasting, and diagnosing disease in humans, this study is significant for the healthcare industry. WBAN data can be inaccurate and unreliable when collected by untrusted sensor nodes, leading to inaccurate diagnoses and treatments. WBAN networks can be improved by identifying untrusted sensor nodes in this study to address this issue. Sensor nodes are categorized using the MLSNB method based on their trust aspects. When compared to other methods currently in use, MLSNB performs better. It is possible, using the proposed methodology, to introduce high-quality, affordable, and easily accessible healthcare systems to the world's growing population, in particular to the elderly and persons living with old-age diseases. [ABSTRACT FROM AUTHOR]

As of late 2019, the COVID19 pandemic has been causing huge concern around the world. Such a pandemic posed serious threats to public safety, the well-being of healthcare workers, and the overall health of the population. If automation can be implemented in healthcare systems, patients could be better cared for and health industries could be less burdened. To combat such challenges, e-health requires apps and intelligent systems. Using WBAN sensors and networks, a doctor or medical professional can advise patients on the best course of action. Patients' fitness could be assessed using WBAN sensors without interfering with their daily activities. When designing a monitoring system, system performance reliability for competent healthcare is critical. Existing research has failed to create a large device capable of handling a large network or to improve WBAN topologies for fast transmitting and receiving patient data. As a result, in this research, we create a multisensor WBAN (MSWBAN) intelligent system for transmitting and receiving critical patient data. To gather information from all cluster nodes and send it to multisensor WBAN, a novel additive distance-threshold routing protocol (ADTRP) is proposed. In small networks where data are managed by the transmitting node and the best data route is determined, this protocol has less redundancy. An edge-cutting-based routing optimization (ES-EC-R ES-EC-RO) is used to find the best route. The Trouped blowfish MD5 (TB-MD5) algorithm is used to encrypt and decrypt data, and the encrypted data are stored in a cloud database for security. The performance metrics of our proposed model are compared to current techniques for the best results. End-to-end latency is 63 ms, packet delivery is 95%, security is 95.7%, and throughput is 9120 bps, according to the results. The purpose of this article is to encourage engineers and front-line workers to develop digital health systems for tracking and controlling virus outbreaks. [ABSTRACT FROM AUTHOR]

The wireless body area network (WBAN) has been proposed to offer a solution to the problem of population ageing, shortage in medical facilities and different chronic diseases. The development of this technology has been further fueled by the demand for real-time application for monitoring these cases in networks. The integrity of communication is constrained by the loss of packets during communication affecting the reliability of WBAN. Mitigating the loss of packets and ensuring the performance of the network is a challenging task that has sparked numerous studies over the years. The WBAN technology as a problem of reducing network lifetime; thus, in this paper, we utilize cooperative routing protocol (CRP) to improve package delivery via end-to-end latency and increase the length of the network lifetime. The end-to-end latency was used as a metric to determine the significance of CRP in WBAN routing protocols. The CRP increased the rate of transmission of packets to the sink and mitigate packet loss. The proposed solution has shown that the end-to-end delay in the WBAN is considerably reduced by applying the cooperative routing protocol. The CRP technique attained a delivery ratio of 0.8176 compared to 0.8118 when transmitting packets in WBAN. [ABSTRACT FROM AUTHOR]